Technocracy, NWO, and 5G Technology Notes
<INSERT 11/10/2018>
Update 11/9/2018 www.appleofmyeyes.org/2018/04/checks-and-balances.html
Published on Nov 9, 2018
I love Jake's reporting, he is so scholarly, but my having about 15 years on him, I tend to be much more tired of the corruption and violence coming out of Washington!
Look at all the mass shootings! They make money on mass shootings - snatching and grabbing and using it for nefarious purposes such as building more Transgender clinics! WAKE UP!
Sandy Hook FAKE shooting was Dec 12, 2012 (12/12/12) - Subscribe to GEMATRIA on YouTube! GEMATRIA SHOULD BE HIRED AS A CONSULTANT BY FBI! HE SUSPECTED THERE WOULD BE SOMETHING TERRIBLE THAT WOULD HAPPEN TODAY SINCE IT IS 11/9/2018 - A Satanic symbolic number.
In 2013 - MANY TRANSGENDER CLINICS WERE BUILT! And I mean MANY! See my blogpost on Tranny Watch, the notes about the Transgendering of children, a current video I watched.
My comment: USA Foreign policy sucks right now. So if China and Russia and Iran and India want to pick up the slack to promote WORLD PEACE - I am all for it. US is known for being bullies and when our great honorable statesmen like Colin Powell, Rex Tillerson, Jeff Sessions - and others - promote DIPLOMACY - they are SQUISHED. Something to consider. If they posture themselves as stronger than USA - Trump will have no standing, the military would only want to get involved if they want to commit suicide, so Trump will hit a WALL that THEY built and I say GOOD!!!
We the GOOD people of United States want Peace and appose those leaders who incite war! <END OF INSERT>
3/30/2018: Quinn Michaels explains the background of Satanic Cults...beginning with "Who is Lucifer" https://www.youtube.com/watch?v=03VzdmERVZc -
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Title for Blog: Technocracy, NWO, and 5G Technology Notes
23July2017
Technocracy is what the NWOs are developing
Watch/listen to SAGE OF QUAY on YouTube! www.Sageofquay.blogspot.com
& www.sageofquay.com
5 G AND ITS DANGERS. SMART GRID - MILITAR TECHNOLOGY. MY NOTES OF THE BROADCAST/AUDIO ON YOUTUBE
www.thecrowhouse.com
- guest speaker
Greed, fear and quest for knowledge, replacing humanity with biologically controlled humans through automation via 5G technology
Milimeter waves - are dangerous to society = radiation
Future Technology - nanotechnology. Projected achieve time 2025-2030
Our bodies are fully autotonomic (I presume this reflects Intelligence Design), self-sustaining
Nanocarbons can print and tie human cellular activity
GIVING LIFE TO LUCIFER CONCEPT
Lucifer is the bearer of Ligt. Light represents knowledge. Cypher=code
Are they trying to uncode life itself? They just cannot!
Human attention span is now less than a gold fish! We are constantly squabbeling!
Gold fish - 9 second attention span
Human - 8 seconds
If you haven't changed what is in your heart, you will not use this technology safely!
Christ got angry in the temple throwing the tables. Reflection of monetary system is implied.
We need the spiritual understanding to cause effective change because we are losing our life skills!
Avoid revolution and violence - yet be ready to fight if necessary!
AI vs EI concept
Consciousness of internet
Thirst for knowledge
Fear concept
Put down the credit cards and mobile phones!
Archinet - anthenet. Created in 1954
(note, the CIA was established in 1952. Iran was overthrown and the CIA helpd Reza Shah to get in power. Queen Elizabeth's father had an untimely heart attack and died while she and Prince Phillip were in Africa, I think on their honeymoon. Korean War was started - all in this time frame)
SERN - was the development of internet after this
Use of cursing/spells - different languages - turn into spells we are casting - because of internet
internet = NET
World Wide Web = WEB
Catch concept - they want to catch the user, consider this
Rediscover Humanity!!
Pick up a pencil! Learn your Three R's (reading, writing, arithmatic)
Your thoughts create your reality
Manipulation of the consciousness
Quantum computing and D wave Tapping ino other dimensions
PizzaGate - uses BLOCK CHAIN - cypher activity. To access pedophile material
Internet is being used t control and map us!
We ampa-morphize everything - someone will come and say he's Christ, yet he is really created by internet!
KNOW THE ENEMY - ARE THEY AN ENEMY? - WHICH ONES ARE SATANIC?
These are a group of men around a table who want to make more money. This greed creates their reality.
Are being experimented on now?
Gathering data now to recreate/create a new HUMANITY?
We still have a chance to be human by putting down the technology - stay hoe and love family and friends!
Mandella Effect discussed. ============================== I searched on Google======= scholarly articles on 5G Technology======================
BELOW IS A SEARCH ON LINKS OF SCHOLARLY ARTICLES ON G5 GRID
http://ieeexplore.ieee.org/abstract/document/6815890/?reload=true
Scholarly articles
http://ieeexplore.ieee.org/abstract/document/6736744/
https://link.springer.com/article/10.1007/s11036-015-0590-7
https://link.springer.com/article/10.1007/s11432-015-5397-4
http://ieeexplore.ieee.org/abstract/document/7389830/
https://link.springer.com/article/10.1007/s11235-014-9885-4
An energy-aware scheme for efficient spectrum utilization in a 5G mobile cognitive radio network architecture
https://link.springer.com/article/10.1007/s11235-014-9890-7
Relay selection for secure 5G green communications
https://www.hindawi.com/journals/misy/2016/9767065/abs/
Research Article
SDN Controlled mmWave Massive MIMO Hybrid Precoding for 5G Heterogeneous Mobile Systems
Mobile Information Systems
Volume 2016 (2016), Article ID 9767065, 10 pages
http://dx.doi.org/10.1155/2016/9767065
https://link.springer.com/article/10.1007/s11036-014-0563-2
Adaptive SON and Cognitive Smart LPN for 5G Heterogeneous Networks
Mobile Networks and Applications
December 2015, Volume 20, Issue 6, pp 745–755
https://www.hindawi.com/journals/misy/2017/3680671/abs/
Mobile Information Systems
Volume 2017 (2017), Article ID 3680671, 11 pages
https://doi.org/10.1155/2017/3680671
Research Article
Efficient and Privacy-Aware Power Injection over AMI and Smart Grid Slice in Future 5G Networks
Yinghui Zhang,1,2,3 Jiangfan Zhao,1 and Dong Zheng1,3
1National Engineering Laboratory for Wireless Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2State Key Laboratory of Cryptology, Beijing 100878, China
3Westone Cryptologic Research Center, Beijing 100070, China
http://onlinelibrary.wiley.com/doi/10.1111/j.1530-9290.2010.00278.x/full
Journal of Industrial Ecology
Explore this journal >
Volume 14, Issue 5
October 2010
Pages 770–790
Journal of Industrial Ecology
Previous article in issue: The Energy and Climate Change Implications of Different Music Delivery Methods
Next article in issue: A High-Resolution Statistical Model of Residential Energy End Use Characteristics for the United States
View issue TOC
Special Issue:
Environmental Applications of Information & Communication Technology
Greenhouse Gas Emissions and Operational Electricity Use in the ICT and Entertainment & Media Sectors
https://scholar.google.com/citations?user=pts2z0wAAAAJ&hl=en&oi=sra
This link has a huge list of scholarly articles on the 5G grid, internet science
Maziar Nekovee
Professor of Telecommunications & Mobile Technology, Sussex University
6G, auction theory, bio-communications, brain-mobile interface
Verified email at www.sussex.ac.uk
- Homepage
https://books.google.com/books?hl=en&lr=&id=HzXZCgAAQBAJ&oi=fnd&pg=PP1&dq=scholarly+articles+for+5g+smart+grid&ots=b9-4ekEL6R&sig=CdBBuTqMmdMm1gCGQThRknU93xQ#v=onepage&q&f=false
http://ieeexplore.ieee.org/abstract/document/6807956/
Browse Journals & Magazines > IEEE Communications Magazine > Volume: 52 Issue: 4
Smart grid technologies for future radio and data center networks
http://ieeexplore.ieee.org/abstract/document/6815890/
Scenarios for 5G mobile and wireless communications: the vision of the METIS project
http://journals.sagepub.com/doi/full/10.1155/2015/872821
Cognitive Radio Enabled Wireless Sensor Networks and Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published January 1, 2015 Editorial
https://www.hindawi.com/journals/misy/2016/2676589/abs/
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
http://dx.doi.org/10.1155/2016/2676589
Review Article
Survey of Promising Technologies for 5G Networks
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
Guangyi Liu and Dajie Jiang
China Mobile Research Institute, Beijing 100053, China
http://journals.sagepub.com/doi/full/10.1155/2015/872821
Cognitive Radio Enabled Wireless Sensor Networks and Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published January 1, 2015 Editorial
https://www.hindawi.com/journals/misy/2016/2676589/abs/
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
http://dx.doi.org/10.1155/2016/2676589
Review Article
Survey of Promising Technologies for 5G Networks
Nam Tuan Le,1 Mohammad Arif Hossain,1 Amirul Islam,1 Do-yun Kim,2 Young-June Choi,2 and Yeong Min Jang1
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
Guangyi Liu and Dajie Jiang
China Mobile Research Institute, Beijing 100053, China
Received 8 October 2015; Accepted 2 March 2016
Academic Editor: Juho Lee
http://ieeexplore.ieee.org/abstract/document/7875185/
Browse Journals & Magazines > IEEE Access > Volume: 5
IEEE Access Special Section Editorial: Optimization for Emerging Wireless Networks: IoT, 5G, and Smart Grid Communication Networks
The Digital Revolution Internet Of Things 5 G and Beyond - this is a downloadable PDF
AM French, JP Shim CAIS 2016
http://ieeexplore.ieee.org/abstract/document/7909091/
Browse Journals & Magazines > IEEE Wireless Communications > Volume: 24 Issue: 2
Smart Grids
https://www.hindawi.com/journals/ijap/2016/7202143/abs/
International Journal of Antennas and Propagation
Volume 2016 (2016), Article ID 7202143, 10 pages
http://dx.doi.org/10.1155/2016/7202143
Research Article
Millimeter-Wave Microstrip Antenna Array Design and an Adaptive Algorithm for Future 5G Wireless Communication Systems
Cheng-Nan Hu,1 Dau-Chyrh Chang,1 Chung-Hang Yu,2 Tsai-Wen Hsaio,2 and Der-Phone Lin2
1Communication Engineering Department, OIT, New Taipei City 22061, Taiwan
2National Chung-Shan Institute of Science and Technology, P.O. Box 90008, Longtan, Taoyuan City, Taiwan
http://ieeexplore.ieee.org/abstract/document/7430394/
Browse Journals & Magazines > IEEE Journal on Selected Area... > Volume: 34 Issue: 3
Guest Editorial Emerging Technologies
Green Communications and Computer Networks - PDF document
By J Wu, J Thompson, H Zhang
http://ieeexplore.ieee.org/abstract/document/7568464/
Browse Conferences > Computer Communication and Ne...
[Panel Sessions - 2 abstracts. - Note, Google describes this document as Panel One.Apparently they deleted Panel One and I wonder why]
Abstract:
Contains abstracts for Panel I: Emerging Research Challenges in the Era of IOT and Panel II: Research Challenges in Big Data and Cloud Computing. The complete presentations were not made available for publication as part of the conference proceedings.
Published in: Computer Communication and Networks (ICCCN), 2016 25th International Conference on
Date of Conference: 1-4 Aug. 2016
http://www.rgnpublications.com/journals/index.php/jims/article/view/458
A Novel Cognitive Radio enabled IoT System for Smart Irrigation
Ammar Ahmed Khan, Aamir Zeb Shaikh, Shabbar Naqvi, Talat Altaf
Abstract
A novel architecture is proposed and analyzed that incorporates cognitive radio concept into Internet of Things (IoT) for smart irrigation system. The proposed system will optimize the use of natural resource i.e. water. Typically, the flow of water for irrigation of crop fields is not uniform due to many reasons including non-uniform terrain, availability of resources at different sites and etc.This un-even flow produces lesser product from the farms. The proposed system uses two data types to model the different conditions of crop filed. Based on these assumptions, the proposed system is analyzed. The simulation results for the proposed scenario are also presented.
http://www.mdpi.com/1996-1073/10/7/909/htm
This is a PDF, yet I am able to access the link
Energies 2017, 10(7), 909; doi:10.3390/en10070909
Article
Optimal Power Allocation for a Relaying-Based Cognitive Radio Network in a Smart Grid
Kai Ma 1,*, Xuemei Liu 1, Jie Yang 1,2, Zhixin Liu 1 and Yazhou Yuan 1
1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
* Correspondence: Tel.: +86-139-3367-9689
Academic Editors: Frede Blaabjerg and Hongjian Sun
Received: 28 April 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
Abstract: This paper obtains optimal power allocation to the data aggregator units (DAUs) and relays for cognitive wireless networks in a smart grid (SG). Firstly, the mutual interference between the primary user and the DAU are considered, and the expressions of the DAU transmission signal are derived based on the sensing information. Secondly, we use the particle swarm optimization (PSO) algorithm to search for the optimal power allocation to minimize the costs to the utility company. Finally, the impact of the sensing information on the network performance is studied. Then two special cases (namely, that only one relay is selected, and that the channel is not occupied by the primary user) are discussed. Simulation results demonstrate that the optimal power allocation and the sensing information of the relays can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
Keywords: cognitive wireless network; smart grid; DAU; relay; power allocation; PSO
I am pasting this whole document because it is recent and allows me to:
Typesetting math: 20%
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Abstract
Introduction
Cognitive Wireless Network Model in a Smart Grid
Problem Formulation and Solutions
Simulation Results
Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Nomenclature
References
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Open Access
Energies 2017, 10(7), 909; doi:10.3390/en10070909
Article
Optimal Power Allocation for a Relaying-Based Cognitive Radio Network in a Smart Grid
Kai Ma 1,*, Xuemei Liu 1, Jie Yang 1,2, Zhixin Liu 1 and Yazhou Yuan 1
1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
* Correspondence: Tel.: +86-139-3367-9689
Academic Editors: Frede Blaabjerg and Hongjian Sun
Received: 28 April 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
Abstract: This paper obtains optimal power allocation to the data aggregator units (DAUs) and relays for cognitive wireless networks in a smart grid (SG). Firstly, the mutual interference between the primary user and the DAU are considered, and the expressions of the DAU transmission signal are derived based on the sensing information. Secondly, we use the particle swarm optimization (PSO) algorithm to search for the optimal power allocation to minimize the costs to the utility company. Finally, the impact of the sensing information on the network performance is studied. Then two special cases (namely, that only one relay is selected, and that the channel is not occupied by the primary user) are discussed. Simulation results demonstrate that the optimal power allocation and the sensing information of the relays can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
Keywords: cognitive wireless network; smart grid; DAU; relay; power allocation; PSO
1. Introduction
Smart grid (SG) is the modernization of generation, transmission, and distribution of a power grid system with the integration of advanced information and communication technologies (ICTs). The decentralized nature enables the integration of renewable energy resources and promises a two-way communications between consumers and utility company, which will improve the efficiency of utility company programs such as demand response, customer participation, and advanced smart metering [1]. In a smart grid, regulation is a type of ancillary service which continuously balance supply with demand in electricity markets under normal conditions [2]. Generally, the regulation service can be provided by on-line generation units that are equipped with the automatic generation control (AGC). In ancillary service markets, the utility company purchases the AGC service according to the errors between the generation and the load. Thus, the electricity costs to the utility company are increased with the errors between supply and demand. It was demonstrated that the errors can be reduced by the demand-side regulation [3,4], which is dependent on the two-way communications between the utility company and the consumers.
Cognitive radio (CR) [5] is widely recognised as a dynamic spectrum access technique, which enables unlicensed users to share the spectrum with licensed users [6,7]. The author [8] investigated how CR can be utilized to serve a smart grid deployment, from a home area network to power generation. It is recognized as a promising technology to address the communication and networking problems in the smart grid [9]. Under the background of the smart grid [10], the two-way communications [11] can be implemented by the advanced metering infrastructure (AMI), which includes cognitive home area networks, cognitive neighborhood area networks, and cognitive wide area networks. Moreover, several advanced communication technologies have been applied to demand response in a smart grid [12,13,14,15,16]. In Reference [12], the authors propose two different architectures for CR communications systems based on the IEEE 802.22 standard to accommodate the current and future needs of SG communications. Cognitive radio-enabled smart grid was presented for demand response to reduce the communication outage [13]. The book [17] provided readers with the most extensive coverage of technologies for 5G wireless systems to date. A cooperative spectrum sharing strategy based on the Nash bargaining solution for cooperative cognitive systems and a power allocation technique with improved energy efficiency for MIMO-OFDM based CR with tolerable degradation at system capacity was proposed in References [18] and [19], respectively. The authors considered the problem of resource allocation in a two-way relay network [20]. In Reference [21], the authors studied the resource allocation algorithm for CR secondary networks with simultaneous wireless power transfer and secure communication based on a multiobjective optimization framework. The differences of the proposed work with the above literature are shown in Table 1.
Table
Table 1. Differences of the proposed work with the literature.
Recently, cooperative relaying has been proposed for communications in a smart grid. The basic idea of cooperative relay is to use relays to help mobile users to transmit to the destination, in order to combat the impact of fading [14] and improve the spectral efficiency [15] for smart grid communications. In Reference [16], D. Niyato et al. proposed a cooperative relay-based meter data collection networks in a smart grid, in order to reduce the electricity costs. The authors developed a scheme that optimized the user assignment and power allocation optimization in CR networks [22]. The secondary user power allocation problem in cognitive radio networks with uncertain knowledge of interference information was studied in Reference [23]. The authors in Reference [24] investigated the energy efficient power allocation for orthogonal frequency division multiplexing based cognitive radio networks (CRNs) in the underlay mode.
Particle swarm optimization (PSO) is a population based stochastic optimization algorithm which was originally introduced by Kennedy and Eberhart [25,26]. PSO has been extended to many application areas such as function optimization [27], artificial neural network training [28,29,30], fuzzy system control [31,32,33,34], power system [35,36] and image processing [37]. This algorithm is motivated by the emergent motion of the foraging behavior of a flock of birds. PSO consists of a swarm of particles. Each particle represents a potential solution, which is a point in the multi-dimensional search space. The global optimum of PSO is regarded as the location of food. Each particle has a fitness value and a velocity to adjust its flying direction according to the experiences of the particle itself and its neighbors. PSO is simple in implementation and has good convergence properties when compared to evolutionary algorithms [38]. The advantages of PSO have caused it to become one of the most popular optimization techniques.
To the best of our knowledge, the combination of the relaying and CR in a smart grid has not been considered in the literature. In this paper, we propose to use both relaying and CR in smart grid communications, in order to reduce the packets loss and improve the spectrum utilization simultaneously. We consider the cognitive wireless network in a smart grid and focus on how to reduce the packets loss in the downlink transmission and improve the quality of communication, and then minimize the costs to the utility company. The main contributions of this paper are as follows:
This paper converts the sensing errors into the channel available confidence and introduce the average interference constraint to the cognitive wireless networks in a smart grid.
We establish a cost model based on the statistical analysis with the regulation errors of a direct load control method for cognitive wireless networks in a smart grid. Specifically, the power allocation problem based on the sensing error information was formulated as a nonlinear optimization problem. Then we use the PSO algorithm to search for the optimum.
We demonstrate that the sensing information in power allocation can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
The rest of the paper is organized as follows. In Section 2, we describe the cognitive wireless network model and the cost model to the utility company in a smart grid. The power allocation problem is formulated as a multi-variable optimization problem and PSO algorithm is employed to seek the optimal solution in Section 3. Simulation results are shown in Section 4. Finally, we draw conclusions in Section 5.
2. Cognitive Wireless Network Model in a Smart Grid
Consider a downlink cellular cognitive wireless network, which includes the primary network and cognitive radio network, as shown in Figure 1. The cognitive radio network is implemented by two-way communications between the utility company and the consumers. The DAU that is deployed by the utility company collects the temperature settings from the consumers and forwards them to the utility company in the uplink transmission. Meanwhile, the DAU receives the control commands from the utility company and forwards them through the relays to the consumers in the downlink transmission. In the primary network, the primary transmitter (PT) transmits to the primary receiver (PR). Assume that the PT transmits to the PR with a fixed power, and the DAU uses the vacant channel to transmit information according to the sensing information.
Energies 10 00909 g001 550
Figure 1. The cognitive wireless network in a smart grid.
2.1. Cognitive Wireless Network
The DAU accesses to the channels of the primary user by spectrum sensing. The available channel of the primary user is divided into k carriers, and the existing probability of the primary user in each carrier is p q . We use the binary variables to represent the activity of the primary user on the carrier k . We denote C k = 1 when the primary user is active on the carrier k and C k = 0 when the primary user is inactive. C ^ k is the sensing results of the DAU on carrier k. We denote C ^ k = 0 when the carrier is occupied by the primary user and C ^ k = 1 otherwise. In practice, the sensing results of the DAU are not accurate, which causes false alarm and mis-detection. The false alarm denotes the carrier that is actually vacant when the DAU believes that the primary user occupies the carrier due to sensing errors. The mis-detection denotes the carrier that is actually occupied by the primary user but refers to the case when DAU believes that the carrier is vacant. We denote the false alarm probability is p f and the mis-detection probability is 1 - p d . When the mis-detection happens, the cognitive network communication can produce interference to the PR, and the instantaneous interference can be expressed as
I sp = P sr | H sr , p | ( 1 - C k ) , (1) where P sr is the transmission power of the DAU transmitter or relays and H sr , p is the channel gain from the DAU transmitter or relays to the PR. We need to ensure that the average interference of the primary user is lower than the interference temperature threshold when the DAU occupies the communication channel of the primary user [39,40,41], i.e.,
I ¯ sp = E C k | C ^ k [ P sr | H sr , p | ( 1 - C k ) ] = P sr δ sr , p 2 ( 1 - E [ C k | C ^ k ] ) ≤ I 0 , (2) where I 0 denotes the interference temperature threshold of the primary user. The instantaneous interference from the primary user to the gateway or relay is described as follows:
I pd = P p | H p , dm | ( 1 - C k ) , (3) where P p is the transmission power of the primary user, H p , dm is the channel gain from the PT to the DAU receiver or relay. The corresponding average interference can be expressed as
I ¯ pd = E C k | C ^ k [ P p | H p , dm | ( 1 - C k ) ] = P sr δ p , dm 2 ( 1 - E [ C k | C ^ k , dm ] ) (4)
2.2. Packets Loss Model
We consider a communication model as shown in Figure 1, where the transmission strategy is the cooperative relaying. Without loss of generality, we only consider the packets loss in the downlink transmission and formulate the packets loss rate as
P r = ( T - R ) g ′ T , (5) where T denotes the arriving rates of the DAU, R is the receiving rate of the gateway, and g ′ is the correct transmission ratio from the gateways to the consumers.
2.3. Transmission Formulation of The Network
We assume that the PT can adjust the transmission power according to its own throughput requirements. Moreover, the utility company is restricted to the average interference temperature of primary user and improve the transmission quality as far as possible, in order to reduce the packets loss and the costs. Under the condition of the mutual interference, the DAU and the relays constitute a virtual antenna array through collaboration, and the relays terminal and DAU receiver will introduce two beamforming weights. In addition, the weight of the relays terminal can eliminate or reduce the interference from other networks, and the DAU is able to obtain a higher Signal to Noise (SNR). Next, we utilize the channel confidence levels to denote the degree of the available channel. We assume that the DAU scans all the channels of the primary user and the results are sent to the DAU transmitter. The channel confidence level is formulated by the following conditional probability:
γ k = E [ C m | | C ^ k | ] = P r [ C m = 1 | C ^ k ] = ( 1 - p q ) P r [ | C ^ k | = 1 ] ( 1 - p q ) P r [ | C ^ k | | C k = 1 ] + p q P r [ | C ^ k | | C k = 0 ] , (6) where γ k ∈ [ 0 , 1 ] , and | C ^ k | denotes the number of C ^ k = 1 in the sensing results. For cognitive wireless networks in a smart grid, the communication is composed of two scheduled time slots: within the first time slot, the relay receives the information from the DAU and the interference from the PT simultaneously. x s and x p are the information generated from the DAU transmitter and the PT, respectively. The received signal [42,43,44] at the relay is denoted by y s , m ,
y s , m = P s h s , m x s + ( 1 - C k ) P p g m x p + η s , m , (7) where P s and P p are the transmission power of the DAU transmitter and the PT, respectively. h s , m and g m are the channel-to-noise ratio from the DAU transmitter and the PT to the relay, respectively. η s , m denotes the zero-mean circular symmetric complex Gaussian noise at the DAU transmitter and the relays. In Equation (7), the received signal at the relays consists of three parts. The first part is the information that the relays receive from the DAU transmitter. The second part is the interference that the relays receive from the primary user. The third part is the background noise. The relays receive average information from the DAU sender as follows:
y ¯ s , m = E C k | C k ^ ( P s h s , m x s + ( 1 - C k ) P p g m x p + η s , m ) = P s h s , m x s + ( 1 - E C k | C k ^ ) P p g m x p + η s , m = P s h s , m x s + ( 1 - γ k ) P p g m x p + η s , m , (8)
In the second time slot, we employ the amplify-and-forward (AF) [45] relay strategy for the cognitive wireless network in a smart grid. The relay receives the information that the DAU transmitter retransmits to the PR. By introducing the beamforming weight vector w m , the retransmission signal can be represented as
x m , d = w m y ¯ s , m | y ¯ s , m = w m y ¯ s , m P s h s , m + ( 1 - γ k ) 2 P p g m + N s , m , (9) In the DAU receiver, by introducing the beamforming weights w d [46], the received signal at the DAU receiver is
y m , d = w d ( P m h m , d x m , d + ( 1 - C k ) P p g d x p + η m , d ) = w d ( w m P m h m , d P s h s , m x s A + ( w m ( 1 - γ k ) P m h m , d P s h s , m A + ( 1 - C k ) P p g d ) x p + w m η s , m P m h m , d A + η m , d ) , (10) where A = P s h s , m + ( 1 - γ k ) 2 P p g m + N s , m .
According to Equation (10), we can obtain the signal-to-noise ratio [47] that the information from the DAU transmitter through the relays to the DAU receiver as follows:
S N R = ∑ m = 1 l S m N m , (11) where S m and N m are the received signals and background noise, respectively. And the expression are as follows:
S m = | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 , (12) and
N m = | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 . (13)
Without loss of generality, we assume that the noise power of all links are the same and denoted as σ 2 . For the cooperative relaying transmission from the utility company to the consumers under the amplify-and-forward (AF) relaying strategy, the receiving rate [48] of the gateway is defined as
R = W 2 log 2 ( 1 + S N R ) , (14) where W is the transmission bandwidth of the DAU.
Substituting Equations (11)–(14) into Eqiation (5), gives
P r = ( T - W 2 log 2 ( 1 + ∑ m = 1 l | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T , (15)
2.4. Costs to Utility Company
In this section, a case of the temperature-priority control strategy which was developed in [49] is studied. As illustrated in Figure 2, the “on” loads with lower indoor temperatures have higher priorities to turn off, and the “off” loads with higher indoor temperatures have higher priorities to turn on. Therefore, the aggregated loads are ranked by their indoor temperatures. Then the loads with lower priorities will be turned on or off in sequence until the load can combine the AGC signal with the baseline load to follow the reference signal.
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Figure 2. Temperature-priority control strategy.
Taking the packets loss rate P r = 5 % as an example, we can obtain the tracking error distribution of the load control strategy through the MATLAB and EasyFit software [50]. As shown in Figure 3, the tracking errors follow the normal distribution. For the reliability of the communication, the probability of providing ancillary service is required to be larger than 99%. Thus, the utility company has to purchase u + 3 σ AGC service because there is P ( μ - 3 σ < = x < = μ + 3 σ ) ≥ 99 % under the normal distribution. We have
Z = p a ( μ + 3 σ ) , (16) where μ is the expectation, σ is the standard variance, and p a is the price per unit fraction of AGC service.
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Figure 3. The tracking error distribution under the packets loss.
Assume the expectation and the standard variance scale linearly with the packets loss rate, i.e., μ = A P r + B and σ = C P r + D . Substituting the expression of μ and σ into Equation (16) and combining with Equation (15), we obtain
Z = p a ( ( A + 3 C ) ( T - W 2 log 2 ( 1 + ∑ m = 1 l | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T + B + 3 D ) . (17)
3. Problem Formulation and Solutions
In this section, we first give the problem formulation and then derive the optimal power allocation to the DAU and the relays. The problem is equivalent to selecting the optimal power allocation of the DAU p s and the relay p m such that the costs to utility company are minimized, and the optimization problem is cast into the following problem.
( P 1 ) min Z s . t . P s + ∑ m = 1 l P m ≤ P t ( 1 - γ k ) P s h s , p ≤ I 0 ( 1 - γ k ) ∑ m = 1 l P m h m , d ≤ I 0
The first constraint is the total power restrictions of the cognitive radio network, the second constraint is the interference temperature threshold constraints, and the third constraint denotes that the transmission of DAU transmitter and the relays should be less than the interference temperature threshold constraints for primary user.
Remark 1. We can observe that (P1) is a non-convex optimization problem according to Equation (17). The traditional gradient optimization methods cannot be applied to solve it. Next, we employ PSO to search for the optimum. Specially, the optimal solution can be obtained by using the KKT condition when the optimization problem has only one relay .
3.1. PSO Algorithm
We use the PSO algorithm to solve the multi-variable optimization problem [51]. For an optimization problem of D variables, the potential solution of the optimization problem can be described as a point in D-dimensional space. Each particle has a velocity vector to determine its direction and a fitness value to measure its corresponding optimization state., The position and velocity are adjusted in D-dimensional search space according to the current optimal particle.
The process can be converted into a mathematical problem as follows. The PSO is initiated by a group of random particles (solutions), and then it searches for the optimum by updating generations. Each particle updates its position by using best present (pbest) and global best (gbest) in the next iteration. The ith particle in D-dimensional space is represented as x i = ( x 1 i , x 2 i , . . . , x d i , . . . , x D i ) , where x d i ∈ [ x min , x max ] , d ∈ [ 1 , D ] . The velocity corresponding to the ith particle is v i = ( v 1 i , v 2 i , . . . , v d i , . . . , v D i ) , where v d i ∈ [ v min , v max ] . The velocity and location update strategies of the i t h particle are defined by :
v i d ← v i d + c 1 · r a n d 1 i d · ( p b e s t i d - p i d ) + c 2 · r a n d 2 i d · ( g b e s t d - p i d ) , (18)
p i d = p i d + v i d , (19) where c 1 and c 2 are the constriction factors. c 1 represents the weight that the ith particle tracks its own historical optimal value pbest i , and c 2 represents the weight that the ith particle tracks the whole group’s optimal value gbest . All particles use the same values c 1 and c 2 . pbest i and gbest are updated all the time according to each particle’s fitness value. Moreover, r a n d 1 i d and r a n d 2 i d stand for random values that are in the range between 0 and 1.
The position of each particle represents the variables of the system. In this paper, the variables are the DAU’s power allocation P s and the relays’s power allocation P m . The flowchart of the PSO algorithm is given in Figure 4, and the pseudo-code of PSO is given in Algorithm 1 as below.
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Figure 4. The flow chart of particle swarm optimization (PSO) algorithm.
Algorithm 1 PSO Algorithm
Input:
Z: size of the whole population; iter-max: maximum iterations; Initialize each particle’s position p i d and velocity v i d .
Output:
each particle’s position p i d .
1:
for iter=1: iter-max do
2:
Calculate their fitness values and update pbest i , gbest ;
3:
Update each particle using Equations (18) and (19) and revise v i d , p i d using v i d =min( v max , max( v max , v i d )), p i d =min( p max , max( p max , p i d ));
4:
end for
3.2. The Solution with One Relay
The DAU selects one relay to transmit information to the consumers. In that case, the costs to the utility company can be denoted as
Z 1 = p a ( ( A + 3 C ) ( T - W 2 log 2 ( 1 + | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T + B + 3 D ) . (20)
In order to minimize the costs to utility company we need to select the optimal power allocation of the DAU p s and the relay p m . And The optimization problem can be described as follows:
( P 2 ) min Z 1 s . t . P s + P m ≤ P t ( 1 - γ k ) P s h s , p ≤ I 0 ( 1 - γ k ) P m h m , d ≤ I 0
We solve the above optimization problem by the Karush Kuhn Tucker (KKT) conditions and obtain the optimal power allocation solution:
( P s * , P m * ) = ( P s , P m ) , i f P s ≤ P s m a x a n d P s ≤ P s m a x ( m i n ( P t - P m m a x , P s m a x ) , P m m a x ) , i f P s < P s m a x a n d P s > P s m a x ( P m m a x , m i n ( P t - P s m a x , P m m a x ) , i f P s > P s m a x a n d P s < P s m a x ( P s m a x , P m m a x ) , i f P s > P s m a x a n d P s > P s m a x , where
( P s , P m ) = ( h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m , h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m ) , (21) and
( P s m a x , P m m a x ) = ( I 0 ( 1 - γ k ) 2 h s , p , I 0 ( 1 - γ k ) 2 h s , p ) . (22)
The optimal power allocation solution Equation (21) is meaningless when γ k = 1 , therefore, we need to analyze the special case that γ k = 1 , which denotes that there is no interference between the primary user and the DAU. Thus, the corresponding interference constraints should be deleted. Moreover, the relay receives the signal of the primary user as follows:
y s , m = P s h s , m x s + η s , m . (23)
The relaying signals under AF relay strategy are as follows:
y s , m ′ = w m y s , m P s h s , m + N s , m . (24)
The received signals from the relay at the DAU receiver are:
y m , d = w d ( P m h m , d y s , m ′ + η m , d ) , (25)
Substituting Equations (23) and (24) into Equation (25), gives
y m , d = P s P m h s , m h m , d w m w d x s P s h s , m + N s , m + ( P m h m , d w m w d η s , m P s h s , m + + N s , m + η m , d w d ) , (26) and the signal-to-noise ratio can be expressed as
S N R = P s P m h s , m h m , d P m h m , d N s , m + N m , d ( P s h s , m + N s , m ) . (27)
Therefore, the optimization problem (P1) can be converted to the following optimization problem:
( P 3 ) min Z 2 s . t . P s + P m ≤ P t
The optimal power solutions based on the KKT conditions are as follows:
( P s * , P m * ) = ( h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m , h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m ) (28)
4. Simulation Results
In the simulation, we consider a communication system consisting of a primary user, a DAU, a relay, and one gateway shared by the consumers. The primary transmitter is located at the origin, the DAU is distributed in (0 m, 30 m), the gateway is located at (20 m, −20 m), and the relays are randomly distributed in the area of (100 m × 100 m). The total system bandwidth is set to be W = 10 4 Hz, the probability of correct transmission from the gateway to the consumers is g ′ = 0 . 99 , and the base price of the AGC service is p a = 20 $/MW. The arriving rates of the DAU are 100 bits/s, i.e., T = 100 bits/s, and the noise power of all communication links is 10 - 1 W, i.e., σ 2 = 10 - 1 W. In addition, the existing probability of primary user in the carrier is p q = 0 . 5 , the false alarm probability is p f = 0 . 2 , the correct detection probability is p d = 0 . 8 , and the interference threshold is I 0 = 6 . 3096 db. In addition, the DAU’s maximum and minimum power allocation are 12 mW and 0 mW, respectively. The relays’s maximum and minimum power are 5 mW and 0 mW, respectively.
In the cognitive radio network, the relays sense the occupancy of the PR’s carriers, and then transmit the sensing results to the DAU. Hence, the DAU calculates the channel confidence level by combining all the sensing results. Furthermore, the DAU determines the relaying power allocation. In the simulation, we use the binomial distribution of 0 and 1 to generate the sensing results.
The convergence results of the best fitness values (i.e., the costs to the utility company) with different number of relays are shown in Figure 5. It is observed that the PSO algorithm can converge to the optimal solution. Comparing the fitness values with different number of relays, we observe that the the costs to the utility company can be reduced by deploying multiple relays.
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Figure 5. The convergence curve of the fitness value with different number of relays. (a) The convergence of the fitness value with one relay; (b) The convergence of the fitness value with six relays; (c) The convergence of the fitness value with ten relays.
The costs to the utility company Z versus the total power P t are given in Figure 6. The costs to the utility company have the similar changing trend under the direct transmission and the cooperative relaying, but the cooperative relaying can reduce the costs to the utility company dramatically.
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Figure 6. The costs to utility company under the transmission modes.
The performance comparisons between the direct transmission and the cooperative relaying are given in Table 2. It is shown that the cooperative relaying can reduce the SNR , the packets loss rate, and the costs to the utility company dramatically.
Table
Table 2. Comparison results.
The relationship between the costs to the utility company Z and the total power P t under Sensing Information (SI) and Non-Sensing Information (NSI) is shown in Figure 7. It is straightforward to observe that the costs to the utility company increases with the total power, however, the costs to the utility company under SI has lower cost than the costs under NSI significantly.
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Figure 7. The costs to utility company under Sensing Information (SI) and Non-Sensing Information (NSI).
As shown in Figure 8, the costs to the utility company are examined with the change of the existence probability of primary user under different total power P t . It can be observed that the costs to utility company under different total power have the similar trend and the costs to utility company is decreasing with the total power before the critical total power and remain the same and then increasing with the total power after that.
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Figure 8. The costs to utility company under different P t .
The relationship between the costs to the utility company and the existence probability of primary user in different total power P p is shown in Figure 9. The costs to the utility company both reach the rock bottom and remain the same, but begin a slow increase after that.
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Figure 9. The cost to utility company under different P p .
5. Conclusions
This paper studies the power allocation problem for a cognitive wireless network in a smart grid based on the sensing information and minimizes the costs to a utility company by using the PSO algorithm to search for the optimal power allocation under the interference temperature threshold constraints of primary users. We obtain the optimal power allocation for cognitive wireless networks in a smart grid and study the cases that only one relay is selected by DAU and the channel is not occupied by the primary user. The simulation results illustrate that the optimal power allocation and the sensing information can decrease the costs to the utility company for cognitive wireless networks in a smart grid. This paper only considers one DAU and that will limit the performance of the cognitive radio network in a smart grid. In the future, we will extended the model to the case with multiple DAUs and multiple relays.
Acknowledgments
This research was supported in part by National Natural Science Foundation of China under Grants 61573303, 61503324, and 61473247, in part by Natural Science Foundation of Hebei Province under Grant F2016203438, E2017203284, F2017203140, and F2017203084, in part by Project Funded by China Postdoctoral Science Foundation under Grant 2015M570233 and 2016M601282, in part by Project Funded by Hebei Education Department under Grant BJ2016052, in part by Technology Foundation for Selected Overseas Chinese Scholar under Grant C2015003052, and in part by Project Funded by Key Laboratory of System Control and Information Processing of Ministry of Education under Grant Scip201604.
Author Contributions
Kai Ma contributed the idea and wrote the paper; Xuemei Liu conceived and designed the experiments; Jie Yang performed the experiments; Zhixin Liu analyzed the data; Yazhou Yuan contributed the analysis tools.
Conflicts of Interest
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
Nomenclature
C k , C ^ k Binary variable.
p q The existing probability of the primary user in each carrier.
p f The false alarm probability.
p d The correct detection probability.
P sr The transmission power of the DAU transmitter or relays.
H sr , p The channel gain from the DAU transmitter or relays to the PR.
I 0 The interference temperature threshold of the primary user.
P p The transmission power of the primary user.
H p , dm The channel gain from the PT to the DAU receiver or relay.
T The arriving rates of the DAU.
R The receiving rate of the gateway.
g ′ The correct transmission ratio from the gateways to the consumers.
γ k The channel confidence level.
x s The information generated from the DAU transmitter.
x p The information generated from the PT.
y s , m The received signal at the relay.
P s The transmission power of the DAU transmitter.
P p The transmission power of the PT.
h s , m The channel-to-noise ratio from the DAU transmitter.
g m The channel-to-noise ratio from the PT to the relay.
η s , m The zero-mean circular symmetric complex Gaussian noise at the DAU transmitter and the relay.
w m , w d The beamforming weight.
S The received signals.
N The background noise.
P r The packets loss rate.
μ The expectation.
σ The standard variance.
p a The price per unit fraction of AGC service.
Z The costs to utility company with multiple relays.
Z ′ The costs to utility company with a relay.
v i d The dth dimension of the velocity for the ith particle.
x i d The dth dimension of the position for the ith particle.
g b e s t The whole group’s optimum value.
p b e s t i The ith particle’s historical optimum value.
r a n d 1 i d Uniform random number over [0,1].
r a n d 2 i d Uniform random number over [0,1].
l w o r s t i k The two worst particles for each sub-swarm.
l b e s t i The better particle for each sub-swarm.
c 1 , c 2 The learning factors.
ω Inertia weight.
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note, search on Bing - maybe more and different articles will be shown
the search engines use algorythms created by what the user's activity is on internet. I believe that is why your search may be different than mine....
Title for Blog: Technocracy, NWO, and 5G Technology Notes
23July2017
Technocracy is what the NWOs are developing
Watch/listen to SAGE OF QUAY on YouTube! Sageofquay.blogspot.com & sageofquay.com
5 G AND ITS DANGERS. SMART GRID - MILITAR TECHNOLOGY. MY NOTES OF THE BROADCAST/AUDIO ON YOUTUBE
www.thecrowhouse.com - guest speaker
Greed, fear and quest for knowledge, replacing humanity with biologically controlled humans through automation via 5G technology
Milimeter waves - are dangerous to society = radiation
Future Technology - nanotechnology. Projected achieve time 2025-2030
Our bodies aer fully autotonomic (I presume this reflects Intelligence Design), self-sustaining
Nanocarbons can print and tie human cellular activity
GIVING LIFE TO LUCIFER CONCEPT
Lucifer is the bearer of Ligt. Light represents knowledge. Cypher=code
Are they trying to uncode life itself? They just cannot!
Human attention span is now less than a gold fish! We are constantly squabbeling!
Gold fish - 9 second attention span
Human - 8 seconds
If you haven't changed what is in your heart, you will not use this technology safely!
Christ got angry in the temple throwing the tables. Reflection of monetary system is implied.
We need the spiritual understanding to cause effective change because we are losing our life skills!
Avoid revolution and violence - yet be ready to fight if necessary!
AI vs EI concept
Consciousness of internet
Thirst for knowledge
Fear concept
Put down the credit cards and mobile phones!
Archinet - anthenet. Created in 1954
(note, the CIA was established in 1952. Iran was overthrown and the CIA helpd Reza Shah to get in power. Queen Elizabeth's father had an untimely heart attack and died while she and Prince Phillip were in Africa, I think on their honeymoon. Korean War was started - all in this time frame)
SERN - was the development of internet after this
Use of cursing/spells - different languages - turn into spells we are casting - because of internet
internet = NET
World Wide Web = WEB
Catch concept - they want to catch the user, consider this
Rediscover Humanity!!
Pick up a pencil! Learn your Three R's (reading, writing, arithmatic)
Your thoughts create your reality
Manipulation of the consciousness
Quantum computing and D wave Tapping ino other dimensions
PizzaGate - uses BLOCK CHAIN - cypher activity. To access pedophile material
Internet is being used t control and map us!
We ampa-morphize everything - someone will come and say he's Christ, yet he is really created by internet!
KNOW THE ENEMY - ARE THEY AN ENEMY? - WHICH ONES ARE SATANIC?
These are a group of men around a table who want to make more money. This greed creates their reality.
Are being experimented on now?
Gathering data now to recreate/create a new HUMANITY?
We still have a chance to be human by putting down the technology - stay hoe and love family and friends!
Mandella Effect discussed. ============================== I searched on Google======= scholarly articles on 5G Technology======================
BELOW IS A SEARCH ON LINKS OF SCHOLARLY ARTICLES ON G5 GRID
http://ieeexplore.ieee.org/abstract/document/6815890/?reload=true
Scholarly articles
http://ieeexplore.ieee.org/abstract/document/6736744/
https://link.springer.com/article/10.1007/s11036-015-0590-7
https://link.springer.com/article/10.1007/s11432-015-5397-4
http://ieeexplore.ieee.org/abstract/document/7389830/
https://link.springer.com/article/10.1007/s11235-014-9885-4
An energy-aware scheme for efficient spectrum utilization in a 5G mobile cognitive radio network architecture
https://link.springer.com/article/10.1007/s11235-014-9890-7
Relay selection for secure 5G green communications
https://www.hindawi.com/journals/misy/2016/9767065/abs/
Research Article
SDN Controlled mmWave Massive MIMO Hybrid Precoding for 5G Heterogeneous Mobile Systems
Mobile Information Systems
Volume 2016 (2016), Article ID 9767065, 10 pages
http://dx.doi.org/10.1155/2016/9767065
https://link.springer.com/article/10.1007/s11036-014-0563-2
Adaptive SON and Cognitive Smart LPN for 5G Heterogeneous Networks
Mobile Networks and Applications
December 2015, Volume 20, Issue 6, pp 745–755
https://www.hindawi.com/journals/misy/2017/3680671/abs/
Mobile Information Systems
Volume 2017 (2017), Article ID 3680671, 11 pages
https://doi.org/10.1155/2017/3680671
Research Article
Efficient and Privacy-Aware Power Injection over AMI and Smart Grid Slice in Future 5G Networks
Yinghui Zhang,1,2,3 Jiangfan Zhao,1 and Dong Zheng1,3
1National Engineering Laboratory for Wireless Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2State Key Laboratory of Cryptology, Beijing 100878, China
3Westone Cryptologic Research Center, Beijing 100070, China
http://onlinelibrary.wiley.com/doi/10.1111/j.1530-9290.2010.00278.x/full
Journal of Industrial Ecology
Explore this journal >
Volume 14, Issue 5
October 2010
Pages 770–790
Journal of Industrial Ecology
Previous article in issue: The Energy and Climate Change Implications of Different Music Delivery Methods
Next article in issue: A High-Resolution Statistical Model of Residential Energy End Use Characteristics for the United States
View issue TOC
Special Issue:
Environmental Applications of Information & Communication Technology
Greenhouse Gas Emissions and Operational Electricity Use in the ICT and Entertainment & Media Sectors
https://scholar.google.com/citations?user=pts2z0wAAAAJ&hl=en&oi=sra
This link has a huge list of scholarly articles on the 5G grid, internet science
Maziar Nekovee
Professor of Telecommunications & Mobile Technology, Sussex University
6G, auction theory, bio-communications, brain-mobile interface
Verified email at sussex.ac.uk - Homepage
https://books.google.com/books?hl=en&lr=&id=HzXZCgAAQBAJ&oi=fnd&pg=PP1&dq=scholarly+articles+for+5g+smart+grid&ots=b9-4ekEL6R&sig=CdBBuTqMmdMm1gCGQThRknU93xQ#v=onepage&q&f=false
http://ieeexplore.ieee.org/abstract/document/6807956/
Browse Journals & Magazines > IEEE Communications Magazine > Volume: 52 Issue: 4
Smart grid technologies for future radio and data center networks
http://ieeexplore.ieee.org/abstract/document/6815890/
Scenarios for 5G mobile and wireless communications: the vision of the METIS project
http://journals.sagepub.com/doi/full/10.1155/2015/872821
Cognitive Radio Enabled Wireless Sensor Networks and Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published January 1, 2015 Editorial
https://www.hindawi.com/journals/misy/2016/2676589/abs/
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
http://dx.doi.org/10.1155/2016/2676589
Review Article
Survey of Promising Technologies for 5G Networks
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
Guangyi Liu and Dajie Jiang
China Mobile Research Institute, Beijing 100053, China
http://journals.sagepub.com/doi/full/10.1155/2015/872821
Cognitive Radio Enabled Wireless Sensor Networks and Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published January 1, 2015 Editorial
https://www.hindawi.com/journals/misy/2016/2676589/abs/
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
http://dx.doi.org/10.1155/2016/2676589
Review Article
Survey of Promising Technologies for 5G Networks
Nam Tuan Le,1 Mohammad Arif Hossain,1 Amirul Islam,1 Do-yun Kim,2 Young-June Choi,2 and Yeong Min Jang1
https://www.hindawi.com/journals/cje/2016/5974586/abs/
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
http://dx.doi.org/10.1155/2016/5974586
Research Article
5G: Vision and Requirements for Mobile Communication System towards Year 2020
Guangyi Liu and Dajie Jiang
China Mobile Research Institute, Beijing 100053, China
Received 8 October 2015; Accepted 2 March 2016
Academic Editor: Juho Lee
http://ieeexplore.ieee.org/abstract/document/7875185/
Browse Journals & Magazines > IEEE Access > Volume: 5
IEEE Access Special Section Editorial: Optimization for Emerging Wireless Networks: IoT, 5G, and Smart Grid Communication Networks
The Digital Revolution Internet Of Things 5 G and Beyond - this is a downloadable PDF
AM French, JP Shim CAIS 2016
http://ieeexplore.ieee.org/abstract/document/7909091/
Browse Journals & Magazines > IEEE Wireless Communications > Volume: 24 Issue: 2
Smart Grids
https://www.hindawi.com/journals/ijap/2016/7202143/abs/
International Journal of Antennas and Propagation
Volume 2016 (2016), Article ID 7202143, 10 pages
http://dx.doi.org/10.1155/2016/7202143
Research Article
Millimeter-Wave Microstrip Antenna Array Design and an Adaptive Algorithm for Future 5G Wireless Communication Systems
Cheng-Nan Hu,1 Dau-Chyrh Chang,1 Chung-Hang Yu,2 Tsai-Wen Hsaio,2 and Der-Phone Lin2
1Communication Engineering Department, OIT, New Taipei City 22061, Taiwan
2National Chung-Shan Institute of Science and Technology, P.O. Box 90008, Longtan, Taoyuan City, Taiwan
http://ieeexplore.ieee.org/abstract/document/7430394/
Browse Journals & Magazines > IEEE Journal on Selected Area... > Volume: 34 Issue: 3
Guest Editorial Emerging Technologies
Green Communications and Computer Networks - PDF document
By J Wu, J Thompson, H Zhang
http://ieeexplore.ieee.org/abstract/document/7568464/
Browse Conferences > Computer Communication and Ne...
[Panel Sessions - 2 abstracts. - Note, Google describes this document as Panel One.Apparently they deleted Panel One and I wonder why]
Abstract:
Contains abstracts for Panel I: Emerging Research Challenges in the Era of IOT and Panel II: Research Challenges in Big Data and Cloud Computing. The complete presentations were not made available for publication as part of the conference proceedings.
Published in: Computer Communication and Networks (ICCCN), 2016 25th International Conference on
Date of Conference: 1-4 Aug. 2016
http://www.rgnpublications.com/journals/index.php/jims/article/view/458
A Novel Cognitive Radio enabled IoT System for Smart Irrigation
Ammar Ahmed Khan, Aamir Zeb Shaikh, Shabbar Naqvi, Talat Altaf
Abstract
A novel architecture is proposed and analyzed that incorporates cognitive radio concept into Internet of Things (IoT) for smart irrigation system. The proposed system will optimize the use of natural resource i.e. water. Typically, the flow of water for irrigation of crop fields is not uniform due to many reasons including non-uniform terrain, availability of resources at different sites and etc.This un-even flow produces lesser product from the farms. The proposed system uses two data types to model the different conditions of crop filed. Based on these assumptions, the proposed system is analyzed. The simulation results for the proposed scenario are also presented.
http://www.mdpi.com/1996-1073/10/7/909/htm
This is a PDF, yet I am able to access the link
Energies 2017, 10(7), 909; doi:10.3390/en10070909
Article
Optimal Power Allocation for a Relaying-Based Cognitive Radio Network in a Smart Grid
Kai Ma 1,*, Xuemei Liu 1, Jie Yang 1,2, Zhixin Liu 1 and Yazhou Yuan 1
1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
* Correspondence: Tel.: +86-139-3367-9689
Academic Editors: Frede Blaabjerg and Hongjian Sun
Received: 28 April 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
Abstract: This paper obtains optimal power allocation to the data aggregator units (DAUs) and relays for cognitive wireless networks in a smart grid (SG). Firstly, the mutual interference between the primary user and the DAU are considered, and the expressions of the DAU transmission signal are derived based on the sensing information. Secondly, we use the particle swarm optimization (PSO) algorithm to search for the optimal power allocation to minimize the costs to the utility company. Finally, the impact of the sensing information on the network performance is studied. Then two special cases (namely, that only one relay is selected, and that the channel is not occupied by the primary user) are discussed. Simulation results demonstrate that the optimal power allocation and the sensing information of the relays can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
Keywords: cognitive wireless network; smart grid; DAU; relay; power allocation; PSO
I am pasting this whole document because it is recent and allows me to:
Typesetting math: 20%
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Abstract
Introduction
Cognitive Wireless Network Model in a Smart Grid
Problem Formulation and Solutions
Simulation Results
Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
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Open Access
Energies 2017, 10(7), 909; doi:10.3390/en10070909
Article
Optimal Power Allocation for a Relaying-Based Cognitive Radio Network in a Smart Grid
Kai Ma 1,*, Xuemei Liu 1, Jie Yang 1,2, Zhixin Liu 1 and Yazhou Yuan 1
1 School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Key Laboratory of System Control and Information Processing, Ministry of Education, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
* Correspondence: Tel.: +86-139-3367-9689
Academic Editors: Frede Blaabjerg and Hongjian Sun
Received: 28 April 2017 / Accepted: 28 June 2017 / Published: 3 July 2017
Abstract: This paper obtains optimal power allocation to the data aggregator units (DAUs) and relays for cognitive wireless networks in a smart grid (SG). Firstly, the mutual interference between the primary user and the DAU are considered, and the expressions of the DAU transmission signal are derived based on the sensing information. Secondly, we use the particle swarm optimization (PSO) algorithm to search for the optimal power allocation to minimize the costs to the utility company. Finally, the impact of the sensing information on the network performance is studied. Then two special cases (namely, that only one relay is selected, and that the channel is not occupied by the primary user) are discussed. Simulation results demonstrate that the optimal power allocation and the sensing information of the relays can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
Keywords: cognitive wireless network; smart grid; DAU; relay; power allocation; PSO
1. Introduction
Smart grid (SG) is the modernization of generation, transmission, and distribution of a power grid system with the integration of advanced information and communication technologies (ICTs). The decentralized nature enables the integration of renewable energy resources and promises a two-way communications between consumers and utility company, which will improve the efficiency of utility company programs such as demand response, customer participation, and advanced smart metering [1]. In a smart grid, regulation is a type of ancillary service which continuously balance supply with demand in electricity markets under normal conditions [2]. Generally, the regulation service can be provided by on-line generation units that are equipped with the automatic generation control (AGC). In ancillary service markets, the utility company purchases the AGC service according to the errors between the generation and the load. Thus, the electricity costs to the utility company are increased with the errors between supply and demand. It was demonstrated that the errors can be reduced by the demand-side regulation [3,4], which is dependent on the two-way communications between the utility company and the consumers.
Cognitive radio (CR) [5] is widely recognised as a dynamic spectrum access technique, which enables unlicensed users to share the spectrum with licensed users [6,7]. The author [8] investigated how CR can be utilized to serve a smart grid deployment, from a home area network to power generation. It is recognized as a promising technology to address the communication and networking problems in the smart grid [9]. Under the background of the smart grid [10], the two-way communications [11] can be implemented by the advanced metering infrastructure (AMI), which includes cognitive home area networks, cognitive neighborhood area networks, and cognitive wide area networks. Moreover, several advanced communication technologies have been applied to demand response in a smart grid [12,13,14,15,16]. In Reference [12], the authors propose two different architectures for CR communications systems based on the IEEE 802.22 standard to accommodate the current and future needs of SG communications. Cognitive radio-enabled smart grid was presented for demand response to reduce the communication outage [13]. The book [17] provided readers with the most extensive coverage of technologies for 5G wireless systems to date. A cooperative spectrum sharing strategy based on the Nash bargaining solution for cooperative cognitive systems and a power allocation technique with improved energy efficiency for MIMO-OFDM based CR with tolerable degradation at system capacity was proposed in References [18] and [19], respectively. The authors considered the problem of resource allocation in a two-way relay network [20]. In Reference [21], the authors studied the resource allocation algorithm for CR secondary networks with simultaneous wireless power transfer and secure communication based on a multiobjective optimization framework. The differences of the proposed work with the above literature are shown in Table 1.
Table
Table 1. Differences of the proposed work with the literature.
Recently, cooperative relaying has been proposed for communications in a smart grid. The basic idea of cooperative relay is to use relays to help mobile users to transmit to the destination, in order to combat the impact of fading [14] and improve the spectral efficiency [15] for smart grid communications. In Reference [16], D. Niyato et al. proposed a cooperative relay-based meter data collection networks in a smart grid, in order to reduce the electricity costs. The authors developed a scheme that optimized the user assignment and power allocation optimization in CR networks [22]. The secondary user power allocation problem in cognitive radio networks with uncertain knowledge of interference information was studied in Reference [23]. The authors in Reference [24] investigated the energy efficient power allocation for orthogonal frequency division multiplexing based cognitive radio networks (CRNs) in the underlay mode.
Particle swarm optimization (PSO) is a population based stochastic optimization algorithm which was originally introduced by Kennedy and Eberhart [25,26]. PSO has been extended to many application areas such as function optimization [27], artificial neural network training [28,29,30], fuzzy system control [31,32,33,34], power system [35,36] and image processing [37]. This algorithm is motivated by the emergent motion of the foraging behavior of a flock of birds. PSO consists of a swarm of particles. Each particle represents a potential solution, which is a point in the multi-dimensional search space. The global optimum of PSO is regarded as the location of food. Each particle has a fitness value and a velocity to adjust its flying direction according to the experiences of the particle itself and its neighbors. PSO is simple in implementation and has good convergence properties when compared to evolutionary algorithms [38]. The advantages of PSO have caused it to become one of the most popular optimization techniques.
To the best of our knowledge, the combination of the relaying and CR in a smart grid has not been considered in the literature. In this paper, we propose to use both relaying and CR in smart grid communications, in order to reduce the packets loss and improve the spectrum utilization simultaneously. We consider the cognitive wireless network in a smart grid and focus on how to reduce the packets loss in the downlink transmission and improve the quality of communication, and then minimize the costs to the utility company. The main contributions of this paper are as follows:
This paper converts the sensing errors into the channel available confidence and introduce the average interference constraint to the cognitive wireless networks in a smart grid.
We establish a cost model based on the statistical analysis with the regulation errors of a direct load control method for cognitive wireless networks in a smart grid. Specifically, the power allocation problem based on the sensing error information was formulated as a nonlinear optimization problem. Then we use the PSO algorithm to search for the optimum.
We demonstrate that the sensing information in power allocation can reduce the costs to the utility company for cognitive wireless networks in a smart grid.
The rest of the paper is organized as follows. In Section 2, we describe the cognitive wireless network model and the cost model to the utility company in a smart grid. The power allocation problem is formulated as a multi-variable optimization problem and PSO algorithm is employed to seek the optimal solution in Section 3. Simulation results are shown in Section 4. Finally, we draw conclusions in Section 5.
2. Cognitive Wireless Network Model in a Smart Grid
Consider a downlink cellular cognitive wireless network, which includes the primary network and cognitive radio network, as shown in Figure 1. The cognitive radio network is implemented by two-way communications between the utility company and the consumers. The DAU that is deployed by the utility company collects the temperature settings from the consumers and forwards them to the utility company in the uplink transmission. Meanwhile, the DAU receives the control commands from the utility company and forwards them through the relays to the consumers in the downlink transmission. In the primary network, the primary transmitter (PT) transmits to the primary receiver (PR). Assume that the PT transmits to the PR with a fixed power, and the DAU uses the vacant channel to transmit information according to the sensing information.
Energies 10 00909 g001 550
Figure 1. The cognitive wireless network in a smart grid.
2.1. Cognitive Wireless Network
The DAU accesses to the channels of the primary user by spectrum sensing. The available channel of the primary user is divided into k carriers, and the existing probability of the primary user in each carrier is p q . We use the binary variables to represent the activity of the primary user on the carrier k . We denote C k = 1 when the primary user is active on the carrier k and C k = 0 when the primary user is inactive. C ^ k is the sensing results of the DAU on carrier k. We denote C ^ k = 0 when the carrier is occupied by the primary user and C ^ k = 1 otherwise. In practice, the sensing results of the DAU are not accurate, which causes false alarm and mis-detection. The false alarm denotes the carrier that is actually vacant when the DAU believes that the primary user occupies the carrier due to sensing errors. The mis-detection denotes the carrier that is actually occupied by the primary user but refers to the case when DAU believes that the carrier is vacant. We denote the false alarm probability is p f and the mis-detection probability is 1 - p d . When the mis-detection happens, the cognitive network communication can produce interference to the PR, and the instantaneous interference can be expressed as
I sp = P sr | H sr , p | ( 1 - C k ) , (1) where P sr is the transmission power of the DAU transmitter or relays and H sr , p is the channel gain from the DAU transmitter or relays to the PR. We need to ensure that the average interference of the primary user is lower than the interference temperature threshold when the DAU occupies the communication channel of the primary user [39,40,41], i.e.,
I ¯ sp = E C k | C ^ k [ P sr | H sr , p | ( 1 - C k ) ] = P sr δ sr , p 2 ( 1 - E [ C k | C ^ k ] ) ≤ I 0 , (2) where I 0 denotes the interference temperature threshold of the primary user. The instantaneous interference from the primary user to the gateway or relay is described as follows:
I pd = P p | H p , dm | ( 1 - C k ) , (3) where P p is the transmission power of the primary user, H p , dm is the channel gain from the PT to the DAU receiver or relay. The corresponding average interference can be expressed as
I ¯ pd = E C k | C ^ k [ P p | H p , dm | ( 1 - C k ) ] = P sr δ p , dm 2 ( 1 - E [ C k | C ^ k , dm ] ) (4)
2.2. Packets Loss Model
We consider a communication model as shown in Figure 1, where the transmission strategy is the cooperative relaying. Without loss of generality, we only consider the packets loss in the downlink transmission and formulate the packets loss rate as
P r = ( T - R ) g ′ T , (5) where T denotes the arriving rates of the DAU, R is the receiving rate of the gateway, and g ′ is the correct transmission ratio from the gateways to the consumers.
2.3. Transmission Formulation of The Network
We assume that the PT can adjust the transmission power according to its own throughput requirements. Moreover, the utility company is restricted to the average interference temperature of primary user and improve the transmission quality as far as possible, in order to reduce the packets loss and the costs. Under the condition of the mutual interference, the DAU and the relays constitute a virtual antenna array through collaboration, and the relays terminal and DAU receiver will introduce two beamforming weights. In addition, the weight of the relays terminal can eliminate or reduce the interference from other networks, and the DAU is able to obtain a higher Signal to Noise (SNR). Next, we utilize the channel confidence levels to denote the degree of the available channel. We assume that the DAU scans all the channels of the primary user and the results are sent to the DAU transmitter. The channel confidence level is formulated by the following conditional probability:
γ k = E [ C m | | C ^ k | ] = P r [ C m = 1 | C ^ k ] = ( 1 - p q ) P r [ | C ^ k | = 1 ] ( 1 - p q ) P r [ | C ^ k | | C k = 1 ] + p q P r [ | C ^ k | | C k = 0 ] , (6) where γ k ∈ [ 0 , 1 ] , and | C ^ k | denotes the number of C ^ k = 1 in the sensing results. For cognitive wireless networks in a smart grid, the communication is composed of two scheduled time slots: within the first time slot, the relay receives the information from the DAU and the interference from the PT simultaneously. x s and x p are the information generated from the DAU transmitter and the PT, respectively. The received signal [42,43,44] at the relay is denoted by y s , m ,
y s , m = P s h s , m x s + ( 1 - C k ) P p g m x p + η s , m , (7) where P s and P p are the transmission power of the DAU transmitter and the PT, respectively. h s , m and g m are the channel-to-noise ratio from the DAU transmitter and the PT to the relay, respectively. η s , m denotes the zero-mean circular symmetric complex Gaussian noise at the DAU transmitter and the relays. In Equation (7), the received signal at the relays consists of three parts. The first part is the information that the relays receive from the DAU transmitter. The second part is the interference that the relays receive from the primary user. The third part is the background noise. The relays receive average information from the DAU sender as follows:
y ¯ s , m = E C k | C k ^ ( P s h s , m x s + ( 1 - C k ) P p g m x p + η s , m ) = P s h s , m x s + ( 1 - E C k | C k ^ ) P p g m x p + η s , m = P s h s , m x s + ( 1 - γ k ) P p g m x p + η s , m , (8)
In the second time slot, we employ the amplify-and-forward (AF) [45] relay strategy for the cognitive wireless network in a smart grid. The relay receives the information that the DAU transmitter retransmits to the PR. By introducing the beamforming weight vector w m , the retransmission signal can be represented as
x m , d = w m y ¯ s , m | y ¯ s , m = w m y ¯ s , m P s h s , m + ( 1 - γ k ) 2 P p g m + N s , m , (9) In the DAU receiver, by introducing the beamforming weights w d [46], the received signal at the DAU receiver is
y m , d = w d ( P m h m , d x m , d + ( 1 - C k ) P p g d x p + η m , d ) = w d ( w m P m h m , d P s h s , m x s A + ( w m ( 1 - γ k ) P m h m , d P s h s , m A + ( 1 - C k ) P p g d ) x p + w m η s , m P m h m , d A + η m , d ) , (10) where A = P s h s , m + ( 1 - γ k ) 2 P p g m + N s , m .
According to Equation (10), we can obtain the signal-to-noise ratio [47] that the information from the DAU transmitter through the relays to the DAU receiver as follows:
S N R = ∑ m = 1 l S m N m , (11) where S m and N m are the received signals and background noise, respectively. And the expression are as follows:
S m = | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 , (12) and
N m = | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 . (13)
Without loss of generality, we assume that the noise power of all links are the same and denoted as σ 2 . For the cooperative relaying transmission from the utility company to the consumers under the amplify-and-forward (AF) relaying strategy, the receiving rate [48] of the gateway is defined as
R = W 2 log 2 ( 1 + S N R ) , (14) where W is the transmission bandwidth of the DAU.
Substituting Equations (11)–(14) into Eqiation (5), gives
P r = ( T - W 2 log 2 ( 1 + ∑ m = 1 l | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T , (15)
2.4. Costs to Utility Company
In this section, a case of the temperature-priority control strategy which was developed in [49] is studied. As illustrated in Figure 2, the “on” loads with lower indoor temperatures have higher priorities to turn off, and the “off” loads with higher indoor temperatures have higher priorities to turn on. Therefore, the aggregated loads are ranked by their indoor temperatures. Then the loads with lower priorities will be turned on or off in sequence until the load can combine the AGC signal with the baseline load to follow the reference signal.
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Figure 2. Temperature-priority control strategy.
Taking the packets loss rate P r = 5 % as an example, we can obtain the tracking error distribution of the load control strategy through the MATLAB and EasyFit software [50]. As shown in Figure 3, the tracking errors follow the normal distribution. For the reliability of the communication, the probability of providing ancillary service is required to be larger than 99%. Thus, the utility company has to purchase u + 3 σ AGC service because there is P ( μ - 3 σ < = x < = μ + 3 σ ) ≥ 99 % under the normal distribution. We have
Z = p a ( μ + 3 σ ) , (16) where μ is the expectation, σ is the standard variance, and p a is the price per unit fraction of AGC service.
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Figure 3. The tracking error distribution under the packets loss.
Assume the expectation and the standard variance scale linearly with the packets loss rate, i.e., μ = A P r + B and σ = C P r + D . Substituting the expression of μ and σ into Equation (16) and combining with Equation (15), we obtain
Z = p a ( ( A + 3 C ) ( T - W 2 log 2 ( 1 + ∑ m = 1 l | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T + B + 3 D ) . (17)
3. Problem Formulation and Solutions
In this section, we first give the problem formulation and then derive the optimal power allocation to the DAU and the relays. The problem is equivalent to selecting the optimal power allocation of the DAU p s and the relay p m such that the costs to utility company are minimized, and the optimization problem is cast into the following problem.
( P 1 ) min Z s . t . P s + ∑ m = 1 l P m ≤ P t ( 1 - γ k ) P s h s , p ≤ I 0 ( 1 - γ k ) ∑ m = 1 l P m h m , d ≤ I 0
The first constraint is the total power restrictions of the cognitive radio network, the second constraint is the interference temperature threshold constraints, and the third constraint denotes that the transmission of DAU transmitter and the relays should be less than the interference temperature threshold constraints for primary user.
Remark 1. We can observe that (P1) is a non-convex optimization problem according to Equation (17). The traditional gradient optimization methods cannot be applied to solve it. Next, we employ PSO to search for the optimum. Specially, the optimal solution can be obtained by using the KKT condition when the optimization problem has only one relay .
3.1. PSO Algorithm
We use the PSO algorithm to solve the multi-variable optimization problem [51]. For an optimization problem of D variables, the potential solution of the optimization problem can be described as a point in D-dimensional space. Each particle has a velocity vector to determine its direction and a fitness value to measure its corresponding optimization state., The position and velocity are adjusted in D-dimensional search space according to the current optimal particle.
The process can be converted into a mathematical problem as follows. The PSO is initiated by a group of random particles (solutions), and then it searches for the optimum by updating generations. Each particle updates its position by using best present (pbest) and global best (gbest) in the next iteration. The ith particle in D-dimensional space is represented as x i = ( x 1 i , x 2 i , . . . , x d i , . . . , x D i ) , where x d i ∈ [ x min , x max ] , d ∈ [ 1 , D ] . The velocity corresponding to the ith particle is v i = ( v 1 i , v 2 i , . . . , v d i , . . . , v D i ) , where v d i ∈ [ v min , v max ] . The velocity and location update strategies of the i t h particle are defined by :
v i d ← v i d + c 1 · r a n d 1 i d · ( p b e s t i d - p i d ) + c 2 · r a n d 2 i d · ( g b e s t d - p i d ) , (18)
p i d = p i d + v i d , (19) where c 1 and c 2 are the constriction factors. c 1 represents the weight that the ith particle tracks its own historical optimal value pbest i , and c 2 represents the weight that the ith particle tracks the whole group’s optimal value gbest . All particles use the same values c 1 and c 2 . pbest i and gbest are updated all the time according to each particle’s fitness value. Moreover, r a n d 1 i d and r a n d 2 i d stand for random values that are in the range between 0 and 1.
The position of each particle represents the variables of the system. In this paper, the variables are the DAU’s power allocation P s and the relays’s power allocation P m . The flowchart of the PSO algorithm is given in Figure 4, and the pseudo-code of PSO is given in Algorithm 1 as below.
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Figure 4. The flow chart of particle swarm optimization (PSO) algorithm.
Algorithm 1 PSO Algorithm
Input:
Z: size of the whole population; iter-max: maximum iterations; Initialize each particle’s position p i d and velocity v i d .
Output:
each particle’s position p i d .
1:
for iter=1: iter-max do
2:
Calculate their fitness values and update pbest i , gbest ;
3:
Update each particle using Equations (18) and (19) and revise v i d , p i d using v i d =min( v max , max( v max , v i d )), p i d =min( p max , max( p max , p i d ));
4:
end for
3.2. The Solution with One Relay
The DAU selects one relay to transmit information to the consumers. In that case, the costs to the utility company can be denoted as
Z 1 = p a ( ( A + 3 C ) ( T - W 2 log 2 ( 1 + | w d | 2 | w m | 2 P m h m , d P s h s , m P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 | w d | 2 | w m | 2 P m h m , d σ 2 P s h s , m + ( 1 - γ k ) 2 P p g m + σ 2 + | w d | 2 σ 2 ) ) g ′ T + B + 3 D ) . (20)
In order to minimize the costs to utility company we need to select the optimal power allocation of the DAU p s and the relay p m . And The optimization problem can be described as follows:
( P 2 ) min Z 1 s . t . P s + P m ≤ P t ( 1 - γ k ) P s h s , p ≤ I 0 ( 1 - γ k ) P m h m , d ≤ I 0
We solve the above optimization problem by the Karush Kuhn Tucker (KKT) conditions and obtain the optimal power allocation solution:
( P s * , P m * ) = ( P s , P m ) , i f P s ≤ P s m a x a n d P s ≤ P s m a x ( m i n ( P t - P m m a x , P s m a x ) , P m m a x ) , i f P s < P s m a x a n d P s > P s m a x ( P m m a x , m i n ( P t - P s m a x , P m m a x ) , i f P s > P s m a x a n d P s < P s m a x ( P s m a x , P m m a x ) , i f P s > P s m a x a n d P s > P s m a x , where
( P s , P m ) = ( h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m , h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m ) , (21) and
( P s m a x , P m m a x ) = ( I 0 ( 1 - γ k ) 2 h s , p , I 0 ( 1 - γ k ) 2 h s , p ) . (22)
The optimal power allocation solution Equation (21) is meaningless when γ k = 1 , therefore, we need to analyze the special case that γ k = 1 , which denotes that there is no interference between the primary user and the DAU. Thus, the corresponding interference constraints should be deleted. Moreover, the relay receives the signal of the primary user as follows:
y s , m = P s h s , m x s + η s , m . (23)
The relaying signals under AF relay strategy are as follows:
y s , m ′ = w m y s , m P s h s , m + N s , m . (24)
The received signals from the relay at the DAU receiver are:
y m , d = w d ( P m h m , d y s , m ′ + η m , d ) , (25)
Substituting Equations (23) and (24) into Equation (25), gives
y m , d = P s P m h s , m h m , d w m w d x s P s h s , m + N s , m + ( P m h m , d w m w d η s , m P s h s , m + + N s , m + η m , d w d ) , (26) and the signal-to-noise ratio can be expressed as
S N R = P s P m h s , m h m , d P m h m , d N s , m + N m , d ( P s h s , m + N s , m ) . (27)
Therefore, the optimization problem (P1) can be converted to the following optimization problem:
( P 3 ) min Z 2 s . t . P s + P m ≤ P t
The optimal power solutions based on the KKT conditions are as follows:
( P s * , P m * ) = ( h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m , h m , d P t + σ 2 ± ( h s , m P t + σ 2 ) ( h m , d P t + σ 2 ) h m , d - h s , m ) (28)
4. Simulation Results
In the simulation, we consider a communication system consisting of a primary user, a DAU, a relay, and one gateway shared by the consumers. The primary transmitter is located at the origin, the DAU is distributed in (0 m, 30 m), the gateway is located at (20 m, −20 m), and the relays are randomly distributed in the area of (100 m × 100 m). The total system bandwidth is set to be W = 10 4 Hz, the probability of correct transmission from the gateway to the consumers is g ′ = 0 . 99 , and the base price of the AGC service is p a = 20 $/MW. The arriving rates of the DAU are 100 bits/s, i.e., T = 100 bits/s, and the noise power of all communication links is 10 - 1 W, i.e., σ 2 = 10 - 1 W. In addition, the existing probability of primary user in the carrier is p q = 0 . 5 , the false alarm probability is p f = 0 . 2 , the correct detection probability is p d = 0 . 8 , and the interference threshold is I 0 = 6 . 3096 db. In addition, the DAU’s maximum and minimum power allocation are 12 mW and 0 mW, respectively. The relays’s maximum and minimum power are 5 mW and 0 mW, respectively.
In the cognitive radio network, the relays sense the occupancy of the PR’s carriers, and then transmit the sensing results to the DAU. Hence, the DAU calculates the channel confidence level by combining all the sensing results. Furthermore, the DAU determines the relaying power allocation. In the simulation, we use the binomial distribution of 0 and 1 to generate the sensing results.
The convergence results of the best fitness values (i.e., the costs to the utility company) with different number of relays are shown in Figure 5. It is observed that the PSO algorithm can converge to the optimal solution. Comparing the fitness values with different number of relays, we observe that the the costs to the utility company can be reduced by deploying multiple relays.
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Figure 5. The convergence curve of the fitness value with different number of relays. (a) The convergence of the fitness value with one relay; (b) The convergence of the fitness value with six relays; (c) The convergence of the fitness value with ten relays.
The costs to the utility company Z versus the total power P t are given in Figure 6. The costs to the utility company have the similar changing trend under the direct transmission and the cooperative relaying, but the cooperative relaying can reduce the costs to the utility company dramatically.
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Figure 6. The costs to utility company under the transmission modes.
The performance comparisons between the direct transmission and the cooperative relaying are given in Table 2. It is shown that the cooperative relaying can reduce the SNR , the packets loss rate, and the costs to the utility company dramatically.
Table
Table 2. Comparison results.
The relationship between the costs to the utility company Z and the total power P t under Sensing Information (SI) and Non-Sensing Information (NSI) is shown in Figure 7. It is straightforward to observe that the costs to the utility company increases with the total power, however, the costs to the utility company under SI has lower cost than the costs under NSI significantly.
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Figure 7. The costs to utility company under Sensing Information (SI) and Non-Sensing Information (NSI).
As shown in Figure 8, the costs to the utility company are examined with the change of the existence probability of primary user under different total power P t . It can be observed that the costs to utility company under different total power have the similar trend and the costs to utility company is decreasing with the total power before the critical total power and remain the same and then increasing with the total power after that.
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Figure 8. The costs to utility company under different P t .
The relationship between the costs to the utility company and the existence probability of primary user in different total power P p is shown in Figure 9. The costs to the utility company both reach the rock bottom and remain the same, but begin a slow increase after that.
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Figure 9. The cost to utility company under different P p .
5. Conclusions
This paper studies the power allocation problem for a cognitive wireless network in a smart grid based on the sensing information and minimizes the costs to a utility company by using the PSO algorithm to search for the optimal power allocation under the interference temperature threshold constraints of primary users. We obtain the optimal power allocation for cognitive wireless networks in a smart grid and study the cases that only one relay is selected by DAU and the channel is not occupied by the primary user. The simulation results illustrate that the optimal power allocation and the sensing information can decrease the costs to the utility company for cognitive wireless networks in a smart grid. This paper only considers one DAU and that will limit the performance of the cognitive radio network in a smart grid. In the future, we will extended the model to the case with multiple DAUs and multiple relays.
Acknowledgments
This research was supported in part by National Natural Science Foundation of China under Grants 61573303, 61503324, and 61473247, in part by Natural Science Foundation of Hebei Province under Grant F2016203438, E2017203284, F2017203140, and F2017203084, in part by Project Funded by China Postdoctoral Science Foundation under Grant 2015M570233 and 2016M601282, in part by Project Funded by Hebei Education Department under Grant BJ2016052, in part by Technology Foundation for Selected Overseas Chinese Scholar under Grant C2015003052, and in part by Project Funded by Key Laboratory of System Control and Information Processing of Ministry of Education under Grant Scip201604.
Author Contributions
Kai Ma contributed the idea and wrote the paper; Xuemei Liu conceived and designed the experiments; Jie Yang performed the experiments; Zhixin Liu analyzed the data; Yazhou Yuan contributed the analysis tools.
Conflicts of Interest
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
Nomenclature
C k , C ^ k Binary variable.
p q The existing probability of the primary user in each carrier.
p f The false alarm probability.
p d The correct detection probability.
P sr The transmission power of the DAU transmitter or relays.
H sr , p The channel gain from the DAU transmitter or relays to the PR.
I 0 The interference temperature threshold of the primary user.
P p The transmission power of the primary user.
H p , dm The channel gain from the PT to the DAU receiver or relay.
T The arriving rates of the DAU.
R The receiving rate of the gateway.
g ′ The correct transmission ratio from the gateways to the consumers.
γ k The channel confidence level.
x s The information generated from the DAU transmitter.
x p The information generated from the PT.
y s , m The received signal at the relay.
P s The transmission power of the DAU transmitter.
P p The transmission power of the PT.
h s , m The channel-to-noise ratio from the DAU transmitter.
g m The channel-to-noise ratio from the PT to the relay.
η s , m The zero-mean circular symmetric complex Gaussian noise at the DAU transmitter and the relay.
w m , w d The beamforming weight.
S The received signals.
N The background noise.
P r The packets loss rate.
μ The expectation.
σ The standard variance.
p a The price per unit fraction of AGC service.
Z The costs to utility company with multiple relays.
Z ′ The costs to utility company with a relay.
v i d The dth dimension of the velocity for the ith particle.
x i d The dth dimension of the position for the ith particle.
g b e s t The whole group’s optimum value.
p b e s t i The ith particle’s historical optimum value.
r a n d 1 i d Uniform random number over [0,1].
r a n d 2 i d Uniform random number over [0,1].
l w o r s t i k The two worst particles for each sub-swarm.
l b e s t i The better particle for each sub-swarm.
c 1 , c 2 The learning factors.
ω Inertia weight.
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note, search on Bing - maybe more and different articles will be shown
the search engines use algorythms created by what the user's activity is on internet. I believe that is why your search may be different than mine....
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