Technocracy
AI Self-Replicating
Smart Dust & NanoTechBots R Us All
www.datasylum.com - biological programming interface. They want
to implant people with nano technology against their will! They are sociopaths!
For links to almost all posts to this blog:
http://www.appleofmyeyes.org/2018/04/table-of-contents.html
3/22 - Please be sure to see my posts on Mike Pence and Rex Tillerson! This is very serious, folks. Rex Tillerson was unlawfully fired and never did anything wrong. When the Deputy Secretary stated opposition, Trump unlawfully fired him. When Heather Nauert defended Rex Tillerson, Trump promoted her to replace the Deputy Secretary and told her to leave the country immediately. This may jeopardize the safety of Ms. Nauert and she may return being mind controlled. This was not a trip planned. Also see PitStop For March 2018 - copy and save in Word document if you want. The blog is not crypted and is not a money maker. I am not supporting Trump now. I have lost confidence when he fired Rex Tillerson and then demanded both Tillerson and Pence to be arrested for PEDOPHILIA - this is a very sick and discusting manuever by President Trump. He lost his competency.
http://www.appleofmyeyes.org/2018/04/table-of-contents.html
3/22 - Please be sure to see my posts on Mike Pence and Rex Tillerson! This is very serious, folks. Rex Tillerson was unlawfully fired and never did anything wrong. When the Deputy Secretary stated opposition, Trump unlawfully fired him. When Heather Nauert defended Rex Tillerson, Trump promoted her to replace the Deputy Secretary and told her to leave the country immediately. This may jeopardize the safety of Ms. Nauert and she may return being mind controlled. This was not a trip planned. Also see PitStop For March 2018 - copy and save in Word document if you want. The blog is not crypted and is not a money maker. I am not supporting Trump now. I have lost confidence when he fired Rex Tillerson and then demanded both Tillerson and Pence to be arrested for PEDOPHILIA - this is a very sick and discusting manuever by President Trump. He lost his competency.
Re: Technocracy, NWO, 5G Technology Notes
Main Source: Sage of Quay
Let's just call this Operation RAW to READY - you take my Raw Notes and make them Ready for something - please! Thank you!
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
Is Technocracy a Cult?
I share this to give you an idea of "what's out there" - not because I agree or disagree
<INSERT 11/9/2018> search: CHECKS AND BALANCES
I DO MY BEST TO KEEP THIS UP TO DATE:
ALL BLOGPOSTS ARE IN ALPHABETICAL ORDER HERE: https://thunderflower2021.blogpost.com/2021/10/table-of-contents.html?m=1
Update 11/9/2018 www.appleofmyeyes.org/2018/04/checks-and-balances.html
Published on Nov 9, 2018
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!!!
Search Tranny Watch here:
I DO MY BEST TO KEEP THIS UP TO DATE:
ALL BLOGPOSTS ARE IN ALPHABETICAL ORDER HERE: https://thunderflower2021.blogpost.com/2021/10/table-of-contents.html?m=1
We the GOOD people of United States want Peace and appose those leaders who incite war! <END OF INSERT>
5 G AND ITS DANGERS.
SMART GRID - MILITAR TECHNOLOGY. MY NOTES OF THE BROADCAST/AUDIO ON
YOUTUBE
- 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 Light. 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
Pick up a
pencil! Learn your Three R's (reading,
writing, arithmatic)
Your thoughts create your reality
Manipulation of te consciousness
Quantum computing and D wave
Tapping ino other dimensions
PizzaGate - uses BLOCK CHAIN - cypher activity. To access pedophile material
Internet is being used to 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 home and love family and friends!
(my note: as long as they don't kill your father like mine did, conspiring with a pedophile priest! Not caring about a 20 ton sander truck driver who was probably trying to murder me was another little ol' thing)
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/
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
https://link.springer.com/article/10.1007/s11235-014-9890-7
 |
| MANY TECHNOCRATS WANT TO MAKE INTERNET A UNIQUE PLACE, aka NEW WORLD ORDER I say, No way, Jose! |
Relay selection for secure 5G green communications
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
Adaptive SON and Cognitive Smart LPN for 5G Heterogeneous
Networks
Mobile Networks and Applications
December 2015, Volume 20, Issue 6, pp 745–755
Volume 2017 (2017), Article ID 3680671, 11 pages
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
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
This link has a huge list of scholarly articles on the 5G grid,
internet science
 |
| DO THEY JUST WANT TO DUMB-DOWN THE POPULATION? |
Professor of Telecommunications & Mobile Technology,
Sussex University
6G, auction theory, bio-communications, brain-mobile
interface
Verified email at www.sussex.ac.uk - Homepage
Browse Journals & Magazines > IEEE Communications
Magazine > Volume: 52 Issue: 4
Smart grid technologies for future radio and data center
networks
Scenarios for 5G mobile and wireless communications: the
vision of the METIS project
Cognitive Radio Enabled Wireless Sensor Networks and
Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published
January 1, 2015 Editorial
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
Review Article
Survey of Promising Technologies for 5G Networks
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
Research Article
5G: Vision and Requirements for Mobile Communication System
towards Year 2020
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
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
Cognitive Radio Enabled Wireless Sensor Networks and
Survivability Challenges
Shamik Sengupta, Walid Saad, Abhishek Roy First Published
January 1, 2015 Editorial
Mobile Information Systems
Volume 2016 (2016), Article ID 2676589, 25 pages
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
Chinese Journal of Engineering
Volume 2016 (2016), Article ID 5974586, 8 pages
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
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
International Journal of Antennas and Propagation
Volume 2016 (2016), Article ID 7202143, 10 pages
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
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
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
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.
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%
MDPI
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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
loading...
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.
 |
| JOHN OLIVER, AKA JOLLIVER - who loves to keep rabbiting on..... |
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:
 |
| Giving Internet "the finger" |
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
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.
Energies 10 00909 g002 550
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.
Energies 10 00909 g003 550
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.
Energies 10 00909 g004 550
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.
Energies 10 00909 g005 550
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.
Energies 10 00909 g006 550
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.
Energies 10 00909 g007 550
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.
Energies 10 00909 g008 550
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.
Energies 10 00909 g009 550
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....
10/10/2018 FOR ALL WHO READ THIS:
Mark Zuckerberg is not Jewish!
Just perform a DNA test!
His real name is Robert T. Morris, who obviously destroyed tens of thousands of computers in the USA in 1989!
My Opinions Are My Own
That was a CIA job!
They obviously gave him a new identity!
He attended Harvard University
He got his PhD from Cornell
He changed his image to cover up for his crime!
I don't care WHO asks - a crime is a crime!
Consider how many deaths resulted from the Zuckerberg crimes!
Morris's ancester may have been the one who created Morris code, changed to Morse code
To protect his identity
Used for the first time when Titanic (or the like, since it may not have been the Titanic that sank and they saved the brand new ship and frauded insurance and got rich off it, and also had money to start the Federal Reserve, which most everyone in the REAL society considers to be nefarious)
REQUEST TO USA:
PLEASE INVESTIGATE! AND KEEP THE TRAITORS FROM ABUSING POWER TO PROTECT MARK ZUCKERBERG AND ALL THE OTHERS!
THANK YOU!
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