Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks - PowerPoint PPT Presentation

Loading...

PPT – Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks PowerPoint presentation | free to download - id: 6abe1f-YmIwY



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks

Description:

HANDBOOK ON GREEN INFORMATION AND COMMUNICATION SYSTEMS Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks – PowerPoint PPT presentation

Number of Views:44
Avg rating:3.0/5.0
Slides: 35
Provided by: HaoL8
Learn more at: http://bbcr.uwaterloo.ca
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Constrained Green Base Station Deployment with Resource Allocation in Wireless Networks


1
Constrained Green Base Station Deployment with
Resource Allocation in Wireless Networks
HANDBOOK ON GREEN INFORMATION AND COMMUNICATION
SYSTEMS
1Zhongming Zheng, 1Shibo He, 2Lin X. Cai, and
1Xuemin (Sherman) Shen 1Department of Electrical
and Computer Engineering University of
Waterloo 2School of Engineering and Applied
Science Princeton University
2
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

3
Introduction
  • Energy Sources
  • Renewable Energy
  • Repeatedly replenished
  • Examples hydropower, biomass
  • Non-renewable Energy
  • Once depleted, no more available
  • Examples coal, natural gas

4
Introduction
  • Green Energy
  • Eco-friendly renewable energy
  • Example wind, solar

5
Introduction
  • Green Wireless Communication Networks
  • WLAN mesh network structure

6
Introduction
  • Projects
  • EARTH
  • Energy Aware
  • Radio and neTwork
  • tecHnologies
  • PERANET
  • GREENRADIO

7
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

8
Literature Review
  • Device Design
  • PV systems
  • 1 Probabilistic methods
  • 2 Simulation model
  • Energy charging and discharging models
  • 3 Battery/energy buffer
  • 4 Power consumption model of BSs

1 H. A. M. Maghraby, M. H. Shwehdi, and G. K.
Al-Bassam, Probabilistic assessment of
photovoltaic (pv) generation systems, Power
Systems, IEEE Transactions on, vol. 17, no. 1,
pp. 205208, Feb. 2002. 2 E. Lorenzo and L.
Navarte, On the usefulness of stand-alone pv
sizing methods, Progress in Photovoltaics
Research and Applications, vol. 8, no. 4, pp.
391409, Aug. 2000. 3 L. X. Cai, Y. Liu, H. T.
Luan, X. Shen, J. W. Mark, and H. V. Poor,
Adaptive resource management in sustainable
energy powered wireless mesh networks, in IEEE
Globecom, Houston, TX, USA, Dec. 5-9 2011, pp.
15. 4 O. Arnold, F. Richter, G. Fettweis, and
O. Blume, Power consumption modeling of
different base station types in heterogeneous
cellular networks, in Future Network Mobile
Summit, Florence, IT, Jun. 16-18 2010, pp. 18.
9
Literature Review
  • Minimal Device Deployment
  • Continuous Case
  • Direct search
  • 5 Quasi-Newton methods
  • Discrete Case
  • 6 Sustainability
  • 7 Outage free

5 G. L. Z. Wei and L. Qi, New quasi-newton
methods for unconstrained optimization problems,
Applied Mathematics and Computation, vol. 175,
no. 2, pp. 11561188, Apr. 2006. 6 Z. Zheng, L.
X. Cai, M. Dong, X. Shen, and H. V. Poor,
Constrained energyaware ap placement with rate
adaptation in wlan mesh networks, in IEEE
GLOBECOM, Houston, TX, USA, Dec. 5-9 2011, pp.
15. 7 S. A. Shariatmadari, A. A. Sayegh, and
T. D. Todd, Energy aware basestation placement
in solar powered sensor networks, in IEEE WCNC,
Sydney, AUS, Apr. 18-21 2010, pp. 16.
10
Literature Review
  • Resource Allocation
  • Scheme Design
  • 8 Traffic scheduling
  • 9 Admission control and routing
  • 10 Power control

8 A. A. Hammad, G. H. Badawy, T. D. Todd, A. A.
Sayegh, and D. Zhao, Traffic scheduling for
energy sustainable vehicular infrastructure, in
IEEE GLOBECOM, Miami, FL, USA, Dec. 6-10 2010,
pp. 16. 9 L. Lin, N. B. Shroff, and R.
Srikant, Asymptotically optimal energy-aware
routing for multihop wireless networks with
renewable energy sources, Networking, IEEE/ACM
Transactions on, vol. 15, no. 5, pp. 10211034,
Oct. 2007. 10 A. Farbod and T. D. Todd,
Resource allocation and outage control for
solarpowered wlan mesh networks, Mobile
Computing, IEEE Transactions on, vol. 6, no. 8,
pp. 960970, Aug. 2007.
11
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

12
System Model
  • Given a set of BSs, users and candidate locations
  • All users are associated with a BS
  • BSs are powered by renewable energy
  • BSs and users may have different power levels of
    charging and transmission
  • In a WLAN, BS and its associated users use the
    same transmission power

13
System Model
  • No inter-WLAN interference with orthogonal
    channels assigned to BSs for inter-WLAN
    communication
  • BSs can only be placed at a given set of
    candidate locations
  • BSs at different candidate locations have
    different charging capabilities

14
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

15
Problem Formulation
The number of deployed BSs
Full coverage Each user is associated with only
one BS
Achieved throughput Traffic demand Harvested
energy Consumed energy
16
Problem Formulation
  • Initialization
  • Output

17
Problem Formulation
  • Problem Analysis
  • Minimal BS placement problem with power
    allocation
  • NP-hard problem
  • Sub-problems are NP-hard
  • Optimal placement of BSs with a fixed power
  • Power allocation of BSs

18
Problem Formulation
  • Algorithm Design Strategy
  • NP-hard ? No solution in polynomial time
  • Design an effective heuristic algorithm
  • Achieve good performance
  • Reduce the time complexity

19
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

20
TCGBP Algorithm
  • First Phase
  • Partition the whole network region into several
    VPs (Voronoi Polygons)
  • Place one BS in each candidate location
  • Connect users to the BS in the same VP region

21
TCGBP Algorithm
  • First Phase

22
TCGBP Algorithm
  • Second Phase
  • Connect BSs and users in neighboring VP regions
    until constraints can not be held
  • Return the result when all users are connected

23
TCGBP Algorithm
  • Second Phase

24
TCGBP Algorithm
  • Phase
    II
  • Phase I

25
TCGBP Algorithm
  •  

26
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

27
Numerical Results
  • Simulation Configurations

Parameter Value
WLAN mesh networks 100 m 100 m
Transmission power levels 10 dBm, 15 dBm, 20 dBm
Charging capability 20, 30 mW per slot
Time duration 1000 slots
Channel bandwidth 40 MHz
Path loss exponent 4
Background noise -20 dBm
28
Numerical Results
  • Different numbers of users and traffic demands

29
Numerical Results
  • Different numbers of candidate locations and
    charging capabilities

30
Outline
  • Introduction
  • Literature Review
  • System Model
  • Problem Formulation
  • TCGBP Algorithm
  • Numerical Results
  • Conclusion Future Work

31
Conclusion
  • Green energy sources
  • Formulate an optimal green BS placement problem
  • Propose TCGBP algorithm
  • Approach the optimal solution with significantly
    reduced time complexity

32
Future Work
  • Study the impacts of dynamics in the energy
    charging and discharging process
  • Analyze the network capacity bounds under
    different deployment strategies

33
(No Transcript)
34
(No Transcript)
About PowerShow.com