Download Resource Allocation Techniques for Cellular Networks in TV

Resource Allocation Techniques
for Cellular Networks in TV
White Space Spectrum
Farzad Hessar, Sumit Roy
University of Washington
April 2014
Outline

Introduction

Primary/Secondary Network Architecture

Channel Allocation Formulation

Solutions

Greedy

Optimal

Numerical Results

Conclusion/Future Works
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Introduction


Dynamic Spectrum Access (DSA)

Database Approach

Spectrum Sensing Approach
Database Approach Requirements

Known Primary Users (PU)

Sharing PU Technical Details

Slow Variation of PU Specification

Practical Case: TV White Space Spectrum
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Database Approach DSA
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Primary/Secondary Network Architecture

Primary Network: Irregular Cells

Secondary Network: Regular cells overlaid with primary
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Primary Network Irregular Cells

Highly Directional Antennas
0
1
30
330
0.8

0.6
Variation of HAAT
60 )
𝚫𝑯(𝜽
𝟎
HAAT(𝜽𝟎 )
300
0.4
0.2

Variation of Δ𝐻
270
𝚫𝑯(𝜽𝟏 )
HAAT(𝜽𝟏 )
90
120
240
HAAT(𝜽
150 𝟐 )
210
180
Pattern
TV Broadcaster Antenna
𝚫𝑯(𝜽
𝟐)
Channel: 5, CallSign:K05KY
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FCC TVWS Regulations

Permissible Channels

Fixed: {2:51}\{3, 4, 37}

Portable: {21:51}\{37}

Power Limits

Antenna Height

Separation Distance (Height-dependent)

Co-channel Protection

Adj-channel Protection
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TVWS Characteristics
Irregular
Primary Cells
FCC
Regulations
- Spatial variation in No. of available
channels
- Location dependent channel quality
- Spatial variation of channel numbers
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TVWS Channel Quality

Location: Seattle, University of Washington

From: http://specobs.ee.washington.edu
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Problem Definition


Basic Question: How do we assign resources (channels)
23
to secondary users in TVWS?
30
47
18
Why is it important?
Why not setup as WiFi network?


23
50
Secondary
18
network in TVWS
are managed by DBA.
35
Regular Cellular Networks

Same set of channels are available
23
every where
30
47 among
difference
29
30
35
47
50

No quality
channels

Main goal is to color the graph based on
number of users.
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Channel Allocation in TVWS
Some Definitions

All permissible channels: 𝐶 = 2,3, … , 36,38, … , 51

Available channels at cell 𝐴𝑖 : Υ 𝐴𝑖 ⊆ 𝐶

For 𝑐 ∈ Υ 𝐴𝑖 specify 𝛾𝑖,𝑃 (𝑐) as the interference level. It
includes co/adjacent channel pollution from primary.

A minimum of one channels must be assigned to each
cell
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Formulate Channel Allocation

Problem formulation 1: For a set of N cells
{𝐴0 , … , 𝐴𝑁−1 }, with channel set Υ 𝐴0 , … , Υ 𝐴𝑁−1 a
channel selection function 𝑓 is desired 𝑓: Υ 𝐴𝑖 → 𝐶𝑖 ⊆
Υ 𝐴𝑖 so that:
Subject to:
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Problem Formulation 1

Pros and Cons for problem formulation-1
 Threshold 𝛾𝑡 must be optimally found
 Maximizing total number of channels does not necessarily
maximizes capacity
 Objective function and Constraints are linear
 Standard solver tools exist
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Formulate Channel Allocation

Problem formulation 2: For a set of N cells
{𝐴0 , … , 𝐴𝑁−1 }, with channel set {Υ 𝐴0 , … , Υ 𝐴𝑁−1 } a
channel selection function 𝑓 is desired 𝑓: Υ 𝐴𝑖 → 𝐶𝑖 ∈
Υ 𝐴𝑖 so that:

Subject to:
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Problem Formulation 2

Pros and Cons for Problem formulation-2
 No threshold selection is required
 Maximizing capacity is guaranteed
 Objective function is nonlinear
 Standard solver tools do not exist
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Solutions

Suboptimal Greedy Algorithm for Problem Definition-1
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Greedy Solution – Problem 1
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Greedy – Problem 1, cntd.
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Optimal Solution – Problem-1

Channel availability vector 𝐴𝑖𝐶 ×1 ∈ 0,1
𝐶 ×1

Channel assignment vector ℒ𝑖𝐶 ×1 ∈ 0,1
𝐶 ×1
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Optimal Solution – Problem-1

Integer Linear Programming:
Subject to:
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NLIP Solution – Problem 2

Non-linear IP:
Subject to:
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Greedy Solution – Problem 2
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Greedy Solution – Problem 2
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Numerical Results

Scenario
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Numerical Results ctd.
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Numerical Results ctd.
~13% loss
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Numerical Results ctd.

Problem 1 vs. Problem 2
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Conclusion

Resource allocation in secondary cellular networks

Main issues in TVWS spectrum



Variation in number of channels

Variation in channel quality
Problem Formulation

Maximize number of allocated channels  IP

Maximize aggregate channel capacity  NLIP
Solutions

Problem-1 Greedy / Optimal (complexity exponential)

Problem-2 Greedy / Optimal (work in progress)
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Future Works

Optimal solution to problem-2


Used for benchmarking other solutions
Integration of resource allocation with SpecObs

Real-time user data collection including channel quality
measurements

Real-time channel assignment in DBA server
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References

F. Hessar, S. Roy, Cloud Based Simulation Engine for TVWS. [Online]. Available:
http://specobs.ee.washington.edu

S. Im and H. Lee, “Dynamic spectrum allocation based on binary integer
programming under interference graph,” in Personal Indoor and Mobile Radio
Communications (PIMRC), 2012 IEEE 23rd International Symposium on, 2012,
pp. 226–231.

L. Cao, L. Yang, X. Zhou, Z. Zhang, and H. Zheng, “Optimus: SINR driven
spectrum distribution via constraint transformation,” in New Frontiers in
Dynamic Spectrum, 2010 IEEE Symposium on, 2010, pp. 1–12.

A. Subramanian, M. Al-Ayyoub, H. Gupta, S. Das, and M. Buddhikot, “Nearoptimal dynamic spectrum allocation in cellular networks,” in New Frontiers
in Dynamic Spectrum Access Networks, 2008. DySPAN 2008. 3rd IEEE
Symposium on, 2008, pp. 1–11.

D. Li and J. Gross, “Distributed TV Spectrum Allocation for Cognitive Cellular
Network under Game Theoretical Framework,” in Proc. IEEE International
Symposium on Dynamic Spectrum Access Networks DYSPAN’12, 2012, pp. 327–
338.

F. Hessar and S. Roy, “Capacity Considerations for Secondary Networks in TV
White Space,” University of Washington, Tech. Rep., 2012.
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