Transcript 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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 2
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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 3
Database Approach DSA
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Primary/Secondary Network Architecture
Primary Network: Irregular Cells Secondary Network: Regular cells overlaid with primary Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 5
Primary Network Irregular Cells
Highly Directional Antennas Variation of HAAT Variation of Δ𝐻 300 330 270
HAAT(
𝜽 𝟏 ) 𝚫𝑯
(
𝜽 𝟏 ) 0 1 0.8
0.6
0.4
0.2
30 𝚫𝑯
(
𝜽 𝟎 )
HAAT(
𝜽 𝟎 ) 90 240 210
HAAT(
𝜽 𝟐 ) 180 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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 7
TVWS Characteristics
Irregular Primary Cells FCC Regulations - Spatial variation in No. of available channels - Location dependent channel quality - Spatial variation of channel numbers Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 8
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)
to secondary users in TVWS?
23 30 47
Why is it important? Why not setup as WiFi network?
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Secondary network in TVWS are managed by DBA.
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Regular Cellular Networks Same set of channels are available every where
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No quality difference among channels
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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 𝐴 𝑖 : Υ 𝐴 𝑖 ⊆ 𝐶 For 𝑐 ∈ Υ 𝐴 𝑖 specify 𝛾 𝑖,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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 11
Formulate Channel Allocation
Problem formulation 1: For a set of N cells {𝐴 0 , … , 𝐴 𝑁−1 } , with channel set channel selection function 𝑓 Υ 𝐴 0 , … , Υ 𝐴 𝑁−1 is desired 𝑓: Υ 𝐴 𝑖 → 𝐶 𝑖 a Υ 𝐴 𝑖 so that: ⊆ Subject to: Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 12
Problem Formulation 1
Pros and Cons for problem formulation-1 Threshold 𝛾 𝑡 must be optimally found Maximizing total number of channels does not necessarily maximizes capacity Objective function and Constraints are linear Standard solver tools exist Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 13
Formulate Channel Allocation
Problem formulation 2: For a set of N cells {𝐴 0 , … , 𝐴 𝑁−1 } , with channel set channel selection function 𝑓 {Υ 𝐴 0 , … , Υ 𝐴 𝑁−1 is desired 𝑓: Υ 𝐴 𝑖 } → 𝐶 𝑖 a Υ 𝐴 𝑖 so that: ∈ Subject to: Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 14
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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 15
Solutions
Suboptimal Greedy Algorithm for Problem Definition-1 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 16
Greedy Solution – Problem 1
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Greedy – Problem 1, cntd.
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Optimal Solution – Problem-1
Channel availability vector 𝐴 𝑖 𝐶 ×1 ∈ 0,1 𝐶 ×1 Channel assignment vector ℒ 𝑖 𝐶 ×1 ∈ 0,1 𝐶 ×1 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 19
Optimal Solution – Problem-1
Integer Linear Programming: Subject to: Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 20
NLIP Solution – Problem 2
Non-linear IP: Subject to: Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 21
Greedy Solution – Problem 2
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Greedy Solution – Problem 2
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Numerical Results
Scenario Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 24
Numerical Results ctd.
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Numerical Results ctd.
~13% loss Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 26
Numerical Results ctd.
Problem 1 vs. Problem 2 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 27
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) Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 28
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 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4/29/2020 29
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, “Near optimal 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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