Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum Farzad Hessar Sumit Roy University of Washington April 2014 Outline Introduction PrimarySecondary Network Architecture ID: 761339
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Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum Farzad Hessar, Sumit Roy University of Washington April 2014
OutlineIntroductionPrimary/Secondary Network ArchitectureChannel Allocation Formulation Solutions Greedy OptimalNumerical ResultsConclusion/Future Works 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 2
IntroductionDynamic 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 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 3
Database Approach DSA 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 4
Primary/Secondary Network Architecture4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 5 Primary Network: Irregular Cells Secondary Network: Regular cells overlaid with primary
Primary Network Irregular Cells Highly Directional Antennas Variation of HAAT Variation of 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 6 HAAT( HAAT( HAAT( ( ( (
FCC TVWS RegulationsPermissible Channels Fixed: {2:51}\{3, 4, 37} Portable: {21:51}\{37} Power LimitsAntenna HeightSeparation Distance (Height-dependent)Co-channel Protection Adj-channel Protection 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 7
TVWS Characteristics4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 8 Irregular Primary Cells FCC Regulations - Spatial variation in No. of available channels - Location dependent channel quality - Spatial variation of channel numbers
TVWS Channel Quality4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 9 Location: Seattle, University of Washington From: http://specobs.ee.washington.edu
Problem DefinitionBasic Question: How do we assign resources (channels) to secondary users in TVWS? Why is it important? Why not setup as WiFi network?Secondary network in TVWS are managed by DBA.4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 10 Regular Cellular Networks Same set of channels are available every where No quality difference among channels Main goal is to color the graph based on number of users. 23 30 47 18 35 23 30 47 29 30 47 50 23 30 47 18 35 18 23 50 23 30 47 29 30 35 50
Channel Allocation in TVWSSome Definitions All permissible channels: Available channels at cell : For specify as the interference level. It includes co/adjacent channel pollution from primary. A minimum of one channels must be assigned to each cell 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 11
Formulate Channel AllocationProblem formulation 1: For a set of N cells , with channel set a channel selection function is desired so that: Subject to: 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 12
Problem Formulation 1 Pros and Cons for problem formulation-1 Threshold must be optimally found Maximizing total number of channels does not necessarily maximizes capacity Objective function and Constraints are linear Standard solver tools exist 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 13
Formulate Channel AllocationProblem formulation 2: For a set of N cells , with channel set a channel selection function is desired so that: Subject to: 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 14
Problem Formulation 2Pros 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 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 15
SolutionsSuboptimal Greedy Algorithm for Problem Definition-1 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 16
Greedy Solution – Problem 14/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 17
Greedy – Problem 1, cntd. 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 18
Optimal Solution – Problem-1 Channel availability vector Channel assignment vector 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 19
Optimal Solution – Problem-1Integer Linear Programming: Subject to: 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 20
NLIP Solution – Problem 2Non-linear IP: Subject to: 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 21
Greedy Solution – Problem 2 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 22
Greedy Solution – Problem 24/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 23
Numerical ResultsScenario 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 24
Numerical Results ctd. 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 25
Numerical Results ctd.4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 26 ~13% loss
Numerical Results ctd.Problem 1 vs. Problem 2 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 27
ConclusionResource allocation in secondary cellular networksMain issues in TVWS spectrum Variation in number of channels Variation in channel quality Problem FormulationMaximize number of allocated channels IPMaximize aggregate channel capacity NLIPSolutions Problem-1 Greedy / Optimal (complexity exponential) Problem-2 Greedy / Optimal (work in progress) 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 28
Future WorksOptimal solution to problem-2Used 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 server4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 29
ReferencesF. Hessar, S. Roy, Cloud Based Simulation Engine for TVWS. [Online]. Available : http ://specobs.ee.washington.eduS. 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. 4/2/2014 Resource Allocation Techniques for Cellular Networks in TV White Space Spectrum 30