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Channel Scheduling Scheme in Cognitive Radio. Lee, Gunhee Idea Presentation. References. A Survey on Cognitive Radio Networks Jingfang Huang, Honggang Wang, and Hong Liu University of Massachusetts, Dartmouth Mobilware 2010 A Survey on Spectrum Management in Cognitive Radio Networks
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Channel Scheduling Scheme in Cognitive Radio Lee, Gunhee Idea Presentation
References • A Survey on Cognitive Radio Networks • Jingfang Huang, Honggang Wang, and Hong Liu • University of Massachusetts, Dartmouth • Mobilware 2010 • A Survey on Spectrum Management in Cognitive Radio Networks • Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran, and S. Mohanty • Georgia Institute of Technology • IEEE Communications Magazine, April 2008 • A Typology of Cutting and Packing Problems • Harald Dyckhoff • RWTH Aachen • European Journal of Operational Research, 1990
Scope • Spectrum Decision • Step 1 : characterize each spectrum band • Step 2 : choose the most appropriate spectrum • Previous works • We can gather multi-channel information simultaneously by using cooperative centralized sensing • We can measure a channel’s usefulness by using runs test for randomness on history data Spectrum Sensing Spectrum Decision Spectrum Sharing Spectrum Mobility
Assumptions • There is a control channel between BS and CR nodes • Local nodes have their payloads of variable lengths (to transmit) • Multiple CR channels are present • Base station gathers history data periodically • We only concern the upload from CR nodes to BS • We do not concern communication between CR nodes • Assume that there is a primary user, and his activity can be simulated using Markov Chain
Keypoint • We can divide CR spectrum decision problem into small subproblems • Gathering history data : binary scheme • Analysing history data : runs analysis • Channel scoring : cumulative distribution function • Channel allocation : integer linear programming • By combining these approaches, we can suggest a framework for CR spectrum decision • Channel Scheduling Scheme (CSS) for CR
Runs Analysis • Runs test for randomness counts every element in the array by default (in this case 0 and 1) • However, we should ignore 1s because we are only interested in whitespaces • So we should modify runs test to fit our interests, thus making runs analysis for CR history data • Why runs are important? Because collision is affected by consecutive 0s, not total 0s (example) 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ch 1 Ch 2 Ch 3
Simulation Length of payload definitely affects collision rate Wasted time unit (by collision) z-value (the result of runs test)
Using History Data • We can predict collision rate using histogram and cumulative distribution function (CDF) of history data after runs analysis • On the other hand, assume that there is a required collision rate , we can find the maximum length of payload of CR nodes • For example, in the given (next page) condition of channel, to achieve “collision rate < 40%”, a payload whose “length < 6 time unit” should be allocated to that channel (otherwise it will collide) • So this problem becomes a kind of cutting & packing problem
Examples 6
Integer Linear Programming • Channel allocation problem is an integer linear programming problem • Cutting and Packing (C&P) problem is well known in Operational Research • Channel allocation problem is same as multicore scheduling problem, cutting stock problem, and bin packing problem (same class of logic) • It is a NP-Hard problem, so there are many heuristics such as • First-Fit (FF) • First-Fit-Decreasing (FFD) • Max-Rest (MR), Max-Rest-Priority-Queue (MRPQ) • Next-Fit (NF) • Next-Fit-Decreasing (NFD) • Best-Fit (BF)
Metric • Measure of heuristics • Throughput : number of processes that complete their execution per time unit • Turnaround : total time between submission of a process and its completion • Response time : amount of time it takes from when a request was submitted until the first response is produced • Fairness : equal time to each process • In this paper, we concentrate on maximizing throughput
To Do • Generate 200++ sample channels using MCMC • Score each channel by using CDF • Conduct the simulation • Measure the efficiency of channel allocation heuristics • Suggest an integrated framework to solve spectrum decision problem • Write a first draft