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 us-

ing 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 sim-

ulated 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 frame-

work 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 consecu-

tive 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

z-value (the result of runs test)

Wast

ed

tim

e u

nit

(by c

olli

sion

)

Length of payload definitely affects collision rate

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

EXAMPLES

6

INTEGER LINEAR PRO-GRAMMING

• 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 execu-

tion 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 de-

cision problem

• Write a first draft