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Spectrum Sharing in Cognitive Radio Spectrum Sharing in Cognitive Radio NetworksNetworks

Neil TangNeil Tang3/23/20093/23/2009

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OutlineOutline References

A Cognitive Radio Network

System Model

Problem Definition

Proposed Algorithms

Simulation Results

Conclusions

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ReferencesReferences J. Tang, S. Misra and G. Xue, Joint spectrum allocation and schedulin

g for fair spectrum sharing in cognitive radio wireless networks, Computer Networks, Vol. 52, No. 11, 2008, pp. 2148-2158.

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A Cognitive Radio NetworkA Cognitive Radio Network

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AssumptionsAssumptions A user refers to a transmitter-receiver pair.

The channels available to each user are known in advance.

A user can dynamically access a channel to deliver its packets, but can only work on one of the available channels at one time.

Half-duplex, unicast communications and no collisions.

A scheduling-based MAC layer.

A spectrum server controlling the spectrum allocation and scheduling.

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Interference ModelInterference ModelPrimary Interference

A B C

A B C

A B C

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Interference ModelInterference ModelProtocol Model: C(a) = C(b) and (d(A,D) RI or d(C,B) RI)

A B

C D

a

b

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Interference ModelInterference ModelPhysical Model

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Problem DefinitionProblem Definition A user-channel pair (i, j) A iff channel j is available to user i. The tot

al number of user-channel pairs is bounded by N*C.

A traffic demand vector d = [d1, d2, … , dN], specifying the traffic demand of each user.

A transmission mode is composed of a subset of user-channel pairs which can be active concurrently. Whether concurrent transmissions are allowed or not can be determined based on the interference models.

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Problem DefinitionProblem Definition A transmission mode can be used in one timeslot. We wish to find a tr

ansmission schedule vector p=[p1,p2, …, pT], where pt is the fraction of time that transmission mode t is activated.

Suppose that all possible transmission modes are given. The scheduling problem is to determine the frame length L and the number of active time slots pt*L of each transmission mode in one frame.

A rate allocation vector r = [r1, r2, … , rN] and a corresponding DSF vector = [1, 2, …, N] = [r1/d1, r2/d2, … , rN/dN].

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Problem DefinitionProblem Definition All problems seeks a feasible rate allocation vector r, all transmission

modes along with a feasible transmission schedule vector

The objective of the MAximum throughput Spectrum allocation and Scheduling (MASS) problem is maximizing the network throughput

The objective of the Max-min MAximum throughput Spectrum allocation and Scheduling (MMASS) problem is maximizing the network throughput under the condition min DSF is maximum among all feasible rate allocation vectors.

The objective of the Proportional fAir Spectrum allocation and Scheduling (PASS) problem is maximizing the utility function ∑log(i)

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Multi-Channel Contention Graph (MCCG)

A transmission mode based on protocol interference model corresponds to a Maximal Independent Set (MIS) in MCCG.

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Proposed AlgorithmsProposed Algorithms Find all transmission modes (optimal) based on MCCG or

a good subset of transmission modes (heuristic).

Formulate LPs or CP to solve the defined problems.

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Compute Transmission Modes for Protocol ModelCompute Transmission Modes for Protocol Model

Compute all MISs in MCCG: existing algorithms

Compute a subset of MISs: - Start from a node, keep adding other nodes until no more can be added. Then we obtain one MIS. - Go through every node. - Repeat such procedure q times. - Adding criteria in each step: w(v) = (dπ(v)cv)/(X[v] + 1))

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LP for MASSLP for MASS

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LPs for MMASSLPs for MMASS

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CP for PASSCP for PASS

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Compute Transmission Modes for Physical ModelCompute Transmission Modes for Physical Model

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Simulation Results – Protocol Model Simulation Results – Protocol Model

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Simulation Results – Physical ModelSimulation Results – Physical Model

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Simulation Results Simulation Results

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ConclusionsConclusions Our numerical results have shown that the performance given by our

heuristic algorithms is very close to that of the optimal solutions.

A good tradeoff between throughput and fairness can be achieved by our PASS algorithms.