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CS541 Advanced Networking 1 Spectrum Sharing in Cognitive Radio Networks Neil Tang 3/23/2009

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CS541 Advanced Networking 2 Outline References A Cognitive Radio Network System Model Problem Definition Proposed Algorithms Simulation Results Conclusions

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CS541 Advanced Networking 3 References J. Tang, S. Misra and G. Xue, Joint spectrum allocation and scheduling for fair spectrum sharing in cognitive radio wireless networks, Computer Networks, Vol. 52, No. 11, 2008, pp. 2148-2158.

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CS541 Advanced Networking 4 A Cognitive Radio Network

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CS541 Advanced Networking 5 Assumptions 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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CS541 Advanced Networking 6 Interference Model Primary Interference ABC ABC ABC

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CS541 Advanced Networking 7 Interference Model Protocol Model: C(a) = C(b) and (d(A,D) R I or d(C,B) R I ) AB CD a b

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CS541 Advanced Networking 8 Interference Model Physical Model

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CS541 Advanced Networking 9 Problem Definition A user-channel pair (i, j) A iff channel j is available to user i. The total number of user-channel pairs is bounded by N*C. A traffic demand vector d = [d1, d2, , d N ], 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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CS541 Advanced Networking 10 Problem Definition A transmission mode can be used in one timeslot. We wish to find a transmission schedule vector p=[p 1,p 2, , p T ], where p t 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 p t *L of each transmission mode in one frame. A rate allocation vector r = [r 1, r 2, , r N ] and a corresponding DSF vector = [ 1, 2, , N ] = [r 1 /d 1, r 2 /d 2, , r N /d N ].

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CS541 Advanced Networking 11 Problem 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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CS541 Advanced Networking 12 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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CS541 Advanced Networking 13 Proposed 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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CS541 Advanced Networking 14 Compute 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) = (d (v) c v )/(X[v] + 1))

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CS541 Advanced Networking 15 LP for MASS

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CS541 Advanced Networking 16 LPs for MMASS

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CS541 Advanced Networking 17 CP for PASS

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CS541 Advanced Networking 18 Compute Transmission Modes for Physical Model

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CS541 Advanced Networking 19 Simulation Results Protocol Model

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CS541 Advanced Networking 20 Simulation Results Physical Model

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CS541 Advanced Networking 21 Simulation Results

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CS541 Advanced Networking 22 Conclusions 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.