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Stochastic resource allocation in wireless networks

University of British Columbia

This thesis presents several efficient and adaptive resource allocation schemes in wireless networks under the framework of Markov Decision Problem (MDP). In particular, we formulate meaningful trade-offs for three specific resource allocation problems as MDPs and show that their solutions exhibit certain special structures. In each case, by utilizing the underlying structure, we present a resource allocation solution that is computationally inexpensive and is scalable in terms of the system parameters. First, we present opportunistic algorithms in scheduling High Speed Downlink Packet Access (HSDPA) users that exploit channel and buffer variations to increase the probability of uninterrupted media play-out. We formulate a feasibility problem with stability and robustness Quality-of-Service (QoS) constraints. A methodology for obtaining a feasible solution is proposed by starting with a stable algorithm that satisfies the stability QoS constraints. Next, we present optimal adaptive modulation and coding policies that minimize the transmission latency and modulation/coding switching cost across finite-state Markovian fading channels. The optimal tradeoff between the transmission delay and the switching costs is formulated as a discounted cost infinite horizon MDP. We show that under certain sufficient conditions optimal modulation and coding selection policies are monotone in the state variables. Finally, we present an ARQ-based power and retransmission control policy that achieves an optimal tradeoff between transmission power, delay, and packet drop penalty costs. Under certain sufficient conditions, we show that the optimal power and retransmission control policies are monotone in the channel quality, the penalty cost, and the number of the retransmission slots left.

Text

2011-02-15

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http://hdl.handle.net/2429/31303

Electrical and Computer Engineering

University of British Columbia

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