The complexity of Markov decision processes
Mathematics of Operations Research
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Convex Optimization
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Performance Analysis on Adaptive Modulation-based BLAST Systems with Queuing Model
Wireless Personal Communications: An International Journal
Queuing with adaptive modulation and coding over wireless links: cross-Layer analysis and design
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
POMDP-Based Coding Rate Adaptation for Type-I Hybrid ARQ Systems over Fading Channels with Memory
IEEE Transactions on Wireless Communications
Two dimensional cross-layer optimization for packet transmission over fading channel
IEEE Transactions on Wireless Communications
Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels
IEEE Transactions on Information Theory
Cross-layer design: a survey and the road ahead
IEEE Communications Magazine
Cross-layer-based modeling for quality of service guarantees in mobile wireless networks
IEEE Communications Magazine
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In this paper, for packet transmission over flat fading channel in single-input-single-output system, we consider the power control problem in a cross-layer design where adaptive modulation is adopted at physical layer to improve spectral efficiency and the queues are modeled as of finite length at data link layer. The goal is to identify the optimal queuing-aware power allocation algorithm to minimize the overall system packet error rate under the constraint of long-term transmit power. One crucial step which we call `inner' problem is to find the optimal power vector at a given target packet error rate at physical layer. Rather than attack the multi-dimensional optimization problem directly using conventional methods, we first observe that the `inner' problem is closely related to an average reward Markov decision process problem, and relax the former to the latter so as to take advantage of its equivalence with linear program which allows efficient solution. Since randomness in the associated Markov decision process is only slight, at most mild, we propose an approximately deterministic policy as suboptimal solution to the `inner' problem with insignificant performance degradation. We also propose two-parameter power allocation functions to achieve suboptimal results with low complexity. The impacts of system parameters on the overall system performance are also evaluated. The accuracy of the numerical result is verified by Monte Carlo simulations.