Theory of linear and integer programming
Theory of linear and integer programming
Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Stochastic dynamic programming and the control of queueing systems
Stochastic dynamic programming and the control of queueing systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Optimal Transmission Policies for Noisy Channels
Operations Research
ON PARALLEL QUEUING WITH RANDOM SERVER CONNECTIVITY AND ROUTING CONSTRAINTS
Probability in the Engineering and Informational Sciences
Optimal resource scheduling in wireless multiservice systems with random channel connectivity
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Markov-based channel characterization for tractable performance analysis in wireless packet networks
IEEE Transactions on Wireless Communications
Instability of the proportional fair scheduling algorithm for HDR
IEEE Transactions on Wireless Communications
Packet scheduler for mobile Internet services using high speed downlink packet access
IEEE Transactions on Wireless Communications
Optimal Transmission Scheduling in Symmetric Communication Models With Intermittent Connectivity
IEEE Transactions on Information Theory
Cross-layer design for resource allocation in 3G wireless networks and beyond
IEEE Communications Magazine
Technical solutions for the 3G long-term evolution
IEEE Communications Magazine
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We present an analytic model and a methodology to determine the optimal packet scheduling policy in a High-Speed Downlink Packet Access (HSDPA) system. The optimal policy is the one that maximizes cell throughput while maintaining a level of fairness between the users in the cell. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for the optimal scheduling policy. We use a FSMC (Finite State Markov Channel) to model the HSDPA downlink channel. A near-optimal heuristic scheduling policy is developed. Simulation is used to study the performance of the resulting heuristic policy and compare it to the computed optimal policy. The results show that the performance of the heuristic policy is very close to that of the optimal policy. The heuristic policy has much less computational complexity, which makes it easy to deploy, with only slight reduction in performance compared to the optimal policy.