Optimal Scheduling Policy Determination for High Speed Downlink Packet Access

Abstract — In this paper, we present an analytic model and methodology to determine optimal scheduling policy that involves two dimension space allocation: time and code, in High Speed Downlink Packet Access (HSDPA) system. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for optimal policy. This framework is used to find the optimal scheduling policy for the case of two users sharing the same cell. Simulation is used to study the performance of the resulted optimal policy using Round Robin (RR) scheduler as a baseline. The policy granularity is introduced to reduce the computational complexity by reducing the action space. The results showed that finer granularity (down to 5 codes) enhances the performance significantly. However, the enhancement gained when using even finer granularity was marginal and does not justify the added complexity. The behaviour of the value function was observed to characterize the optimal scheduling policy. These observations is then used to develop a heuristic scheduling policy. The devised heuristic policy has much less computational complexity which makes it easy to deploy and with only slight reduction in performance compared to the optimal policy according to the simulation results. I

A Markov Decision Process Model for Dynamic Wavelength Allocation in WDM Networks

This paper outlines an optimal dynamic wavelength allocation in all-optical WDM networks. A simple topology consists of a 2-hop path network with three nodes is studied for three classes of traffic where each class corresponds to different source-destination pair. For each class, call interarrival and holding times are exponentially distributed. The objective is to determine a wavelength allocation policy in order to maximize the weighted sum of users of all classes. Consequently, this method is able to provide differentiated services in the network. The problem can be formulated as a Markov Decision Process to compute the optimal resource allocation policy. It has been shown numerically that for two and three classes of users, the optimal policy is of threshold type and monotonic. Simulation results compare the performance of the optimal policy, with that of Complete Sharing and Complete Partitioning policies