Optimality of threshold policies for transmission scheduling in correlated fading channels
IEEE Transactions on Communications
A constrained MDP approach to dynamic quantizer design for HMM state estimation
IEEE Transactions on Signal Processing
Delay-optimal power and precoder adaptation for multi-stream MIMO systems
IEEE Transactions on Wireless Communications
Redundant data transmission in control/estimation over wireless networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
Optimal adaptive modulation and coding with switching costs
IEEE Transactions on Communications
IEEE Transactions on Signal Processing
Monotonicity of constrained optimal transmission policies in correlated fading channels with ARQ
IEEE Transactions on Signal Processing
On-line learning and optimization for wireless video transmission
IEEE Transactions on Signal Processing
A dynamical games approach to transmission-rate adaptation in multimedia WLAN
IEEE Transactions on Signal Processing
IEEE Transactions on Communications
Redundant data transmission in control/estimation over lossy networks
Automatica (Journal of IFAC)
Hi-index | 35.70 |
This paper addresses the optimal power and rate allocation control in multiple-input multiple-output (MIMO) wireless systems over Markovian fading channels. The problem is posed as an infinite horizon average-cost constrained Markov decision process (CMDP) with the goal of minimizing the average transmission power subject to delay constraints. By using a Lagrangian formulation of the CMDP, we use the concepts of stochastic dominance, submodularity, and multimodularity to prove that the optimal randomized policies are monotone. Three important structural results on the nature of the optimal randomized policies are derived. First, we show that the action space can be exponentially reduced by decomposing the rate allocation problem into bit-loading problem across individual antennas and the total rate allocation based on the current buffer occupancy and channel state. Second, we show that the optimal rate allocation policy is a randomized mixture of two pure policies that are monotonically increasing in the buffer occupancy. Finally, we show that the optimal power allocation is piecewise linear in the delay constraint. These three structural results can be exploited to devise efficient online reinforcement learning algorithms for optimal rate allocation.