Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO Systems Via Distributive Stochastic Learning
In this paper, the authors propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the Global Queue State Information (GQSI) only and computed at the Base Station Controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in one cluster are adaptive to both intra-Cluster Channel State Information (CCSI) and intra-Cluster Queue State Information (CQSI), and computed at the Cluster Manager (CM) over a shorter time scale. They show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost Constrained Partially Observed Markov Decision Process (CPOMDP).