Dynamic scheduling for wireless multicast in massive MIMO HetNet

Consider a massive multiple input and multiple output (MIMO) heterogeneous network (HetNet) scenario where the macro base station is coupled with low-power nodes (LPNs) to provide wireless multicast transmission to user equipments (UEs) within a single macro cell. The Markov decision process (MDP) model is adopted to model the dynamic scheduling problem introduced by the mobility of UEs. With asymptotic conclusion for the massive MIMO multicast transmission, we propose a MDP-based off-line scheduling problem with determined system parameters and derive a linear programming (LP) model to solve the problem. As for the system with undetermined parameters, we adopt Q-learning algorithm to solve the optimal policy. To derive the low-complexity algorithm, the state aggregation method is utilized to build an series of sub-problems to estimate the original complex MDP problem. Finally we use the MDP Toolbox in MATLAB to run simulations to test the proposed algorithm. The performance of the proposed algorithm is shown and analyzed in details.