An optimization framework for scheduling and resource allocation in multi-stream heterogeneous MIMO-OFDMA wireless networks

Channel/queue-aware scheduling and resource allocation algorithms in MIMO-OFDMA can be regarded as a multi-objective optimization problem taking into account not only the system throughput but also the transmitted power, the QoS constraints, the priority levels of different traffic classes and the amount of backlogged data in the data link control (DLC) layer queues. Based on tools from information and queueing theories, convex optimization and stochastic approximation, a unified framework for channel- and queue-aware QoS guaranteed scheduling and resource allocation for heterogeneous multi-service OFDMA wireless networks is proposed in this paper. This framework builds upon results presented in [1] (and references therein), which themselves are rooted on dual decomposition optimization and stochastic approximation techniques whose associated complexities are linear in the number of resource units and users. Furthermore, they achieve negligible duality gaps in numerical simulations based on current standards-like scenarios, thus guaranteeing the optimality of the solutions. This work generalizes previous results presented by these authors by leveraging the assumptions that all users were assumed to have the same number of receive antennas and, moreover, only one information stream per user was allowed irrespective of the number of antennas at the transmitter/receiver.