Optimal resource management for smart grid powered multi-input multi-output (MIMO) systems is of great importance for future green wireless communications. A novel framework is put forth to account for the stochastic renewable energy sources (RES), dynamic energy prices, as well as random wireless channels. Based on practical models, the resource allocation task is formulated as an optimization problem that aims at maximizing the weighted sum-rate of the MIMO broadcast channels. A two-way transaction mechanism and storage units are introduced to accommodate the RES variability. In addition to system operating constraints, a budget threshold is imposed on the worst-case energy transaction cost due to the possibly adversarial nature. Capitalizing on the uplink-downlink duality and the Lagrangian relaxation-based subgradient method, an efficient algorithm is developed to obtain the optimal strategy. Generalizations to the setups of time-varying channels and ON–OFF transmissions are also discussed. Numerical results are provided to corroborate the merits of the novel approaches.
['Shuyan Hu', 'Yu Zhang', 'Xin Wang', 'Georgios B. Giannakis']
Weighted Sum-Rate Maximization for MIMO Downlink Systems Powered by Renewables