AN ONLINE THROUGHPUT MAXIMIZATION ALGORITHM FOR GREEN COORDINATED MULTI-POINT SYSTEMS

Wireless systems are upgraded to use renewable energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a \emph{green} coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green CoMP, the long-term weighted throughput maximization problem is investigated by expecting a \mbox{non-positive} consumption of the long-term on-grid energy. Motivated by the capacity-achieving property and simple implementation, an online zero-forcing dirty paper precoder is proposed to update the precoding matrices by combining statistical learning with the Lyapunov learning. A tradeoff relation is theoretically established to show that the long-term weighted throughput approaches the ${\cal O}(V)$-neighbor of optimal value while the long-term consumed on-grid energy increases at a rate of ${\cal O}(\nicefrac{\log^2(V)}{\sqrt V})$, where $V$ is an introduced control parameter. Numerical results are used to verify the performance of the online zero-forcing dirty paper precoder.