Stochastic user scheduling and power control for energy harvesting networks with statistical delay provisioning

We study the stochastic user scheduling and power control problem for an uplink multi-user network over time-varying channels, where the users randomly harvest renewable energies from the environment. For each user, the renewable energies and arriving data packets with a constant rate are stored in energy (battery) and data buffers, respectively. Users have statistical packet delay constraints in terms of maximum acceptable delay-outage probabilities. We classify the users as prioritized and non-prioritized users. Our goal is to maximize the arrival rate of the non-prioritized user while supporting the minimum data rate requirements for the prioritized users. We reformulate the problem as an infinite-horizon Markov decision process (MDP) using asymptotic delay analysis and study the optimal scheduling and power control policy. Since the optimal policy requires centralized processing with high computational complexity, we develop a reduced-complexity distributed algorithm, which can be implemented at each individual user. Online algorithm is devised, which does not require the statistical knowledge of the channel fading and energy harvesting (EH) processes. Numerical results demonstrate the effectiveness of the centralized and distributed schemes for different delay constraints and EH settings.