Learning to be green: robust energy efficiency maximization in dynamic MIMO-OFDM systems

In this paper, we examine the maximization of energy efficiency (EE) in next-generation multi-user MIMO-OFDM networks that evolve dynamically over time - e.g. due to user mobility, fluctuations in the wireless medium, modulations in the users' load, etc. Contrary to the static/stationary regime, the system may evolve in an arbitrary manner so, targeting a fixed optimum state (either static or in the mean) becomes obsolete; instead, users must adjust to changes in the system "on the fly", without being able to predict the state of the system in advance. To tackle these issues, we propose a simple and distributed online optimization policy that leads to no regret, i.e. it allows users to match (and typically outperform) even the best fixed transmit policy in hindsight, irrespective of how the system varies with time. Moreover, to account for the scarcity of perfect channel state information (CSI) in massive MIMO systems, we also study the algorithm's robustness in the presence of measurement errors and observation noise. Importantly, the proposed policy retains its no-regret properties under very mild assumptions on the error statistics and, on average, it enjoys the same performance guarantees as in the noiseless, deterministic case. Our analysis is supplemented by extensive numerical simulations which show that, in realistic network environments, users track their individually optimum transmit profile even under rapidly changing channel conditions, achieving gains of up to 600% in energy efficiency over uniform power allocation policies.