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In this paper, we examine the maximization of energy efficiency (EE) innext-generation multi-user MIMO-OFDM networks that evolve dynamically over time- e.g. due to user mobility, fluctuations in the wireless medium, modulationsin the users' load, etc. Contrary to the static/stationary regime, the systemmay evolve in an arbitrary manner so, targeting a fixed optimum state (eitherstatic or in the mean) becomes obsolete; instead, users must adjust to changesin the system "on the fly", without being able to predict the state of thesystem in advance. To tackle these issues, we propose a simple and distributedonline optimization policy that leads to no regret, i.e. it allows users tomatch (and typically outperform) even the best fixed transmit policy inhindsight, irrespective of how the system varies with time. Moreover, toaccount for the scarcity of perfect channel state information (CSI) in massiveMIMO systems, we also study the algorithm's robustness in the presence ofmeasurement errors and observation noise. Importantly, the proposed policyretains its no-regret properties under very mild assumptions on the errorstatistics and, on average, it enjoys the same performance guarantees as in thenoiseless, deterministic case. Our analysis is supplemented by extensivenumerical simulations which show that, in realistic network environments, userstrack their individually optimum transmit profile even under rapidly changingchannel conditions, achieving gains of up to 600% in energy efficiency overuniform power allocation policies.

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