| Summary: | The ever-increasing energy requirement incurred by future dense wireless communication networks has always been a challenging issue. Eliminating the inter-cell interference (ICI) is considered as a key factor for green communication whilst adapting to energy demand variations contributes to the stable cost-efficient operation of the system. This thesis focuses on learning-based energy management and interference control among base stations (BSs) using convex optimization methods in multi-cell networks. The robust distributed coordinated approaches are proposed to solve aggregate transmit power minimization problem constrained by certain signal-to-interference-plus-noise-ratio (SINR) outage probabilities in the presence of imperfect channel state information. The intractable problem is first converted to a tractable form and then decomposed into independent sub-problems to be solved at individual BSs. The individual BSs gradually learn the ICI imposed from other BSs via sub-gradient iterations with a light inter-BS communication overhead. Then, the problem of maximizing the weighted SINR requirements is investigated. The original problem is first converted into an equivalent total transmit power minimization problem for a fixed scale of SINR targets. Then, an upper confidence bound based algorithm is proposed to optimally and distributively scale the SINR targets based on per-BS power budget and coordinate ICI among BSs. Next, a combinatorial multi-armed bandit (CMAB) inspired online learning algorithm is introduced to minimize the time-averaged energy cost at BSs, powered by various energy markets and local renewable energy sources. The algorithm sustains traffic demands by enabling sparse beamforming to schedule dynamic user-to-BS allocation and proactive energy provisioning at BSs to make ahead-of-time price-aware energy management decisions. Finally, in order to address the dynamic statistics of renewable energy supply, an adaptive strategy inspired by CMAB model for energy storage management and cost-aware coordinated load control is proposed. The proposed strategy makes online foresighted energy storage decisions to minimize the average energy cost over long time horizon.
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