Real-time Resource Management and Energy Trading for Green Cloud-RAN

Student thesis: Doctoral ThesisDoctor of Philosophy


This thesis considers cloud radio access network (C-RAN), where the remote radio heads (RRHs) are equipped with renewable energy resources and can trade energy with the grid. Due to uneven distribution of mobile radio traffic and inherent intermittent nature of renewable energy resources, the RRHs may need real-time energy provisioning to meet the users demands. Given the amount of available energy resources at RRHs, the main contributions of the thesis begin with introducing realtime resource management strategies to the RRHs with a shortage of power budget to select an optimal number of user terminals based on their available energy budget. On the other hand, sparse beamforming strategies introduced in the second part of the thesis account for all RRHs with or without a shortage of power and take consideration of realistic constraints on fronthaul capacity restrictions. The proposed strategies strike an optimum balance among the total power consumption in the fronthaul through adjusting the degree of partial cooperation among RRHs, RRHs total transmit power and the maximum or total spot-market energy cost. A smart energy management strategy based on the combinatorial multi-armed bandit (CMAB) theory for C-RAN, which is powered by a hybrid of grid and renewable energy sources is studied in the last part of the thesis. A combinatorial upper confidence bound (CUCB) algorithm to maximize the overall rewards, earned as a result of minimizing the cost of energy trading at individual RRHs of the C-RAN has been introduced. Adapting to the dynamic wireless channel conditions, the proposed CUCB algorithm associates a set of optimal energy packages, to be purchased from the day-ahead markets, to a set of RRHs to minimize the total cost of energy purchase from the main power grid by dynamically forming super arms. A super arm is formed on the basis of calculating the instantaneous energy demands at the current time slot, learning from the cooperative energy trading at the previous time slots and adjusting the mean rewards of the individual arms.
Date of Award2017
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorMohammad Nakhai (Supervisor) & Fatin Said (Supervisor)

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