Resource allocation for joint energy and spectral efficiency in cloud radio access network based on deep reinforcement learning

<jats:p>The rapid increase of user data traffic demand has promoted the telecommunication sector toward adopting a new generation, that is, fifth‐generation (5G). Cloud radio access network (CRAN) has gained considerable attention to satisfy the high traffic demand in the 5G network via deployment and intelligent management of multiple remote radio units (RRHs). However, optimizing the instantaneous network performance may lead to myopic decision‐making, such as excessive on/off switching of RRHs. This paper proposes a deep reinforcement learning (DRL) based framework, with the goal of maximizing the long‐term tradeoff between energy efficiency (EE) and spectral efficiency (SE). To this end, we specifically formulate the joint optimization problem as a Markov decision process (MDP) subject to the constraints on per‐RRH transmission power and user quality of service (QoS) demands. Meanwhile, considering the spatio‐temporal channel state information (CSI), we adopt machine learning (ML) techniques to extract generalized features before feeding them into the input of DRL. Combining with RRH status and QoS requirements, the proposed algorithm can learn the near‐optimal control strategy to turn on/off RRHs, followed by solving a power optimization problem. Simulation results reveal that the proposed method yields better performance as compared to myopic and DRL methods without considering CSI generalization.</jats:p>