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AI heralds a step-change in the performance and capability of wirelessnetworks and other critical infrastructures. However, it may also causeirreversible environmental damage due to their high energy consumption. Here,we address this challenge in the context of 5G and beyond, where there is acomplexity explosion in radio resource management (RRM). On the one hand, deepreinforcement learning (DRL) provides a powerful tool for scalable optimizationfor high dimensional RRM problems in a dynamic environment. On the other hand,DRL algorithms consume a high amount of energy over time and risk compromisingprogress made in green radio research. This paper reviews and analyzes how toachieve green DRL for RRM via both architecture and algorithm innovations.Architecturally, a cloud based training and distributed decision-making DRLscheme is proposed, where RRM entities can make lightweight deep localdecisions whilst assisted by on-cloud training and updating. On the algorithmlevel, compression approaches are introduced for both deep neural networks andthe underlying Markov Decision Processes, enabling accurate low-dimensionalrepresentations of challenges. To scale learning across geographic areas, aspatial transfer learning scheme is proposed to further promote the learningefficiency of distributed DRL entities by exploiting the traffic demandcorrelations. Together, our proposed architecture and algorithms provide avision for green and on-demand DRL capability.

Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge