Energy-Efficient Content Caching Strategy in Cell-Free Massive MIMO Networks with Reinforcement Learning.

To reduce the burden of transmission delay on the fronthaul link, this paper investigates an energy-efficient content caching, access point (AP) clustering and digital-to-analog converter resolution selection strategy in cell-free massive multiple-input multiple-output (CF-mMIMO) network with cache enabled are investigated. Specifically, we first give the signaling transmission model, cache model and power model. Furthermore, we formulated an optimization problem for maximizing the energy-efficiency of the considered system by optimizing the content caching, AP clustering and ADC/DAC resolution. Finally, a reinforcement learning method-a deep deterministic policy gradient algorithm is adopted to acquire the optimal solution.