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In this paper, we leverage reinforcement learning (RL) and cross-layernetwork coding (CLNC) for efficiently pre-fetching users' contents to the localcaches and delivering these contents to users in a downlink fog-radio accessnetwork (F-RAN) with device-to-device (D2D) communications. In the consideredsystem, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users areequipped with local caches for alleviating traffic burden at the fronthaul,while users' contents can be easily and quickly accommodated. In CLNC, thecoding decisions take users' contents, their rates, and power levels of F-APsand CE-D2D users into account, and RL optimizes caching strategy. Towards thisgoal, a joint content placement and delivery problem is formulated as anoptimization problem with a goal to maximize system sum-rate. For this NP-hardproblem, we first develop an innovative decentralized CLNC coalition formation(CLNC-CF) algorithm to obtain a stable solution for the content deliveryproblem, where F-APs and CE-D2D users utilize CLNC resource allocation. Bytaking the behavior of F-APs and CE-D2D users into account, we then develop amulti-agent RL (MARL) algorithm for optimizing the content placements at bothF-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CFand RL framework can effectively improve the sum-rate by up to 30\%, 60\%, and150\%, respectively, compared to: 1) an optimal uncoded algorithm, 2) astandard rate-aware-NC algorithm, and 3) a benchmark classical NC withnetwork-layer optimization.

A Joint Reinforcement-Learning Enabled Caching and Cross-Layer Network Code for Sum-Rate Maximization in F-RAN with D2D Communications