Joint Design of Recommendation and Caching in D2D-assisted Edge Caching Networks

Wireless edge caching can alleviate the transmission pressure and improve the quality of experience (QoE) provided to users, which has been proposed as a promising technique in the sixth generation (6G) communication systems. This paper considers a clustered device-to-device (D2D) caching network, where users having similar request preference are classified into the same cluster. Due to the fact that recommendation policy will have a significant effect on user requests, the joint design of content recommendation and caching is investigated, aiming to enhancing the system caching efficiency (CE). Under the constraints of the recommendation quality, recommendation quantity and cache size, we formulate the CE maximization problem. The original problem is an NP-hard integer programming problem, making it difficult to solve. Therefore, it is partitioned into two sub-problems. The binary variables in the first sub-problem, namely, the recommendation design sub-problem, is relaxed to be continuous, and then the relaxed problem is solved by applying the methods of successive convex approximation and arithmetic geometric mean inequality. The recommendation decisions are recovered following a greedy strategy based principle. The second sub-problem, i.e., the caching design sub-problem, is addressed by using the smooth function to replace the $l_{0}$ norm. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show the obtained CE performance is superior to existing homogeneous recommendation method.