An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

Abstract

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm.

Optimal Decoding Order and Power Allocation for Sum Throughput Maximization in Downlink NOMA Systems

“…Similar to the optimal decoding order, the finding of optimal power allocation can also be solved through theoretical analysis [16][17][18][19] or experimental searching [20,21]. In [16], two closed-form optimal power allocation solutions were derived based on the Karush-Kuhn-Tucker (KKT) conditions by fixing the strong user's capacity and the weak user's capacity, respectively.…”

“…Ref. [20] introduced a block-successive upper-bound minimization algorithm to find the globally optimal power allocation. Ref.…”