Energy-Efficient Design for Reconfigurable Intelligent Surface Aided Cell-Free Ultra Dense HetNets

In this article, we investigate the energy efficiency (EE) of reconfigurable intelligent surfaces (RISs) aided cell-free ultra-dense HetNets (CFUDN). To maximize the EE of CFUDN, users (UEs) association and clustering, RISs subsurface (SSF) associations are carefully designed. Then, the phase shift matrix of RISs and transmission power of base stations (BSs) are jointly optimized. Due to the non-convexity and high complexity of the formulated problem, this jointly optimized problem is usually difficult to solve. At present, the most commonly adopted method to deal with joint optimization problems is the block coordinate descent (BCD) algorithm based on an alternative optimization framework. However, as we all know, the BCD algorithm has some degree of performance loss due to the alternate optimization of variables. To overcome this challenging issue, a novel joint optimization framework based on Riemannian product manifolds (RPM) is proposed, in which the phase shift matrix of RISs and transmission power of BSs can be simultaneously optimized rather than alternating ways. The simulation results and computational complexity analysis demonstrate that the joint optimization framework based on the RPM exhibits superior performance and lower complexity compared to the joint optimization algorithm based on the BCD.

I. Introduction

According to the prediction of ITU, from 2020 to 2030, the global Internet protocol data traffic will increase by 55% annually and eventually reach 5016 EB, and the data rate will expand to 1 Tb/s [1], [2]. In addition to supporting very high data rates, future sixth-generation (6G) wireless networks will also provide various other heterogeneous services, including sensing, positioning, low latency, and ultra-reliable communication [3]. Due to its natural advantages (including huge data throughput, ultra-low latency, ultra-high reliability, high EE, and ubiquitous and uniform coverage [4]), cell-free massive MIMO (CF-MMIMO) has been regarded as a crucial and core technology for the 6G networks. The fundamental idea of CF-MMIMO is to deploy a large number of distributed access points (APs) that are connected to a central processing unit (CPU) to serve all users in a wide area.