A Semidynamic Bidirectional Clustering Algorithm for Downlink Cell-Free Massive Distributed Antenna System

Abstract

Cell-free massive distributed antenna system (CF-MDAS) can further reduce the access distance between mobile stations (MSs) and remote access points (RAPs), which brings a lower propagation loss and higher multiplexing gain. However, the interference caused by the overlapping coverage areas of distributed RAPs will severely degrade the system performance in terms of the sum-rate. Since that clustering RAPs can mitigate the interference, in this paper, we investigate a novel clustering algorithm for a downlink CF-MDAS with the limited-capacity backhaul. To reduce the backhaul burden and mitigate interference effectively, a semidynamic bidirectional clustering algorithm based on the long-term channel state information (CSI) is proposed, which has a low computational complexity. Simulation results show that the proposed algorithm can efficiently achieve a higher sum-rate than that of the static clustering one, which is close to the curve obtained by dynamic clustering algorithm using the short-term CSI. Furthermore, the proposed algorithm always reveals a significant performance gain regardless of the size of the networks.

An inertial Douglas–Rachford splitting algorithm for nonconvex and nonsmooth problems

“…To solve this problem, some researchers proposed various algorithms by reducing the computation complexity in wireless communication applications, which can improve the performances of Cell-free massive distributed antenna systems, recognition speeds, online allocation efficiency of virtualized network services with different categories of servers, and so on. [8][9][10] Moreover, abundant achievements in compressed sensing, [11][12][13] sparse approximation of signals 14 and image processing, 15,16 etc. also rely on numerical techniques and algorithms for nonconvex problems.…”