Sparse Beamforming for an Ultradensely Distributed Antenna System With Interlaced Clustering

Abstract

Recently, a novel network architecture for a distributed antenna system with interlaced clustering has been proposed to mitigate the cell-edge problem. Under this network architecture, we propose a more practical implementation for ultradensely deployed remote antenna units (RAUs) with large numbers of users. Furthermore, we focus on the user selection (USC) and sparse beamforming technologies to optimize the weighted sum rate (WSR) with both backhaul and power constraints. First, we divide each cluster pattern (CP) into several adaptive cells, where RAUs are connected to a central processor via finitecapacity backhaul links. Aiming at reducing the computational complexity for large numbers of users and RAUs, we solve the original problem with two steps. In the first stage, we propose an efficient USC algorithm to find the largest user subset that satisfies the quality of service requirement. In the second stage, we adopt a CP-based weighted sum minimum mean square error algorithm to optimize the WSR problem for the selected users in the previous stage. Moreover, two decomposition algorithms, named primal decomposition and dual decomposition, are exploited to further reduce the computational complexity. Furthermore, based on the adaptive cells, we provide a low-complexity alternating optimization method for sparse beamforming. Finally, simulation results show that the proposed algorithms can achieve a significant performance gain on edge-user rates without losing much performance gain. At the same time, the backhaul information exchange is largely reduced, and approximately 90% of the RAUs consumes less than 66.7% of backhaul for each RAU. INDEX TERMS Distributed antenna system, sparse beamforming, interlaced clustering, CP-based weighted minimum mean square error (CP-WMMSE), user selection (USC), primal decomposition and dual decomposition.

Asymptotic performance of reconfigurable intelligent surface assisted MIMO communication for large systems using random matrix theory

“…Firstly, we compare the proposed algorithm with the traditional beamforming method such as WMMSE algorithm [26,31] and BCD method [24]. The WMMSE algorithm [26,31] is named as WMMSE+RCG.…”

“…Firstly, we compare the proposed algorithm with the traditional beamforming method such as WMMSE algorithm [26, 31] and BCD method [24]. The WMMSE algorithm [26, 31] is named as WMMSE+RCG. As shown in Figure 2, with the increase of the BS equipped antennas $$M$$, the gap of the upper bound between the proposed RMT algorithm and traditional algorithms diminishes, which corroborates the claim that the proposed method with deterministic approximation based on RMT can asymptotically approach the performance of method with direct matrix inversion for a sufficient large number of antennas.…”

“…To solve problem P 0 , when 𝜽 is fixed, the stationary active beamforming solution is designed according to the WMMSE algorithm in traditional method [26,27]. The Lagrangian formulation of Equation ( 8) is…”

“…To solve problem $${{\rm {P}}^{0}}$$, when $${\bm {\theta }}$$ is fixed, the stationary active beamforming solution is designed according to the WMMSE algorithm in traditional method [26, 27]. The Lagrangian formulation of Equation () is $$$$\begin{equation} {{\mathcal {L}}_{\mathcal {R}}} = \sum \limits _{k = 1}^K {{\left({{\kappa _k}{E_k} - \log {\kappa _k}} \right)}} + \lambda {\left({\sum \limits _{k = 1}^K {{{{\bf w}}_k}{{\bf w}}_k^{\rm {H}} - {P_{\rm {T}}}} } \right)} \end{equation}$$$$$${E_k}$$ is the mean squared error, and $${{{\bf w}}_k}$$ can be computed with the parameters $${\kappa _k}$$ and $${\chi _k}$$ in an alternating fashion as follows: …”