Decentralized Beamforming Design for Intelligent Reflecting Surface-Enhanced Cell-Free Networks

Abstract

Cell-free networks are considered as a promising distributed network architecture to satisfy the increasing number of users and high rate expectations in beyond-5G systems. However, to further enhance network capacity, an increasing number of high-cost base stations (BSs) is required. To address this problem and inspired by the cost-effective intelligent reflecting surface (IRS) technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks. We first transform the centralized weighted sum-rate maximization problem into a tractable consensus optimization problem, and then an incremental alternating direction method of multipliers (ADMM) algorithm is proposed to locally update the beamformer. The complexity and convergence of the proposed method are analyzed, and these results show that the performance of the new scheme can asymptotically approach that of the centralized one as the number of iterations increases. Results also show that IRSs can significantly increase the system sum-rate of cell-free networks and the proposed method outperforms existing decentralized methods.Index Terms-Beamforming, cell-free networks, intelligent reflecting surface, decentralized optimization. I. INTRODUCTIONRecently, a user-centric network paradigm called cellfree networks has been considered as a promising technique to provide high network capacity and overcome the cellboundary effect of traditional network-centric networks (e.g., cellular networks) [1]-[4]. In cell-free networks, a large number of distributed service antennas, which are connected to central processing units (CPUs), coherently serve all users on the same time-frequency resource [2]. This distributed communication network can offer many degrees of freedom and high multiplexing gain. Recent results show that cellfree networks outperform traditional cellular and small-cell networks in several practical scenarios [2], [3]. To provide high directional gains, beamforming design is important in cell-free networks. To cooperatively design beamforming, a centralized zero-forcing (ZF) beamforming scheme is proposed in [5]. Since the CPU should collect all instantaneous channel state information (CSI) of all base stations (BSs), centralized approaches might be unsalable when the number of BSs and users (UEs) is large and the beamforming optimization at the CPU may be overwhelming due to the high dimensionality of aggregated beamformers. To avoid instantaneous CSI exchange among BSs via backhauling and reduce

Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge

“…Cell-free networks have recently been proposed to serve a high number of users in a cellular network. A key enabler of this technology is the beamforming technique [197,198]. Here, CR may exploit a new dimension for its opportunistic transmission in addition to the time-frequency dimension: the spatial dimension.…”

Power Optimization for Aerial Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO-Based Wireless Sensor Network

“…ere have been only a few studies on the application of IRSs in CF massive MIMO networks, mainly focusing on the rate optimization problem [25][26][27][28], while the problem of power minimization has not been addressed. In [13,22], the problem of power optimization for single-IRS-assisted single-cell communication was studied.…”

Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming

“…Unlike the existing multi-antenna devices, IRS is a passive device that does not perform active signal transmitting. This passive trait of IRS has spurred considerable research interests over the past few years in adapting the traditional methods of active beamforming, covering a variety of system models ranging from point-to-point communication [3]- [6] to downlink cellular network [7]- [19], uplink cellular network [20], [21], interference channel [22], and wiretap channel [23]. While the prior studies in this area mostly assume that the phase shift can be chosen arbitrarily for every reflective element, there is a growing trend toward practical beamforming that restricts the choice for phase shift to a set of discrete values [24]- [33].…”

“…In the realm of continuous IRS beamforming, two standard optimization tools-semidefinite relaxation (SDR) [35] and fractional programming (FP) [36], [37]-have been brought to the fore to address the nonvexity of the IRS beamforming problem. For instance, [7], [12], [14], [15], [19], [20], [38] suggest using SDR because of the quadratic programming form of the IRS beamforming problem, while [5], [11], [13], [17], [18], [21], [23], [23], [34] invoke FP to deal with the fractional function of the signal-to-interference-plus-noise ratio (SINR). Moreover, [22] develops a unified Riemannian conjugate gradient algorithm to enable interference alignment in an IRS-assisted system, [10], [16] cope with the nonconvex beamforming problem via successive convex approximation, [6] relies on the minorization-maximization (MM) to address the hardware impairments in the IRS beamforming problem, and [3], [19] suggest a decentralized beamforming scheme based on alternating direction method of multipliers (ADMM).…”