Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

Huang, Shaocheng; Ye, Yu; Xiao, Ming

doi:10.1109/tccn.2020.3019604

Cited by 23 publications

(15 citation statements)

References 39 publications

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“…Though problem (P4) can be approximatively solved with the SDR technique [9], [15], finding the solution is of high complexity when N R is large (i.e., O((N R+1) 4.5 ) [15]). Based on our previous work [16], the MM method is an effective and low-complexity way to solve the non-convex problem (P4). The basic idea is to transform the original problem (P4) into a sequence of majorized subproblems that can be solved with closed-form minimizers.…”

Section: Decentralized Beamforming Designmentioning

confidence: 99%

“…The basic idea is to transform the original problem (P4) into a sequence of majorized subproblems that can be solved with closed-form minimizers. At first, according to lemma 1 in [16], we can find a valid majorizer of g b (θ b ) at point…”

Section: Decentralized Beamforming Designmentioning

confidence: 99%

“…The proof of convergence for the MM method is similar to [16] and omitted here because of the space limitation. We define the stopping criteria of the MM method as i i = I i , where I i the maximum permitted number of iteration.…”

Section: Decentralized Beamforming Designmentioning

confidence: 99%

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

Huang

Xiao

et al. 2021

IEEE Wireless Commun. Lett.

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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

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Section: Decentralized Beamforming Designmentioning

confidence: 99%

Section: Decentralized Beamforming Designmentioning

confidence: 99%

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

Huang

Xiao

et al. 2021

IEEE Wireless Commun. Lett.

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“…To include the optimal power allocation P in parallel, we consider the normalization of the columns of (49) to unit-norm and multiply the log det(•) and Tr(•) terms in (48) from the right hand side with the power matrix P , ∀ . Power optimization problem for each link can be formally stated similarly as in (31), as a function of the local variables L and L instead of gradients. Solving it yields the following parallel and optimal power allocation scheme…”

Section: A Per-link Independent Sub-problems In Ulmentioning

confidence: 99%

Per-link Parallel and Distributed Hybrid Beamforming for Multi-Cell Massive MIMO Millimeter Wave Full Duplex

Sheemar¹,

Slock²

2022

Preprint

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This paper presents two novel hybrid beamforming (HYBF) designs for a multi-cell massive multiple-input-multiple-output (mMIMO) millimeter wave (mmWave) full duplex (FD) system under limited dynamic range (LDR). Firstly, we present a novel centralized HYBF (C-HYBF) scheme based on alternating optimization. In general, the complexity of C-HYBF schemes scales quadratically as a function of the number of users and cells, which may limit their scalability. Moreover, they require significant communication overhead to transfer complete channel state information (CSI) to the central node every channel coherence time for optimization. The central node also requires very high computational power to jointly optimize many variables for the uplink (UL) and downlink (DL) users in FD systems. To overcome these drawbacks, we propose a very low-complexity and scalable cooperative per-link parallel and distributed (P$\&$D)-HYBF scheme. It allows each mmWave FD base station (BS) to update the beamformers for its users in a distributed fashion and independently in parallel on different computational processors. The complexity of P$\&$D-HYBF scales only linearly as the network size grows, making it desirable for the next generation of large and dense mmWave FD networks. Simulation results show that both designs significantly outperform the fully digital half duplex (HD) system with only a few radio-frequency (RF) chains and achieve similar performance.

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“…In [24], a novel HYBF design for an FD mmWave MIMO relay is proposed. In [25], a machine-learning based HYBF for a oneway mmWave FD relay is investigated. In [26], HYBF for a bidirectional point-to-point OFDM FD system is available.…”

Section: A State-of-the-art On Mmwave Fdmentioning

confidence: 99%

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

Sheemar¹,

Thomas²,

Slock³

2021

Preprint

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Full-Duplex (FD) communication can revolutionize wireless communications as it doubles spectral efficiency and offers numerous other advantages over a half-duplex (HD) system. In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme for millimeter-wave (mmWave) massive MIMO FD system for weighted sum-rate (WSR) maximization with multi-antenna HD uplink and downlink users with non-ideal hardware. Moreover, we present a novel interference and self-interference (SI) aware optimal power allocation scheme for the optimal beamforming directions. The analog processing stage is assumed to be quantized, and both the unit-modulus and unconstrained cases are considered. Moreover, compared to the traditional sum-power constraints, the proposed algorithm is designed under the joint sum-power and the practical per-antenna power constraints. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional limited dynamic range (LDR) noise model to mmWave. Our HYBF design relies on alternating optimization based on the minorization-maximization method. We investigate the maximum achievable gain of a hybrid FD system with different levels of the LDR noise variance and with different numbers of radio-frequency (RF) chains over a HD system. Simulation results show that the mmWave massive MIMO FD systems can significantly outperform the fully digital HD systems with only a few RF chains if the LDR noise generated from the limited number of RF chains available is low. If the LDR noise variance dominates, FD communication with HYBF results to be disadvantageous than a HD system.

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Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

Cited by 23 publications

References 39 publications

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

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

Per-link Parallel and Distributed Hybrid Beamforming for Multi-Cell Massive MIMO Millimeter Wave Full Duplex

Practical Hybrid Beamforming for Millimeter Wave Massive MIMO Full Duplex with Limited Dynamic Range

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