Hybrid Precoding for Mixture Use of Phase Shifters and Switches in mmWave Massive MIMO

Qi, Chenhao; Liu, Qiang; Yu, Xianghao; Li, Geoffrey Ye

doi:10.1109/tcomm.2022.3173304

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…We thus solve the following convex problem of minorant maximization formulated in (25) for generating φ (κ+1) satisfying (24):…”

Section: B Alternating Optimization In φ φ φmentioning

confidence: 99%

“…The HBF design has been the subject of extensive research [10]- [28], with single user HBF being considered in [12], [15], [22], [24], [26] and multiuser (MU) HBF being considered in [13], [16], [18]- [21], [27], [28]. Due to the computationally challenging unit modulus constraints imposed on each entry of the ABF matrix, all these papers have only developed heuristic procedures, which do not guarantee convergence or predictable performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

Zhu,

Tuan,

Dutkiewicz

et al. 2024

IEEE Trans. Wireless Commun.

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A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convexsolver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

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“…We thus solve the following convex problem of minorant maximization formulated in (25) for generating φ (κ+1) satisfying (24):…”

Section: B Alternating Optimization In φ φ φmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

Zhu,

Tuan,

Dutkiewicz

et al. 2024

IEEE Trans. Wireless Commun.

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show abstract

“…by obstacles due to the short wavelengths [6]. To address these problems, massive multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRSs) have been proposed [7]- [11]. However, deploying IRSs and massive MIMO in mmWave communication systems faces several challenges such as IRS deployment optimization, and joint active and passive beamforming design.…”

mentioning

confidence: 99%

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Zhang

Yang

Chen

et al. 2024

IEEE Trans. Wireless Commun.

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In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS. I. INTRODUCTIONM ILLIMETER wave (mmWave) communications, which utilizes the 30-300 GHz frequency band to achieve multi-gigabit data rates, is a promising technology for emerging and envisioned wireless systems [2]-[5]. However, mmWave suffers from severe path loss and is easily blocked S. Zhang and D. Liu are with the

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Deep Ridge Regression Neural Network-based hybrid precoder and combiner design

Nagapuri,

Penchala,

Vallem

et al. 2024

Multimed Tools Appl

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Hybrid Precoding for Mixture Use of Phase Shifters and Switches in mmWave Massive MIMO

Cited by 6 publications

References 34 publications

Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Deep Ridge Regression Neural Network-based hybrid precoder and combiner design

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