Hybrid precoding for millimeter wave massive MIMO with analog combining

Zhou, Shaoqing; Xu, Wei; Zhang, Hua; You, Xiaohu

doi:10.1109/wcsp.2017.8171060

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…where l k is the path of the kth sensor with the maximum gain α l k k and θ l k k , 1 ≤ k ≤ K, denotes the corresponding AoA. The digital combiner F BB derived using the equivalent baseband channel matrix can be expressed as F BB = F H RF G. Invoking the asymptotic orthogonality property of mmWave massive MIMO channels [50] leads to…”

Section: A Fusion Rule For C-mimo Based Wsnmentioning

confidence: 99%

Distributed Detection for Centralized and Decentralized Millimeter Wave Massive MIMO Sensor Networks

Chawla

Singh

Patel

et al. 2021

IEEE Trans. Veh. Technol.

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Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system's performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results.

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Section: A Fusion Rule For C-mimo Based Wsnmentioning

confidence: 99%

Distributed Detection for Centralized and Decentralized Millimeter Wave Massive MIMO Sensor Networks

Chawla

Singh

Patel

et al. 2021

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…The sub-6 GHz will continue the spine of future 5G wireless networks and the B5G as well. To achieve a high data rate (up to 20 Gbps), high spectral efficiency (100 bps/Hz), and high mobility (up to 1000 km/h), several technologies are considered as the backbone of the 5G networks, such as the massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) [7,8], the internet of things (IoT) [9], ultra dense networks (UDNs), visible light communications (VLC), the optical wireless communications (OWC), and the device to device (D2D) communications [10]. The IoT connects billions of sensors, devices, and machines to each other, and they exchange data among themselves.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems

et al. 2020

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Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.

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