Reduced-complexity SVD with adjustable accuracy for precoding in large-scale MIMO systems

Tsai, Pei–Yun; Liu, Chin-Yi

doi:10.1109/sips.2015.7345023

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Therefore, the computational complexity of the Algorithm 3 is dominated by the SVD operation in each iteration which is O N RF 3 . It should be noted that there are numerous algorithms to reduce the computation complexity of SVD such as the techniques in [57], [58].…”

Section: B Hybrid Combiner Designmentioning

confidence: 99%

Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

Mirzaei,

ShahbazPanahi,

Sohrabi

et al. 2021

Preprint

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In this paper, we study the channel estimation problem in correlated massive multiple-input-multiple-output (MIMO) systems with a reduced number of radio-frequency (RF) chains. Importantly, other than the knowledge of channel correlation matrices, we make no assumption as to the structure of the channel. To address the limitation in the number of RF chains, we employ hybrid beamforming, comprising a low dimensional digital beamformer followed by an analog beamformer implemented using phase shifters. Since there is no dedicated RF chain per transmitter/receiver antenna, the conventional channel estimation techniques for fully-digital systems are impractical. By exploiting the fact that the channel entries are uncorrelated in its eigen-domain, we seek to estimate the channel entries in this domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Under a total energy budget, we aim to design the hybrid transmit beamformer (precoder) and the receive beamformer (combiner) in each training time slot, in order to estimate the channel using the minimum mean squared error criterion. To this end, we choose the precoder and combiner in each time slot such that they are aligned to transmitter and receiver eigendirections, respectively. Further, we derive a water-filling-type expression for the optimal energy allocation at each time slot. This expression illustrates that, with a low training energy budget, only significant components of the channel need to be estimated. In contrast, with a large training energy budget, the energy is almost equally distributed among all eigen-directions. Simulation results show that the proposed channel estimation scheme can efficiently estimate correlated massive MIMO channels within a few training time slots. TABLE I SUMMARY OF RELATED WORKS Frequency Channel Model Assumption Estimation Technique Reference CS-based [15], [21]-[24], [27], [29], [30] mmWave Parameteric Sparsity Beam training followed by CS-based resolution refining [28] ESPRIT [25] MUSIC [31] PASTd [32], [33] Microwave and mmWave Kronecker Known Channel Correlations MMSE [34], [35] training approach, while a subsequent CS algorithm refines the resolution. As an alternative to adaptive CS algorithms, traditional random CS approaches exploiting pseudo-random weights are used in a phased array MIMO system with a hybrid structure [24], [29]. The authors of [23] apply random CS with simpler analog beamformers, where the network of switches is replaced with the phase-shifter network 1 . In [30], the CSbased channel estimation algorithm extended to a mmWave communication system equipped with one-bit analog-to-digital converters (ADCs) and HB. The authors of [25], [31]-[33] propose non-CS-based techniques to estimate the channel in massive MIMO systems with the hybrid precoder/combiner structure. Assuming the parametric channel model, the authors in [31] exploit twodimensional beamspace multiple signal classification (MU-SIC) to estimate the path directions followed by a leasts...

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Section: B Hybrid Combiner Designmentioning

confidence: 99%

Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

Mirzaei,

ShahbazPanahi,

Sohrabi

et al. 2021

Preprint

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“…The simulation result shows iterative algorithm performs better than conventional detection with lower amount of iterations. Communications beamforming method to increase the capacity with optimum proper power allocation was proposed by [5] [6]. We use the two-level algorithm to decompose the multi-user MIMO channel into multiple single-user channels, which contains block diagonalization and SVD.Pilot contamination and interference are well-known problems affecting communication system, requiring the need of an equalizer.…”

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confidence: 99%

A Novel Iterative Detection in Downlink of Massive MIMO System

Sun¹,

Wang²,

Wang³

et al. 2019

JCC

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In this paper, we propose the novel iterative detection which operates iteratively on blocks of the received signal in downlink in massive MIMO (Massive Multiple-input Multiple-output) system. This article will combine the equivalent channel with soft detection and soft decoder, and finally propose a new structure for the iterative detection in downlink based on 5G simulation test platform of NCRL and analysis the performance of the novel structure. The simulation result shows iterative algorithm performs better than conventional detection with lower amount of iterations. Communications beamforming method to increase the capacity with optimum proper power allocation was proposed by [5] [6]. We use the two-level algorithm to decompose the multi-user MIMO channel into multiple single-user channels, which contains block diagonalization and SVD.Pilot contamination and interference are well-known problems affecting communication system, requiring the need of an equalizer. Various solutions to the problem were widely investigated. The optimal, iterative equalizer applying soft feedback is LMMSE ISDIC equalizer. Iterative channel estimation and data detection is researched in [7] [8]. We consider time division duplexing since it can exploit channel reciprocity [9]. An innovated double-turbo receiver was proposed to alleviate the pilot contamination and interference, which combines the equivalent channel estimation with the detector and the decoder.The paper is organized as follows. In Section 2, we propose the system model, including precoding algorithm, detection algorithm and so on. Section 3 introduces the novel detection structure and compares the proposed structure and the traditional one. Some numerical results with a performance comparison are shown in Section 4. Last, Section 5 draws the conclusions on the proposed schemes.

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Performance analysis of linear precoders and SVD in downlink MassiveMIMO Frequency selective channels

Goli

Chakka

2016

2016 International Conference on Signal Processing and Communication (ICSC)

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Reduced-complexity SVD with adjustable accuracy for precoding in large-scale MIMO systems

Cited by 6 publications

References 14 publications

Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

A Novel Iterative Detection in Downlink of Massive MIMO System

Performance analysis of linear precoders and SVD in downlink MassiveMIMO Frequency selective channels

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