scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

DFT-Based Low-Complexity Channel Estimation Method for Millimeter-Wave MIMO Systems

2019 ◽  
Vol 107 (1) ◽  
pp. 205-216 ◽  
Author(s):  
Xinrong Ye ◽  
Gan Zheng ◽  
Aiqing Zhang ◽  
Li You ◽  
Xiqi Gao
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mimo Systems ◽  
Estimation Method ◽  
Low Complexity

scholarly journals Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 301
Author(s):  
Jianhe Du ◽  
Jiaqi Li ◽  
Jing He ◽  
Yalin Guan ◽  
Heyun Lin
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Estimation Algorithm ◽  
Step Length ◽  
Low Complexity ◽  
Base Station ◽  
Estimation Accuracy ◽  
Input Multiple Output

For multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the precise acquisition of channel state information (CSI) is a huge challenge. With the increase of the number of antennas at the base station (BS), the traditional channel estimation techniques encounter the problems of pilot training overhead and computational complexity increasing dramatically. In this paper, we develop a step-length optimization-based joint iterative scheme for multi-user mmWave massive MIMO systems to improve channel estimation performance. The proposed estimation algorithm provides the BS with full knowledge of all channel parameters involved in up- and down-links. Compared with existing algorithms, the proposed algorithm has higher channel estimation accuracy with low complexity. Moreover, the proposed scheme performs well even with a small number of training sequences and a large number of users. Simulation results are shown to demonstrate the performance of the proposed channel estimation algorithm.

scholarly journals WSDSBL Method for Wideband Channel Estimation in Millimeter-Wave MIMO Systems with Lens Antenna Array

2020 ◽  
pp. 826-832
Author(s):  
Jicheng Dong ◽  
◽  
Wei Zhang ◽  
Bowen Yang ◽  
Xihong Sang
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Communication Systems ◽  
Bayesian Learning ◽  
Mimo Systems ◽  
Estimation Accuracy ◽  
Sparse Bayesian Learning ◽  
Low Snr ◽  
Estimation Scheme ◽  
Wideband Channel

In millimeter wave (mmWave) communication systems, Channel State Information(CSI) is extremely essential for beamforming. The traditional Successive Support Detection (SSD) algorithm can achieve high wideband channel estimation accuracy, but it only used least square (LS) algorithm to recover the detected channel part, the estimation accuracy is low under low SNR regions. To tackle this problem, in this paper, inspired by the classic Support Detection (SD) channel estimation scheme in narrowband, we propose an efficient Wideband Support Detection Sparse Bayesian Learning (WSDSBL) channel estimation scheme. For every subcarrier, we first detect the support of the wideband beamspace channel of the subcarrier, then we use the Sparse Bayesian Learning (SBL) scheme to recover it. Simulation results show that the proposed WSDSBL channel estimation algorithm is better than conventional wideband channel estimation schemes in MSE performance and achievable sum-rate performance, especially in low SNR regions.

scholarly journals A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE

Telecom ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 3-17
Author(s):  
Mário Marques da Silva ◽  
Rui Dinis ◽  
João Guerreiro
Keyword(s):  
Channel Estimation ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Data Detection ◽  
Pilot Symbols ◽  
Multiple Input ◽  
Input Multiple Output

5G Communications will support millimeter waves (mm-Wave), alongside the conventional centimeter waves, which will enable much higher throughputs and facilitate the employment of hundreds or thousands of antenna elements, commonly referred to as massive Multiple Input–Multiple Output (MIMO) systems. This article proposes and studies an efficient low complexity receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO (m-MIMO). Superimposed pilots suppress the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, leading to a reduction in spectral efficiency. On the other hand, MIMO systems tend to be associated with an increase of complexity and increase of signal processing, with an exponential increase with the number of transmit and receive antennas. A reduction of complexity is obtained with the use of the two proposed algorithms. These algorithms reduce the complexity but present the disadvantage that they generate a certain level of interference. In this article, we consider an iterative receiver that performs the channel estimation using superimposed pilots and data detection, while mitigating the interference associated with the proposed algorithms, leading to a performance very close to that obtained with conventional pilots, but without the corresponding loss in the spectral efficiency.

scholarly journals Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4760
Author(s):  
Shuai Hou ◽  
Yafeng Wang ◽  
Chao Li
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Message Passing ◽  
Massive Mimo ◽  
Communication Systems ◽  
Bayesian Learning ◽  
Mimo Systems ◽  
Complex Signal ◽  
Hybrid Architecture ◽  
Sparse Channel

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

A Low-Complexity Sparse Channel Estimation Method for OFDM Systems

2010 ◽  
Vol E93-B (8) ◽  
pp. 2211-2214
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU ◽  
Lan CHEN
Keyword(s):  
Channel Estimation ◽  
Estimation Method ◽  
Low Complexity ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel
Sign in / Sign up

Export Citation Format

Share Document