Channel estimation based on low‐complexity hierarchical codebook design for millimeter‐wave MIMO systems

2021 ◽  
Author(s):  
Baghdad Hadji ◽  
Lamya Fergani ◽  
Mustapha Djeddou
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mimo Systems ◽  
Low Complexity ◽  
Codebook Design

Low-Complexity Downlink Channel Estimation for Millimeter-Wave FDD Massive MIMO Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 1103-1107 ◽  
Author(s):  
Xianda Wu ◽  
Guanghua Yang ◽  
Fen Hou ◽  
Shaodan Ma
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity

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 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.

scholarly journals Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 49738-49749
Author(s):  
Ting Jiang ◽  
Maozhong Song ◽  
Xuejian Zhao ◽  
Xu Liu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems

scholarly journals Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High-Order PARALIND Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ting Jiang ◽  
Maozhong Song ◽  
Xiaorong Zhu ◽  
Xu Liu
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mimo Systems ◽  
Model Simulation ◽  
Path Loss ◽  
Estimation Algorithm ◽  
Multipath Channels ◽  
High Order ◽  
Propagation Path ◽  
Necessary Condition

Channel state information (CSI) is important to improve the performance of wireless transmission. However, the problems of high propagation path loss, multipath, and frequency selective fading make it difficult to obtain the CSI in broadband millimeter-wave (mmWave) system. Based on the inherent multidimensional structure of mmWave multipath channels and the correlation between channel dimensions, mmWave multiple input multiple output (MIMO) channels are modelled as high-order parallel profiles with linear dependence (PARALIND) model in this paper, and a new PARALIND-based channel estimation algorithm is proposed for broadband mmWave system. Due to the structural property of PARALIND model, the proposed algorithm firstly separates the multipath channels of different scatterers by PARALIND decomposition and then estimates the channel parameters from the factor matrices decomposed from the model based on their structures. Meanwhile, the performance of mmWave channel estimation is analysed theoretically. A necessary condition for channel parameter estimation is given based on the uniqueness principle of PARALIND model. Simulation results show that the proposed algorithm performs better than traditional compressive sensing-based channel estimation algorithms.

Sign in / Sign up

Export Citation Format

Share Document