Enhanced channel estimation for W-CDMA forward link using pilot symbols over Rayleigh-fading multipath channels

2002 ◽  
Author(s):  
Je Gil Koo ◽  
Seok Jun Ko ◽  
Hyung Jin Choi
Keyword(s):  
Channel Estimation ◽  
Rayleigh Fading ◽  
Multipath Channels ◽  
Forward Link ◽  
Pilot Symbols

Multi-branch DFE for Rayleigh fading multipath channels

1997 ◽  
Vol 33 (18) ◽  
pp. 1537
Author(s):  
D.J. Brooks ◽  
J.A. Chambers
Keyword(s):  
Rayleigh Fading ◽  
Multipath Channels

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.

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