Two-way discriminatory channel estimation for non-reciprocal wireless MIMO channels

2011 ◽  
Author(s):  
Chao-Wei Huang ◽  
Tsung-Hui Chang ◽  
Xiangyun Zhou ◽  
Y.-W. Peter Hong
Keyword(s):  
Channel Estimation ◽  
Mimo Channels

Iterative channel estimation and data detection in frequency-selective fading MIMO channels

2004 ◽  
Vol 15 (5) ◽  
pp. 459-470 ◽  
Author(s):  
Maja Lončar ◽  
Ralf R. Müller ◽  
Joachim Wehinger ◽  
Christoph F. Mecklenbräuker ◽  
Tetsushi Abe
Keyword(s):  
Channel Estimation ◽  
Data Detection ◽  
Selective Fading ◽  
Mimo Channels ◽  
Frequency Selective Fading ◽  
Frequency Selective

Sparse Channel Estimation for Spatial Non-Stationary Massive MIMO Channels

2020 ◽  
Vol 24 (3) ◽  
pp. 681-684 ◽  
Author(s):  
Shuai Hou ◽  
Yafeng Wang ◽  
Tianyi Zeng ◽  
Sheng Wu
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Channels ◽  
Sparse Channel Estimation ◽  
Sparse Channel

scholarly journals Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Athar Waseem ◽  
Aqdas Naveed ◽  
Sardar Ali ◽  
Muhammad Arshad ◽  
Haris Anis ◽  
...  
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Sampling Theorem ◽  
Base Station ◽  
Mimo Channels ◽  
Pilot Design

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

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