Compressive sensing and prior support based adaptive channel estimation in massive MIMO

2016 ◽  
Author(s):  
Haifen Yang ◽  
Yutao Fan ◽  
Dong Liu ◽  
Zhi Zheng ◽  
Shuisheng Lin
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Adaptive Channel Estimation

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 Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

2017 ◽  
Vol 67 (6) ◽  
pp. 668
Author(s):  
Qingzhu Wang ◽  
Mengying Wei ◽  
Yihai Zhu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Data Representation ◽  
Frequency Division ◽  
Frequency Division Duplexing

<p class="p1">To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms.</p>

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.

scholarly journals Structured Compressive Sensing-Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO

2016 ◽  
Vol 64 (2) ◽  
pp. 601-617 ◽  
Author(s):  
Zhen Gao ◽  
Linglong Dai ◽  
Wei Dai ◽  
Byonghyo Shim ◽  
Zhaocheng Wang
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Spatio Temporal ◽  
Joint Channel

scholarly journals Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

2015 ◽  
Vol 63 (23) ◽  
pp. 6169-6183 ◽  
Author(s):  
Zhen Gao ◽  
Linglong Dai ◽  
Zhaocheng Wang ◽  
Sheng Chen
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Adaptive Channel Estimation
Sign in / Sign up

Export Citation Format

Share Document