TY - GEN
T1 - A Compressive Sensing Based Channel Prediction Scheme with Uneven Pilot Design in Mobile Massive MIMO Systems
AU - Shi, Yi
AU - Jiang, Zhiyuan
AU - Liu, Yan
AU - Wang, Ying
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Massive Multiple Input Multiple Output (MIMO) is a widely used technique that can provide tremendous gain in spectral efficiency. However, the degradation of beamforming performance due to outdated Channel State Information at the Transmitter side (CSIT) induced by mobility of users, both in Time Division Duplex (TDD) and Frequency Division Duplex (FDD) modes, has been a severe problem awaiting to be solved. Many channel prediction schemes have been proposed to address this problem. Most of them focus on the prediction of future CSI on pilot symbols, then reconstruct the non-pilot symbols through conventional interpolation methods. However, the CSI cannot be simply reconstructed through interpolation in high mobility scenario because of the limitation of even pilot design - the phenomenon is named as 'Doppler aliasing' in this paper. To address this, we propose a novel uneven pilot pattern that can provide more Doppler spectrum resolution compared with even pilot which is currently used in most of communication systems. Based on the novel pilot setting, we then propose a channel prediction scheme based on the compressive sensing technique. Numerical results show our scheme can outperform the state-of-the-art algorithms, including deep neural networks and autoregression models, for about 15 percents in terms of average throughput in simulated channel generated by the COST2100 channel model.
AB - Massive Multiple Input Multiple Output (MIMO) is a widely used technique that can provide tremendous gain in spectral efficiency. However, the degradation of beamforming performance due to outdated Channel State Information at the Transmitter side (CSIT) induced by mobility of users, both in Time Division Duplex (TDD) and Frequency Division Duplex (FDD) modes, has been a severe problem awaiting to be solved. Many channel prediction schemes have been proposed to address this problem. Most of them focus on the prediction of future CSI on pilot symbols, then reconstruct the non-pilot symbols through conventional interpolation methods. However, the CSI cannot be simply reconstructed through interpolation in high mobility scenario because of the limitation of even pilot design - the phenomenon is named as 'Doppler aliasing' in this paper. To address this, we propose a novel uneven pilot pattern that can provide more Doppler spectrum resolution compared with even pilot which is currently used in most of communication systems. Based on the novel pilot setting, we then propose a channel prediction scheme based on the compressive sensing technique. Numerical results show our scheme can outperform the state-of-the-art algorithms, including deep neural networks and autoregression models, for about 15 percents in terms of average throughput in simulated channel generated by the COST2100 channel model.
UR - http://www.scopus.com/inward/record.url?scp=85123357894&partnerID=8YFLogxK
U2 - 10.1109/WCSP52459.2021.9613184
DO - 10.1109/WCSP52459.2021.9613184
M3 - Conference Proceeding
AN - SCOPUS:85123357894
T3 - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
BT - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021
Y2 - 20 October 2021 through 22 October 2021
ER -