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Location-aware channel estimation enhanced TDD based massive MIMO

Location-aware channel estimation enhanced TDD based massive MIMO
Location-aware channel estimation enhanced TDD based massive MIMO
Pilot contamination (PC) is a stumbling block in of realizing massive multi-input multi-output (MIMO) systems. This contribution proposes a location-aware channel estimation-enhanced massive MIMO system employing time-division duplexing protocol, which is capable of significantly reducing the inter-cell interference caused by PC and, therefore, improving the achievable system performance. Specifically, we present a novel location-aware channel estimation algorithm, which utilizes the property of the steering vector to carry out a fast Fourier transform-based post-processing after the conventional pilot-aided channel estimation for mitigating PC. Our asymptotic analysis proves that this post-processing is capable of removing PC from the interfering users with different angle-of-arrivals (AOAs). Since in practice the AOAs of some users may be similar, we further present a location-aware pilot assignment method to ensure that users utilizing the same pilot have distinguishable AOAs, in order to fully benefit from the location-aware channel estimation. Simulation results demonstrate that the proposed scheme can dramatically reduce the inter-cell interference caused by the re-use of the pilot sequence and improve the overall system performance significantly, while only imposing a modest extra computational cost, in comparison with the conventional pilot-aided channel estimation.
7828-7840
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Zhao, Peiyao
7f47a3da-a60f-4f6e-bd33-a680a6104041
Qian, Chen
2b85263f-c5ae-413e-a0d3-4c3abd84ff61
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, Zhaocheng
70339538-3970-4094-bcfc-1b5111dfd8b4
Zhao, Peiyao
7f47a3da-a60f-4f6e-bd33-a680a6104041
Qian, Chen
2b85263f-c5ae-413e-a0d3-4c3abd84ff61
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Wang, Zhaocheng, Zhao, Peiyao, Qian, Chen and Chen, Sheng (2016) Location-aware channel estimation enhanced TDD based massive MIMO. IEEE Access, 4, 7828-7840. (doi:10.1109/ACCESS.2016.2625306).

Record type: Article

Abstract

Pilot contamination (PC) is a stumbling block in of realizing massive multi-input multi-output (MIMO) systems. This contribution proposes a location-aware channel estimation-enhanced massive MIMO system employing time-division duplexing protocol, which is capable of significantly reducing the inter-cell interference caused by PC and, therefore, improving the achievable system performance. Specifically, we present a novel location-aware channel estimation algorithm, which utilizes the property of the steering vector to carry out a fast Fourier transform-based post-processing after the conventional pilot-aided channel estimation for mitigating PC. Our asymptotic analysis proves that this post-processing is capable of removing PC from the interfering users with different angle-of-arrivals (AOAs). Since in practice the AOAs of some users may be similar, we further present a location-aware pilot assignment method to ensure that users utilizing the same pilot have distinguishable AOAs, in order to fully benefit from the location-aware channel estimation. Simulation results demonstrate that the proposed scheme can dramatically reduce the inter-cell interference caused by the re-use of the pilot sequence and improve the overall system performance significantly, while only imposing a modest extra computational cost, in comparison with the conventional pilot-aided channel estimation.

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More information

Accepted/In Press date: 20 October 2016
e-pub ahead of print date: 7 November 2016
Published date: 26 November 2016
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 404250
URI: http://eprints.soton.ac.uk/id/eprint/404250
PURE UUID: bb5d9f73-c52c-419d-8f6a-37d6d890aa79

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Date deposited: 05 Jan 2017 10:05
Last modified: 06 Oct 2020 19:34

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Contributors

Author: Zhaocheng Wang
Author: Peiyao Zhao
Author: Chen Qian
Author: Sheng Chen

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