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Spatially common sparsity based adaptive channel estimation and feedback for FDD Massive MIMO

Spatially common sparsity based adaptive channel estimation and feedback for FDD Massive MIMO
Spatially common sparsity based adaptive channel estimation and feedback for FDD Massive MIMO
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.
channel estimation, compressive sensing, feedback, frequency division duplex, massive multi-input multi-output, spatially common sparsity, temporal correlation
1053-587X
6169-6183
Gao, Zhen
e0ab17e4-5297-4334-8b64-87924feb7876
Dai, Linglong
a3b9a8e1-777f-4196-a388-969444c7239d
Wang, Zhaocheng
a6bc4037-d26d-4ddd-9ce5-f018980115b6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Gao, Zhen
e0ab17e4-5297-4334-8b64-87924feb7876
Dai, Linglong
a3b9a8e1-777f-4196-a388-969444c7239d
Wang, Zhaocheng
a6bc4037-d26d-4ddd-9ce5-f018980115b6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Gao, Zhen, Dai, Linglong, Wang, Zhaocheng and Chen, Sheng (2015) Spatially common sparsity based adaptive channel estimation and feedback for FDD Massive MIMO. IEEE Transactions on Signal Processing, 63 (23), 6169-6183. (doi:10.1109/TSP.2015.2463260).

Record type: Article

Abstract

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér–Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

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e-pub ahead of print date: 31 July 2015
Published date: 1 December 2015
Keywords: channel estimation, compressive sensing, feedback, frequency division duplex, massive multi-input multi-output, spatially common sparsity, temporal correlation
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 383482
URI: https://eprints.soton.ac.uk/id/eprint/383482
ISSN: 1053-587X
PURE UUID: d6f01cb0-44d7-4da2-a6a0-947dfaa9424d

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Date deposited: 17 Nov 2015 10:41
Last modified: 17 Jul 2017 20:12

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