Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM

Xinhua Lu, Carles Navarro Manchón, Zhongyong Wang

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
21 Downloads (Pure)

Abstract

Massive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing channel estimation in the uplink. Utilizing the sparse channel structure is a promising approach to improve the channel estimation performance while circumventing such problems. In this paper, we investigate the detailed physical structure in the delay-spatial domain of uplink channels in massive MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems and construct a hierarchical probabilistic model based on Dirichlet process (DP) prior to match the channel's structural sparse features. Based on the model, we derive a structured sparse channel estimation algorithm by implementing collapsed variational Bayesian inference (CVBI). The simulation results demonstrate that the proposed CVBI-DP algorithm can improve channel estimation performance significantly compared with the state-of-the-art methods for massive MIMO-OFDM, without increasing the computational complexity and pilot overhead.
Original languageEnglish
Article number8629904
JournalIEEE Access
Volume7
Pages (from-to)16665-16674
Number of pages10
ISSN2169-3536
DOIs
Publication statusPublished - 30 Jan 2019

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Channel estimation
Orthogonal frequency division multiplexing
Computational complexity
Base stations
Communication systems
Antennas

Keywords

  • Dirichlet process
  • Massive MIMO
  • collapsed variational Bayesian inference
  • structured sparse channel

Cite this

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abstract = "Massive multiple input multiple output (MIMO) technology significantly improves the capacity of wireless communication systems by deploying hundreds of antennas at the base station. However, the large scale of the array implies higher computational complexity and pilot overhead when implementing channel estimation in the uplink. Utilizing the sparse channel structure is a promising approach to improve the channel estimation performance while circumventing such problems. In this paper, we investigate the detailed physical structure in the delay-spatial domain of uplink channels in massive MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) systems and construct a hierarchical probabilistic model based on Dirichlet process (DP) prior to match the channel's structural sparse features. Based on the model, we derive a structured sparse channel estimation algorithm by implementing collapsed variational Bayesian inference (CVBI). The simulation results demonstrate that the proposed CVBI-DP algorithm can improve channel estimation performance significantly compared with the state-of-the-art methods for massive MIMO-OFDM, without increasing the computational complexity and pilot overhead.",
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Collapsed VBI-DP Based Structured Sparse Channel Estimation Algorithm for Massive MIMO-OFDM. / Lu, Xinhua; Manchón, Carles Navarro; Wang, Zhongyong.

In: IEEE Access, Vol. 7, 8629904, 30.01.2019, p. 16665-16674.

Research output: Contribution to journalJournal articleResearchpeer-review

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