Mobility-Aware Reconstruction Algorithm for Correlated Nonzero Neighborhood Structured Downlink Channel in Massive MIMO

Kenarsari, Sanaz Rezvani; Naeiny, Mahmoud Ferdosizade

doi:10.1109/access.2018.2808500

IEEE Access

2018

DOI: 10.1109/access.2018.2808500

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Mobility-Aware Reconstruction Algorithm for Correlated Nonzero Neighborhood Structured Downlink Channel in Massive MIMO

Sanaz Rezvani Kenarsari

Mahmoud Ferdosizade Naeiny

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Cited by 3 publications

References 29 publications

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Blind detection for spatial modulation uplink multi‐user massive MIMO communications: A sparse Bayesian learning‐expectation maximization approach

Khanzadeh

Naeiny

2022

Trans Emerging Tel Tech

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Massive spatial modulation (SM) multi‐input multi‐output (MIMO) system is a promising technique in uplink communications for future mobile communication, due to its power and spectral efficiencies. However, these systems, like other MIMO communications, face the challenge of channel estimation. Pilot‐based channel estimation methods result in data rate reduction as well as imposing additional complexity at the receiver side, which is intensified in time‐varying channels. Therefore, blind channel estimation is an alternative way to avoid pilot transmission. Considering a time‐varying channel and taking the advantages of machine learning techniques, blind channel estimation and data detection for SM uplink multi‐user massive MIMO communications is presented in this article. In this regard, a blind multi‐user detection based on the expectation‐maximization (EM) algorithm, called BMU‐EM, is presented first; however, this detector suffers from high computational complexity. In order to mitigate the complexity problem, a blind multi‐user detection based on sparse Bayesian learning and expectation‐maximization, called BMU‐SBEM, is proposed. Simulation results show that the BMU‐SBEM detector performs almost close to the optimum detector where the perfect channel information is available. Furthermore, the computational complexity of the BMU‐SBEM detector increases linearly with the number of users, making it suitable for massive communications in time‐varying channels.

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Blind detection for spatial modulation uplink multi‐user massive MIMO communications: A sparse Bayesian learning‐expectation maximization approach

Khanzadeh

Naeiny

2022

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

Adaptive pilot reuse scheme for nonzero neighborhood structured downlink channel in massive MIMO

Kenarsari

Naeiny

2019

AEU - International Journal of Electronics and Communications

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Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

et al. 2023

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Mobility-Aware Reconstruction Algorithm for Correlated Nonzero Neighborhood Structured Downlink Channel in Massive MIMO

Cited by 3 publications

References 29 publications

Blind detection for spatial modulation uplink multi‐user massive MIMO communications: A sparse Bayesian learning‐expectation maximization approach

Blind detection for spatial modulation uplink multi‐user massive MIMO communications: A sparse Bayesian learning‐expectation maximization approach

Adaptive pilot reuse scheme for nonzero neighborhood structured downlink channel in massive MIMO

Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

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