Sparse Detection for Spatial Modulation in Multiple Access Channels

Tu, Yuliang; Gui, Lin; Qin, Qibo; Hong, Wen

doi:10.1109/iscc.2018.8538700

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…In the SBL detector, the conditional distribution in (3) can be maximized by taking the advantage of Bayes' theorem and calculating the marginal distribution over the parameters 𝝀 ∶,n as in (23). 16 ( X∶,n , λ∶,n…”

Section: Sparse Bayesian Learningmentioning

confidence: 99%

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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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Section: Sparse Bayesian Learningmentioning

confidence: 99%

“…Nonetheless, the parameters of this distribution should be estimated. These parameters are calculated in (24) and (25), utilizing the Bayes' theorem as 16…”

Section: Sparse Bayesian Learningmentioning

confidence: 99%