MIMO Detection With Imperfect Channel State Information Using Expectation Propagation

We investigate a turbo soft detector based on the expectation propagation (EP) algorithm for large-scale multipleinput multiple-output (MIMO) systems. Optimal detection in MIMO systems becomes computationally unfeasible for highorder modulations and/or large number of antennas. In this situation, the linear minimum mean square error (LMMSE) exhibits a low-complexity with a good performance, however far from optimal. To improve the performance, the EP algorithm can be used. In this paper, we review previous EP-based detectors and enhance their estimation in terms of complexity and performance. Specifically, we improve the convergence of the self-iterated EP stage by replacing the uniform prior by a non-uniform one, which better characterizes the information returned by the decoder once the turbo procedure starts. We also review the EP parameters to avoid instabilities when using high-order modulations and to reduce the computational complexity. Simulation results illustrate the robustness and enhanced performance of this novel detector in comparison with previous approaches found in the literature. Results also show that the proposed detector is robust in the presence of imperfect channel state information (CSI).

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“…2, i.e., when no error is introduced in the channel matrix. More complex covariances for the noise could be introduced [17].…”

Section: Simulation Resultsmentioning

confidence: 99%

Self and Turbo Iterations for MIMO Receivers and Large-Scale Systems

Santos

Murillo-Fuentes

2019

IEEE Wireless Commun. Lett.

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“…The detectors presented in Section IV assumes a perfect CSI is available at the receiver. In practical systems, it is not possible to obtain a perfect CSI due to imperfections of a channel estimation and quantization errors [239]. This imperfection is more severe in case of massive MIMO detection due to a pilot contamination.…”

Section: The Impact Of Channel Estimation and Precoding On Massivmentioning

confidence: 99%

“…This imperfection is more severe in case of massive MIMO detection due to a pilot contamination. In [240], [241], [239], several symbol detection techniques whereby CSI is estimated from orthogonal pilot sequences. As the MIMO size becomes large, acquiring CSI through pilot sequences transmission is impractical because of a large overhead and the time required for a pilot transmission may exceed the channel coherence time.…”

Section: The Impact Of Channel Estimation and Precoding On Massivmentioning

confidence: 99%

“…The proposed decoder results in a near-ML performance with a significant complexity reduction. In [239], performance of the EP detector in practical situations in which imperfect CSI is available has been investigated. Results show that the lack of perfect CSI produces a significant performance loss of the EP detector.…”

Section: The Impact Of Channel Estimation and Precoding On Massivmentioning

confidence: 99%

See 1 more Smart Citation

Massive MIMO Detection Techniques: A Survey

Albreem

Juntti

Shahabuddin

2019

IEEE Commun. Surv. Tutorials

366

229

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“…While the resulting estimation error always has an adverse effect on the performance of MIMO detectors, the proposed algorithm can be made more robust if the channel estimation error model is incorporated into the design of the s-update and polishing steps, following an approach similar to [23].…”

Section: B Polishingmentioning

confidence: 99%

Iterative Signal Detection for Large-Scale GSM-MIMO Systems

Lopes

Souto

2018

IEEE Trans. Veh. Technol.

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MIMO Detection With Imperfect Channel State Information Using Expectation Propagation

Cited by 31 publications

References 24 publications

Self and Turbo Iterations for MIMO Receivers and Large-Scale Systems

Self and Turbo Iterations for MIMO Receivers and Large-Scale Systems

Massive MIMO Detection Techniques: A Survey

Iterative Signal Detection for Large-Scale GSM-MIMO Systems

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