Decision-Feedback Differential Detection with Optimum Detection Order Metric for Noncoherent Massive MIMO Systems

Yammine, George; Fischer, Robert F. H.

doi:10.23919/eusipco47968.2020.9287328

Cited by 1 publication

(4 citation statements)

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“…The user power-space-profile (PSP) represents the average receive power, P m = E{|h m | 2 } for each receive antenna at the receiver [14]. The knowledge of the PSP is assumed to be available at the base station as noted in Fig.…”

Section: System Modelmentioning

confidence: 99%

“…. , ς Nrx ) is a real-valued diagonal weighting matrix that contains weights matched to the power-space-profile as described in [13], [14], where we set ς 2 m ∼ P m . The complex matrix Z is a Hermitian matrix, i.e., Z = Z H and composed of the correlation with z k,l = z * l,k , which form a set of sufficient statistics for non-coherent maximum-likelihood detection.…”

Section: System Modelmentioning

confidence: 99%

“…Decision-feedback differential detection (DFDD) [13], [36] improves the performance of DD and still avoids the high computational complexity of MSDD. It combines the principle of decisionfeedback and optimum decision order [13], [14], and lead only to a small loss in performance compared to optimum MSDD and MSDSD. DFDD is based on individual symbol detection algorithm such that, decisions are made successively taking the previously estimated symbols into consideration to detect the current symbol b DFDD k as follows…”

Section: B Decision-feedback Differential Detectionmentioning

confidence: 99%

“…In [13], the authors propose a sorted feedback differential detection algorithm for massive MIMO systems. In [14], they perform decision-feedback differential detection with an optimum sorting order for single-user and multiple-user massive MIMO systems. In [15], the use of multiple-symbol differential sphere decoding to implement optimum multiple-symbol differential detection [16] is compared with the optimum sorted decision-feedback differential detection proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

Mahmoud¹,

El-Mahdy²,

Fischer³

2021

Preprint

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<div>In this work, non-coherent massive MIMO differential phase-shift keying modulation (DPSK) detection is considered to get rid of the complexity of channel estimation. However, most of the well-performing DPSK detection techniques require high computational complexity at the receiver. The use of deep-learning is proposed for detecting the transmitted DPSK symbols over a single-user massive MIMO system. We provide a multiple-symbol differential detection implementation using deep-learning. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with differential detection (DD), decision-feedback differential detection (DFDD), and multiple-symbol differential detection (MSDD) for the same system parameters. Where multiple-symbol differential sphere detection (MSDSD) is used to implement MSDD. The results show that the proposed deep-learning-based classification neural networks outperform decision-feedback differential detection and achieve an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection.</div>

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%