Hybrid ML-MMSE adaptive multiuser detection based on joint channel estimation in SDMA-OFDM systems

Yeşilyurt, Uğur; Ertug, Ozgur

doi:10.23919/softcom.2017.8115545

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…where S(·) calculates the number of non-zero elements in the inner vector. By configuring different objective functions of g(·), traditional detection algorithms minimize the SER results through maximizing the likelihood function [18], minimizing the MSE [4] or zero-forcing the noise values [11], where the mathematical expressions are 2 ,…”

Section: Problem Formulation and Traditionalmentioning

confidence: 99%

Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Chen

Zhang

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

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Section: Problem Formulation and Traditionalmentioning

confidence: 99%

Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Chen

Zhang

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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“…Genetic algorithm (GA)‐assisted MMSE MUD attains suboptimal performance with decreased complexity . The hybrid ML‐MMSE adaptive multiuser detection technique was applied to SDMA‐OFDM systems . Joint ML and MMSE‐SIC detection has been recommended for multi‐cell network environments .…”

Section: Introductionmentioning

confidence: 99%

Hybrid SNR-Adaptive Multiuser Detectors for SDMA-OFDM Systems

Yeşilyurt¹,

Ertug²

2018

ETRI Journal

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Multiuser detection (MUD) and channel estimation techniques in space‐division multiple‐access aided orthogonal frequency‐division multiplexing systems recently has received intensive interest in receiver design technologies. The maximum likelihood (ML) MUD that provides optimal performance has the cost of a dramatically increased computational complexity. The minimum mean‐squared error (MMSE) MUD exhibits poor performance, although it achieves lower computational complexity. With almost the same complexity, an MMSE with successive interference cancellation (SIC) scheme achieves a better bit error rate performance than a linear MMSE multiuser detector. In this paper, hybrid ML‐MMSE with SIC adaptive multiuser detection based on the joint channel estimation method is suggested for signal detection. The simulation results show that the proposed method achieves good performance close to the optimal ML performance at low SNR values and a low computational complexity at high SNR values.

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“…Therefore, a study that allows the BER to be minimized while ensuring an acceptable average computational complexity in a hybrid multiuser detection MC-CDMA system is a proposal for future work. In fact, this aspect of hybrid detection is a relatively new topic in OFDMA and MIMO systems [139,140].…”

Section: Future Workmentioning

confidence: 99%

Bit error rate and spectral efficiency evaluation of MC-CDMA cellular systems employing multiuser detection

Mora¹

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I would also like to thank my parents, Nancy and Jorge, because with their love, selfdenial, sacrifice and example, they help me and support me when I make mistakes, they advise me when I doubt, and when I need them, they are always by my side.I would like to express my gratitude to Prof. Dr. Celso de Almeida, for his support, patience, and encouragement throughout my graduate studies. To him I owe moments of kind dedication and guidance during the development of this work. His technical and editorial advice was essential to the completion of this dissertation and he has taught me innumerable lessons and insights on the workings of academic research and life in general.My thanks also go to the members of my dissertation committee, Prof.

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Hybrid ML-MMSE adaptive multiuser detection based on joint channel estimation in SDMA-OFDM systems

Cited by 8 publications

References 10 publications

Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Hybrid SNR-Adaptive Multiuser Detectors for SDMA-OFDM Systems

Bit error rate and spectral efficiency evaluation of MC-CDMA cellular systems employing multiuser detection

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