Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation

Xiang, Luping; Liu, Yusha; Luong, Thien Van; Maunder, Robert G.; Hanzo, Lajos

doi:10.1109/access.2020.3032627

Cited by 29 publications

(16 citation statements)

References 36 publications

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“…Owing to the fact that ML algorithms are essentially data-driven, they have attained a superb performance in system identification and capturing pragmatic system imperfections [3]. In essence, neural networks (NNs) can be utilized for precise channel estimation [4], [5]. However, deep learning (DL) algorithms generally impose high require knowledge of channel statistics.…”

Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning-Enhanced Channel Estimation for OFDM Systems

et al. 2021

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Recently much research work has focused on employing deep learning (DL) algorithms to perform channel estimation in the upcoming 6G communication systems. However, these DL algorithms are usually computationally demanding and require a large number of training samples. Hence, this work investigates the feasibility of designing efficient machine learning (ML) algorithms that can effectively estimate and track time-varying, frequency-selective channels. The proposed algorithm is integrated with orthogonal frequency-division multiplexing (OFDM) to eliminate intersymbol interference (ISI) induced by the frequency-selective multipath channel and compared with the well-known least square (LS) and linear minimum mean square error (LMMSE) channel estimation algorithms. The obtained results have demonstrated that even when a small number of pilot samples, N P , is inserted before the N subcarriers OFDM symbol, the introduced ML-based channel estimation is superior to the LS and LMMSE algorithms. This dominance is reflected in the bit-error-rate (BER) performance of the proposed algorithm, which attains a gain of 2.5 dB and 5.5 dB over the LMMSE and LS algorithms, respectively, when N P = N 8 . Furthermore, the BER performance of the proposed algorithm is shown to degrade by only 0.2 dB when the maximum Doppler frequency is randomly varied. Finally, the number of iterations required by the proposed algorithm to converge to the smallest achievable mean-squared error (MSE) are thoroughly examined for various signalto-noise ratio (SNR) levels. INDEX TERMS Deep learning (DL), machine learning (ML), orthogonal frequency-division multiplexing (OFDM), intersymbol interference (ISI).The associate editor coordinating the review of this manuscript and approving it for publication was F. K. Wang . computational requirements, which, in turn, increases the complexity of the communications systems. Moreover, since DL networks usually contain a large number of neurons distributed among many layers, these networks require a large number of training samples to converge to the desired output. Unlike DL algorithms, shallow ML algorithms exhibit low computational requirements that make them suitable for implementing fast signal processing schemes, which can fulfil the increasing data traffic requirements imposed by the new generation of wireless communication systems. Consequently, immense attention has been shifted towards exploiting ML algorithms to revolutionize modern wireless communication systems [6]. In essence, conventional communication systems are generally based on well-established mathematical models. Hence, channel estimation algorithms such as the minimum mean square error (MMSE) algorithm

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Section: Introductionmentioning

confidence: 99%

Efficient Machine Learning-Enhanced Channel Estimation for OFDM Systems

et al. 2021

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“…It has significantly improved the performance with a low computational complexity. In [153], a fully-connected multi-layer DNNs for joint channel estimation and data detection of spatial modulation (SM) MIMO system, called Deep spatial modulation (DeepSM), is proposed. The DeepSM architecture operates in a data-driven approach and obtains a BER performance close to that of the conventional SM MIMO system over time-invariant channels with low detection complexity.…”

Section: Belief Propagation (Bp)mentioning

confidence: 99%

Deep Learning for Massive MIMO Uplink Detectors

Albreem

Alhabbash²,

Shahabuddin

et al. 2022

IEEE Commun. Surv. Tutorials

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Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers' performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector's architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

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“…Hence, model-driven DL networks usually only need to learn a small number of key parameters. In [3], a DNN-aided spatial modulation concept, named DeepSM, was proposed. The authors designed a pair of DNN structures to replace data-based channel estimator and detector.…”

Section: Application Of Dnn For Cementioning

confidence: 99%

Application of Deep Learning in Wireless Networks for Channel Estimation: A Survey

Peng

Zhu

Liang

2022

J. Phys.: Conf. Ser.

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The quality of channel estimation (CE) is critical to the performence of wireless communication systems. In addition to the conventional model based CE methods appeared in the past few decades, deep learning (DL) based CE methods, which is introduced to learn the statistical characteristics of wireless channels, has emerged as a promising methods in recently years. In this survey, different DL models applied to solve CE problems are discussed firstly, then DL-based CE problems in different wireless communication systems are investigated.

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Deep-Learning-Aided Joint Channel Estimation and Data Detection for Spatial Modulation

Cited by 29 publications

References 36 publications

Efficient Machine Learning-Enhanced Channel Estimation for OFDM Systems

Efficient Machine Learning-Enhanced Channel Estimation for OFDM Systems

Deep Learning for Massive MIMO Uplink Detectors

Application of Deep Learning in Wireless Networks for Channel Estimation: A Survey

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