Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

Demir, Özlem Tuğfe; Björnson, Emil

doi:10.1109/ojcoms.2019.2959913

Cited by 44 publications

(27 citation statements)

References 24 publications

(93 reference statements)

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“…It is difficult to get the expression of MMSE interpolation under a non-linear model, as shown in (8). By treating the non-linear distortion as noise, distortion-aware linear MMSE (DA-LMMSE) estimation proposed in [10] can improve the estimation performance. However, the required effective noise variance incorporating non-linear distortion is hard to obtain in practical systems.…”

Section: B Conventional Channel Estimation Methodsmentioning

confidence: 99%

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

Mei

Zhang

et al. 2021

IEEE Trans. Commun.

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In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

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Section: B Conventional Channel Estimation Methodsmentioning

confidence: 99%

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

Mei

Zhang

et al. 2021

IEEE Trans. Commun.

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“…The end-to-end learning approach [8] has found many applications in scenarios in which the channel model is unknown or well-established mathematical models are unavailable. Another application that involves the use of DL to address hardware nonlinearities in MIMO systems (e.g., hardware impairments) was presented [9]. These researchers proposed two DL-based estimators to exploit the nonlinear characteristics with the aim of improving the estimation performance.…”

Section: ) Unknown Model and Nonlinearitiesmentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

et al. 2021

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Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

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“…Several other methods such as Huber fitting-based ADMM (Alternating Direction Method of Multipliers) and conventional ADMM method [24] were also considered recently for uplink signal detection, but for a system with a large number of antennas, these algorithms do not provide a good trade-off between the error and the complexity. Recently, various optimal algorithm are designed for the uplink signal detection [25][26][27][28][29][30].…”

Section: Relevant Prior Art and Motivationmentioning

confidence: 99%

SSOR Preconditioned Gauss-Seidel Detection and Its Hardware Architecture for 5G and beyond Massive MIMO Networks

et al. 2021

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With the limitedness of the sub-6 GHz bandwidth, the world is exploring a thrilling wireless technology known as massive MIMO. This wireless access technology is swiftly becoming key for 5G, B5G, and 6G network deployment. The massive MIMO system brings together antennas at both base stations and the user terminals to provide high spectral service. Despite the fact that massive MIMO offers astronomical benefits such as low latency, high data rate, improved array gain, and far better reliability, it faces several implementation challenges due to the hundreds of antennas at the base station. The signal detection at the base station during the uplink is one of the critical issues in this technology. Detection of user signal becomes computationally complex with a multitude of antennas present in the massive MIMO systems. This paper proposes a novel preconditioned and accelerated Gauss–Siedel algorithm referred to as Symmetric Successive Over-relaxation Preconditioned Gauss-Seidel (SSORGS). The proposed algorithm will address the signal detection challenges associated with massive MIMO technology. Furthermore, we enhance the convergence rate of the proposed algorithm by introducing a novel Symmetric Successive Over-relaxation preconditioner (SSOR) scheme and an initialization scheme based on the instantaneous channel condition between the base station and the user. The simulation results show that the proposed algorithm referred to as Symmetric Successive Over-relaxation Preconditioned Gauss-Seidel (SSORGS) provides optimal BER performance. At BER =10−3, over the range of SNR, the SSORGS algorithm performs better than the traditional algorithms. Additionally, the proposed algorithm is computationally more efficient than the traditional algorithms. Furthermore, we designed a comprehensive hardware architecture for the SSORGS algorithm to find the interrelated components necessary to build the actual physical system.

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Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

Cited by 44 publications

References 24 publications

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

Intelligent Radio Signal Processing: A Survey

SSOR Preconditioned Gauss-Seidel Detection and Its Hardware Architecture for 5G and beyond Massive MIMO Networks

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