Deep Learning-Based Robust Precoding for Massive MIMO

Shi, Junchao; Wang, Wenjin; Yi, Xinping; Gao, Xiqi; Li, Geoffrey Ye

doi:10.1109/tcomm.2021.3105569

Cited by 35 publications

(16 citation statements)

References 53 publications

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“…The substantial research in [99], [100], and [101] results in a DL-aided precoding framework for downlink massive MU-MIMO systems with a uniform planar array at the BS. The objective of this framework is to implement precoding that maximizes the ergodic rate while limiting overall transmit power under a constraint by using both instantaneous and statistical CSI.…”

Section: Intelligent Transmitter Design and Autoencodersmentioning

confidence: 99%

“…The study in [102] proposes another data-driven DNN-based precoding architecture in which the network fully learns the input-output relationship of a nearly optimum precoder to maximize mutual information. Simulation results show that the suggested network produces almost the same performance as the optimum precoder with significantly lower complexity than the traditional iterative precoders, just like in [99], [100], and [101]. The work in [103] takes a similar approach to [102], in which a data-driven DNN learns the behavior of an ideal precoder to maximize the mutual information with considerably reduced complexity.…”

Section: Intelligent Transmitter Design and Autoencodersmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: Intelligent Transmitter Design and Autoencodersmentioning

confidence: 99%

Section: Intelligent Transmitter Design and Autoencodersmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…According to the presented results, the proposed method can increase SE, by using partial CSI feedback. [24] presented a deep learning approach for down-link precoder design in m-MIMO systems by making use of channel estimates and statistical parameters of channel estimation error. In this context, near-optimal performance can be achieved with low computational complexity.…”

Section: A Related Workmentioning

confidence: 99%

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

et al. 2022

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The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the MLassisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

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“…For example, [11], [12] propose ML solutions for selection of pre-computed beams. In addition, [13], [14] propose an approach in which the channel state information is input to a CNN network to produce a prediction of key parameters in traditional beamforming algorithms. In addition, [15] proposes an unsupervised learning solution for beamforming.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

DeepTx: Deep Learning Beamforming with Channel Prediction

Huttunen¹,

Korpi²,

Honkala³

2022

Preprint

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Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.

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Deep Learning-Based Robust Precoding for Massive MIMO

Cited by 35 publications

References 53 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

DeepTx: Deep Learning Beamforming with Channel Prediction

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