Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models

Raj, Vishnu; Kalyani, Sheetal

doi:10.1109/lcomm.2018.2868103

Cited by 115 publications

(91 citation statements)

References 8 publications

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“…Hence, the derived objective optimizes the signaling such that a tradeoff is achieved between minimizing the transmit power and maximizing the reconstruction likelihood. If we assume a constant training SN R scenario, the second term becomes a constant and we recover the objective used in [4]- [6], [8].…”

Section: Kl-loss For Awgn Channelmentioning

confidence: 99%

“…. In other words, while the works in [4]- [6], [8] impose hard constraints, this work imposes a soft constraint.…”

Section: Kl-loss For Awgn Channelmentioning

confidence: 99%

“…Previous works on end to end communication system design using deep learning [4]- [6], [8], [14] relied on Autoencoders (AE) for designing the encoder and decoder. One of the original purposes of AE is to perform dimensionality reduction arXiv:1904.08559v3 [cs.IT] 25 Jan 2020 [15] by using the latent codes produced by the encoder as compressed representation.…”

Section: Introductionmentioning

confidence: 99%

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Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

Raj

Kalyani

2020

IEEE Trans. Cogn. Commun. Netw.

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Recent research in the design of end to end communication system using deep learning has produced models which can outperform traditional communication schemes. Most of these architectures leveraged autoencoders to design the encoder at the transmitter and decoder at the receiver and train them jointly by modeling transmit symbols as latent codes from the encoder. However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols. Traditional autoencoders are not designed to work with latent codes corrupted with noise. In this work, we provide a framework to design end to end communication systems which accounts for the existence of noise corrupted transmit symbols. The proposed method uses deep neural architecture. An objective function for optimizing these models is derived based on the concepts of variational inference. Further, domain knowledge such as channel type can be systematically integrated into the objective. Through numerical simulation, the proposed method is shown to consistently produce models with better packing density and achieving it faster in multiple popular channel models as compared to the previous works leveraging deep learning models.

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Section: Kl-loss For Awgn Channelmentioning

confidence: 99%

“…. In other words, while the works in [4]- [6], [8] impose hard constraints, this work imposes a soft constraint.…”

Section: Kl-loss For Awgn Channelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

Raj

Kalyani

2020

IEEE Trans. Cogn. Commun. Netw.

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“…In order to design a radio communication system with autoencoders and to overcome the lack of channel knowledge, a twophase training strategy was followed in [55]. For the same objective, [56] and [57] utilized stochastic perturbation and policy gradients techniques, respectively. Generative models can be used to generate samples of a given communication channel, in particular GANs have been exploited in [50], [58].…”

Section: ) Application Of Supervised Learning To Communicationsmentioning

confidence: 99%

Machine Learning Tips and Tricks for Power Line Communications

et al. 2019

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A great deal of attention has been recently given to Machine Learning (ML) techniques in many different application fields. This paper provides a vision of what ML can do in Power Line Communications (PLC). We firstly and briefly describe classical formulations of ML, and distinguish deterministic from statistical learning models with relevance to communications. We then discuss ML applications in PLC for each layer, namely, for characterization and modeling, for the development of physical layer algorithms, for media access control and networking. Finally, other applications of PLC that can benefit from the usage of ML, as grid diagnostics, are analyzed. Illustrative numerical examples are reported to serve the purpose of validating the ideas and motivate future research endeavors in this stimulating signal/data processing field.

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“…The authors in [23] have also used auto-encoders for the channel state information (CSI) feedback problem. Interestingly, [24] studies the physical layer structures without channel models via DL.…”

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confidence: 99%

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

Elbir

Papazafeiropoulos

2020

IEEE Trans. Veh. Technol.

135

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In multi-user millimeter wave (mmWave) multipleinput-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.

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Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models

Cited by 115 publications

References 8 publications

Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

Machine Learning Tips and Tricks for Power Line Communications

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

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