Machine Learning in the Air

Gündüz, Deniz; Kerret, Paul de; Sidiropoulos, Nicholas D.; Gesbert, David; Murthy, Chandra R.; Schaar, Mihaela van der

doi:10.1109/jsac.2019.2933969

Cited by 199 publications

(81 citation statements)

References 77 publications

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“…Due to the large amount of recent works on this topic, we only discuss a few representative papers; detailed surveys can be found in [7]- [10]: The work [11] used deep learning for decoding linear codes. Decoding of structured codes using DNNs was considered in [12], while symbol recovery in multiple-input multiple-output (MIMO) systems was treated in [13].…”

Section: Introductionmentioning

confidence: 99%

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Shlezinger

Farsad

Eldar

et al. 2020

IEEE Trans. Wireless Commun.

148

130

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Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm. We identify the specific parts of the Viterbi algorithm that are channel-model-based, and design a DNN to implement only those computations, leaving the rest of the algorithm structure intact.We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems to that integrate DNNs into established algorithms.Parts of this work were accepted for presentation in the

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Section: Introductionmentioning

confidence: 99%

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

Shlezinger

Farsad

Eldar

et al. 2020

IEEE Trans. Wireless Commun.

148

130

View full text Add to dashboard Cite

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“…In the realm of electrical and computer engineering, a plethora of works presented in the literature can be found where ML has contributed to the enhancement of previous systems, or in finding new approximate solutions to recurring problems. These techniques have also been widely employed in communication technology, whether it be at the physical layer or the upper layers, among which we mention: deep learning based detection and decoding, 85 antenna selection in MIMO, 86 wireless and cellular networks, 87 cognitive radios, 88 wireless sensor networks, 89 cybersecurity, 90 and others 91…”

Section: Machine Learning Overviewmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

102

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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“…NN-based approaches have recently shown significant improvements over their model-based counterparts in various wireless communication problems [12][13][14][15] . Data-driven approaches have also been proposed for massive MIMO channel estimation and feedback, in order to exploit the common structures observed in typical massive MIMO CSI matrices.…”

Section: Review Articlementioning

confidence: 99%

Deep Learning for Massive MIMO Channel State Acquisition and Feedback

Mashhadi

Gündüz

2020

J Indian Inst Sci

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Introduction Massive multiple-input multiple-output (MIMO) systems are an important component of 5G and future generation wireless networks due to their ability to serve many users simultaneously with high spectral and energy efficiency. The main idea in massive MIMO is to equip base stations (BSs) in wireless networks with large arrays of cooperating antennas to facilitate spatial multiplexing of many user equipments (UEs) within the same time-frequency resources. Since the number of antennas at the BS is typically assumed to be significantly more than the number of users, a large number of degrees of freedom are available in the downlink, which can be used to shape the transmitted signals in a specific direction or to null interference. This yields a beamforming gain that translates into increased energy efficiency, reduced interference, or improved coverage. In the uplink, each single-antenna user in a massive MIMO system can scale down its transmit power proportionally to the number of antennas at the

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Machine Learning in the Air

Cited by 199 publications

References 77 publications

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

A review on the design and optimization of antennas using machine learning algorithms and techniques

Deep Learning for Massive MIMO Channel State Acquisition and Feedback

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