The Degrees of Freedom of the Interference Channel With a Cognitive Relay Under Delayed Feedback

Kang, Hyo Seung; Kang, Minho; Nosratinia, Aria; Choi, Wan

doi:10.1109/tit.2017.2708107

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The degrees of freedom of interference channels with a cognitive relay under delayed feedback is considered in [17]. Lastly, a new cooperative transmit scheme is proposed for the multi-way relay channel, building upon a distributed compute-and-forward strategy [18].…”

Section: Related Workmentioning

confidence: 99%

Interfering Relay Channels

Hiep

Oechtering

Skoglund

et al. 2017

Entropy

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This paper introduces and studies a model in which two relay channels interfere with each other. Motivated by practical scenarios in heterogeneous wireless access networks, each relay is assumed to be connected to its intended receiver through a digital link with finite capacity. Inner and outer bounds for achievable rates are derived and shown to be tight for new discrete memoryless classes, which generalize and unify several known cases involving interference and relay channels. Capacity region and sum capacity for multiple Gaussian scenarios are also characterized to within a constant gap. The results show the optimality or near-optimality of the quantize-bin-and-forward coding scheme for practically relevant relay-interference networks, which brings important engineering insight into the design of wireless communications systems.

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Section: Related Workmentioning

confidence: 99%

Interfering Relay Channels

Hiep

Oechtering

Skoglund

et al. 2017

Entropy

View full text Add to dashboard Cite

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Interference Alignment in Relay-Aided Networks with Imperfect CSIT

Tian

Wang

2020

Wireless Pers Commun

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Deep Learning for Fading Channel Prediction

Jiang

Schotten

2020

IEEE Open J. Commun. Soc.

127

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Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.

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The Degrees of Freedom of the Interference Channel With a Cognitive Relay Under Delayed Feedback

Abstract: This paper studies the interference channel with a cognitive relay (ICCR) under delayed feedback.Three types of delayed feedback are studied: delayed channel state information at the transmitter (CSIT),

Cited by 3 publications

References 35 publications

Interfering Relay Channels

Interfering Relay Channels

Interference Alignment in Relay-Aided Networks with Imperfect CSIT

Deep Learning for Fading Channel Prediction

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