Outage Analysis of Decode-and-Forward Two-Way Relay Selection With Different Coding and Decoding Schemes

Li, Enyu; Wang, Xuhu; Wu, Zeju; Hao, Siyuan; Dong, Yunquan

doi:10.1109/jsyst.2018.2810019

Cited by 14 publications

(10 citation statements)

References 28 publications

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“…From (19), we can deduce that the maximum diversity order for the S-DF scheme over *Rayleigh fading channels is = ⁄ . This is because diversity order is defined as the slope of the curve as a function of the average SNR in log-log scale, i.e., [34] = lim SNR→∞ (− log / log SNR) = (20) As we note, the result in (20) is novel and generalizes known results on the diversity order of these relaying schemes on Rayleigh fading channels to the cascaded Rayleigh fading scenario. Our analytical results show that the full diversity order ( ≈ ) can be obtained for classical Rayleigh fading channels (where the selected relay is fixed).…”

Section: A) S-df Relayingmentioning

confidence: 58%

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Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Alghorani,

Chekkouri,

Chekired

et al. 2020

SSRN Journal

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We investigate the performance of a dual-hop intervehicular communications system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called *Rayleigh), which involves the product of independent Rayleigh random variables. This channel model provides a realistic description of inter-vehicular communications, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, = 2); our system model considers the general cascading order , for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in intervehicular communications. The analytical and simulation results show that both decode-and-forward and amplify-and-forward relaying schemes have the same diversity order ( = / ) in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.Index Terms-*Rayleigh distributions, machine learning, cooperative communications, relay selection, and inter-vehicular networks.

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Section: A) S-df Relayingmentioning

confidence: 58%

“…Relay selection has been studied extensively in the literature, see, e.g., [20][21][22] and the references therein. However, current results are limited to Rayleigh fading channel assumption (i.e., 𝑛 = 1).…”

Section: Introductionmentioning

confidence: 99%

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Alghorani,

Chekkouri,

Chekired

et al. 2020

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Several studies have reported that cooperative communications through multiple Rayleigh fading channels can improve the link reliability when traffic density is high [6], [19]. Relay selection has been studied extensively in the literature, see, e.g., [20]- [22] and the references therein. However, current results are limited to Rayleigh fading channel assumption (i.e., n = 1).…”

Section: Related Workmentioning

confidence: 99%

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Alghorani¹,

pierre²

2021

Preprint

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<div>We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n; for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.</div>

show abstract

“…Several studies have reported that cooperative communications through multiple Rayleigh fading channels can improve the link reliability when traffic density is high [6,19]. Relay selection has been studied extensively in the literature, see, e.g., [20][21][22] and the references therein. However, current results are limited to Rayleigh fading channel assumption (i.e., n = 1).…”

Section: Related Workmentioning

confidence: 99%

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Alghorani¹,

pierre²

2021

Preprint

View full text Add to dashboard Cite

<div>We investigate the performance of a dual-hop intervehicular</div><div>communications (IVC) system with relay selection</div><div>strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n; for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation</div><div>scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.</div>

show abstract

Outage Analysis of Decode-and-Forward Two-Way Relay Selection With Different Coding and Decoding Schemes

Cited by 14 publications

References 28 publications

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

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