A novel amplify-and-forward relay channel model for mobile-to-mobile fading channels under line-of-sight conditions

Performance of multi-hop multi-branch amplify and forward (AF) cooperative communication over Rician fading channels is presented in this paper by a novel analysis method. Rician distribution is used to analyze the system because this channel model is useful in aerospace wireless communication channels. We derive an asymptotic expression for the statistics of the received signal-to-noise ratio (SNR) in the system under study with well-known moment-generating function (MGF)-based approach with maximal ratio combining (MRC) scheme. Moreover, for comparative analysis in different branches and hops, outage probability and symbol error ratio are derived. Finally, numerical simulation results are provided to confirm the theoretical results. These results are presented to illustrate the considerable performance improvement achieved by the increasing number of branches and hops.

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Analysis of Packet Diversity in Buffer-Aided Relaying over Symmetric and Asymmetric Rayleigh Fading Channels

Symmetry ◽

10.3390/sym12020241 ◽

2020 ◽

Vol 12 (2) ◽

pp. 241 ◽

Cited By ~ 1

Author(s):

Hina Nasir ◽

Nadeem Javaid ◽

Waseem Raza ◽

Muhammad Shafiq

Keyword(s):

Outage Probability ◽

Fading Channels ◽

Relay Selection ◽

Signal To Noise Ratio ◽

Packet Delay ◽

Cooperative Relaying ◽

Link Quality ◽

Relay Channel ◽

Amplify And Forward ◽

Active Channels

In this paper, we propose innovative schemes for relay selection that jointly explore packet selection and relay selection for buffer-aided amplify and forward (AF) cooperative relaying networks. The first proposed scheme chooses the most suitable channel based on link quality from all active channels, i.e., channels with neither empty nor full corresponding buffers. In the second proposed scheme, the most suitable channel is chosen based on buffer status. When the source-relay channel is determined, the corresponding relay collects data in the buffer. Likewise, when the relay-destination channel is picked, the most suitable packet is dispatched from the buffer. The most suitable packet is one that provides the highest end-to-end equivalent signal-to-noise ratio. We simulated the outage probability, average throughput and packet delay and analyzed the proposed protocol for both symmetric and asymmetric channel conditions. Comparison is made against the existing buffer-aided schemes. The results show that the proposed relay and packet selection systems help to reduce the outage probability, diversity gain and delay.

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A Geometrical Channel Model for MIMO Mobile-to-Mobile Fading Channels in Cooperative Networks

10.1109/vetecs.2009.5073296 ◽

2009 ◽

Cited By ~ 7

Author(s):

Batool Talha ◽

Matthias Patzold

Keyword(s):

Fading Channels ◽

Channel Model ◽

Cooperative Networks ◽

Mobile Fading Channels

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

10.36227/techrxiv.12107199.v1 ◽

2020 ◽

Author(s):

Yahia Alghorani ◽

samuel pierre

Keyword(s):

Machine Learning ◽

Power Allocation ◽

Fading Channels ◽

Rayleigh Fading ◽

Channel Model ◽

Machine Learning Algorithms ◽

Rayleigh Fading Channels ◽

High Signal ◽

Approximate Analytic Solution ◽

Amplify And Forward

<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>

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