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Learning Based Relay and Antenna Selection in Cooperative Networks


Learning Based Relay and Antenna Selection in Cooperative Networks

Saha, Apurba (2014) Learning Based Relay and Antenna Selection in Cooperative Networks. Masters thesis, Concordia University.

Text (application/pdf)
SAHA_MASc_F2014.pdf - Accepted Version


We investigate a cross-layer relay selection scheme based on Q-learning algorithm. For the study, we consider multi-relay adaptive decode and forward (DF) cooperative diversity networks over multipath time-varying Rayleigh fading channels. The proposed scheme selects relay subsets that maximizes the link layer transmission efficiency without having knowledge of channel state information (CSI). Results show that the proposed scheme outperforms the capacity based cooperative transmission with the same number of reliable relays in terms of transmission efficiency gain. Furthermore, a Q-learning based cross-layer antenna selection for the multiple antenna relay networks is proposed, where multiple antennas allow more links from the relays to the destination under time varying Rayleigh fading channel. We studied the performance of multi-antenna relay networks and compared with single antenna case. Both schemes are shown to offer high bandwidth efficiency from low to high signal-to-noise ratios (SNRs). Finally, we conclude that cooperative diversity with learning offers improved performance enhancement and bandwidth efficiency for the communication network.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Saha, Apurba
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:30 June 2014
Thesis Supervisor(s):Hamouda, Walaa
ID Code:978728
Deposited By: APURBA SAHA
Deposited On:03 Nov 2014 14:38
Last Modified:18 Jan 2018 17:47
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