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Good Neighbor Alternative to Best Response and Machine Learning Based Beamforming and Power Adaptation for MIMO Ad Hoc Networks

Kuzminskiy, Alexandr, Xiao, Pei and Tafazolli, Rahim (2020) Good Neighbor Alternative to Best Response and Machine Learning Based Beamforming and Power Adaptation for MIMO Ad Hoc Networks In: 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications: Track 2: Networking and MAC, 31 Aug - 3rd Sept 2020, London, UK (moved to Virtual Conference).

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Abstract

Decentralized joint transmit power and beam- forming selection for multiple antenna wireless ad hoc net- works operating in a multi-user interference environment is considered. An important feature of the considered environ- ment is that altering the transmit beamforming pattern at some node generally creates more signi�cant changes to in- terference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence be- havior of the proposed algorithm compared to the best re- sponse and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Kuzminskiy, Alexandra.kuzminskiy@surrey.ac.uk
Xiao, PeiP.Xiao@surrey.ac.uk
Tafazolli, RahimR.Tafazolli@surrey.ac.uk
Date : 20 June 2020
Additional Information : Embargo OK Metadata OK Awaiting details when paper is published online
Depositing User : James Marshall
Date Deposited : 03 Jul 2020 15:31
Last Modified : 03 Jul 2020 15:31
URI: http://epubs.surrey.ac.uk/id/eprint/858135

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