Distributed Cooperative and Noncooperative Joint Power and Beamforming Adaptation Game for MIMO Sensor Network
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Date
2020
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Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Distributed Joint beamforming and power adaptation algorithms are of interest for MIMO ad hoc networks. Cooperative and non-cooperative games have been presented to decrease interference (mutual) at each sensor node, under receiver signal-to-interference and noise (SINR) constraints. In reduced feedback algorithms, the optimum transmitter node beamformer is selected from a predefined codebook. This paper introduces a cooperative optimal beamformer selection algorithm to minimize the total power consumption for cluster-based network topology under different minimum SINR constraints. The algorithm reduces overhead incurred by 48% and 10% while increasing the convergence rate to the steady-state allocation by 17% and 9% for the cooperative and non-cooperative beamformer selection games, respectively. Simulation results verify the proposed theoretical analysis, and demonstrate the performance of the Enhanced Co-Operative Power Minimization Algorithm (ECOPMA), Reinforcement Learning based Power allocation and Beamformer Algorithm (RLPBA) for the non-cooperative game [1], with state of the art methods and centralized (optimal) solutions as a fair benchmark. © 2020 IEEE.
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Keywords
Beamforming, MIMO Ad hoc network, Overhead Rate, Potential Game, Reinforcement Learning, SINR
Turkish CoHE Thesis Center URL
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0
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Source
Proceedings of 2020 1st Information Technology to Enhance E-Learning and other Application Conference, IT-ELA 2020 -- 1st International Conference Information Technology to Enhance E-Learning and other Application, IT-ELA 2020 -- 12 July 2020 through 13 July 2020 -- Baghdad -- 164981
Volume
Issue
Start Page
127
End Page
131