Distributed Cooperative and Noncooperative Joint Power and Beamforming Adaptation Game for MIMO Sensor Network

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.