Khiarullah, Ali KamilTureli, UfukKivanc, DidemMekatronik / Mechatronics2024-05-252024-05-25201901875-68911875-688310.2991/ijcis.d.191121.0012-s2.0-85078228255https://doi.org/10.2991/ijcis.d.191121.001https://hdl.handle.net/20.500.14517/1494Tureli, Ufuk/0000-0002-4986-5270; Tureli, Didem/0000-0001-6835-2940;During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference and overhead reduction, but have failed to achieve the trade-off between communication overhead and power minimization. Cooperative method using game theory achieves the power minimization, but introduced the overhead. The non-cooperative solution using game theory reduced the overhead, but it takes more power and iterations for convergence. In this paper, a novel game theory based algorithms proposed to achieve the trade-off between power control and communication overhead for multiple antennas enabled wireless ad-hoc networks operating in multiple-users interference environment. The optimized joint iterative power adaption and beamforming method designed to minimize the mutual interference at every wireless node with constant received signal to interference noise ratio (SINR) at every receiver node. First cooperative potential game theory based algorithm designed for the power and interference minimization in which users cluster and binary weight books along used to reduce the overhead. Then the non-cooperative based approach using the reinforcement learning (RL) method is proposed to reduce the number of iterations and power consumption in networks, the proposed RL procedure is fully distributed as every transmit node require only an observation of its instantaneous beamformer label which can be obtained from its receive node. The simulation results of both methods prove the efficient power adaption and beamforming for small and large networks with minimum overhead and interference compared to state-of-art methods. (C) 2019 The Authors. Published by Atlantis Press SARL.eninfo:eu-repo/semantics/openAccessOptimized intelligent design for smart systemsWireless network beamformingPower adaptionInterferenceStrategic decision-makingGame theoryReinforcement learningArticleQ3Q112214361445WOS:000515063600043