Quantum Learning-Enabled Green Communication for Next-Generation Wireless Systems

Next generation wireless systems have witnessed significant R&D attention from academia and industries to enable wide range of applications for connected environment around us. The technical design next in terms relay transmit power control is very critical due the ever-reducing size these sensor enabled systems. growing demand computation capability smart decision making further diversified significance control. Towards harnessing benefits Quantum Reinforcement Leaning (QRL) systems, this article presents a framework joint optimal Relay Power Selection (QRL-RPS). In QRL-RPS, each node learns using its present past local state's knowledge take selection. Firstly, RPS problem modelled as Markov Decision Process (MDP), then QRL optimization aspect MDP formulated focusing on energy consumption throughput network utility. Secondly, QRL-RPS algorithm developed based Grover's iteration solve problem. comparative performance evaluation attests benefit proposed compared state-of-the-art techniques.