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