A matching game framework for users clustering and resource allocation with wireless power transfer in a CR‐NOMA network. (29th October 2022) - Das, Deepa; Khadanga, Rajendra Kumar; Rout, Deepak Kumar
Summary: This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non‐orthogonal multiple access (CR‐NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one‐to‐one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale‐Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied. Abstract : This paper proposes a resource allocation scheme for maximizing system throughput with residual power transfer in a densely deployed users CR‐NOMA network considering the downlink scenario. The objective of throughput maximization is achieved by jointly determining the optimal clusters and power allocation that enables the cluster heads to harvest energy from the primary network. The users clustering is based on the Gale‐Shapley matching game theory integrated with the Hungarian method, and the power allocation to the users is solved by employing the SVM algorithm.