Deep reinforcement learning-driven cross-layer optimization of IRS-assisted NOMA with blockchain for secure 6G oriented networks spectrum management

The rapid development of connected devices and data intensive applications is demanding the need for smart spectrum management mechanisms in emerging 6G Networks. Traditional centralized spectrum allocation schemes often find it challenging to cope with excessive wireless environments and larger-scale distributed networks and results in underutilizing the spectrum and adding more communication latency. To get over these difficulties, a cross-layer architecture with intelligent reflecting surface assisted communication, decentralized spectrum coordination and collaborative learning is proposed in this paper. The framework makes use of Federated Learning, in order to teach spectrum prediction models jointly at the edge devices, thus allowing data privacy preservation while making better decisions regarding resource allocation. In addition, adaptive signal propagation is achieved by configurable reflecting elements for improving communication reliability under different channel conditions. The proposed system is tested using the MATLAB simulations of a dense wireless network with multiple edge devices. The spectrum utilization, communication latency, network throughput, secrecy rate, and federated-learning convergence are used to evaluate the performance. The simulation results show that the proposed framework can achieve a spectrum utilization rate of around 82%, and the communication latency is around 12ms, with the federated-learning accuracy of about approximately 92% in the case of a dense network. These results show that the proposed design for a cross layer offers an efficient and scalable solution for decentralized spectrum management in the next generation of wireless systems.