Blockchain-Assisted Federated Deep Reinforcement Learning (B-FDRL) for Trustworthy Spectrum Allocation in 5G Cognitive Radio Networks

Abstract

The issue of effective and secure spectrum allocation is a substantial challenge in 5G cognitive radio networks. This challenge has emerged due to the availability of spectrum dynamically, heterogeneous user demands and increasing security threats. Current methodologies that employ a combination of deep reinforcement learning (DRL) and federated learning (FL) enhance spectrum utilization and enable decentralized decision-making; however, they suffer from challenges such as lack of trust, vulnerability to malicious participants, and absence of transparent incentive schemes. This paper addresses these challenges by proposing a blockchain-assisted federated deep reinforcement learning (B-FDRL) framework for trustworthy spectrum allocation. The designed model incorporates DRL based adaptive spectrum decision-making and FL for privacy preserving collaborative learning along with blockchain enabled smart contracts for secure model aggregation, tamper-proof transaction logging and incentive-based participation. To incorporate a reputation-aware mechanism to enhance reliability and deter misbehavior. The experiment done shows that the proposed framework gives high spectrum efficiency, reduced interference, better fairness and robust security as compared to conventional DRL and FL methods. This shows that the proposed method is effective for scalable, secure and smart spectrum management for future wireless networks.