Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning

Area of Science:

• Wireless Communication
• Network Engineering
• Artificial Intelligence

Background:

• Cognitive radio networks (CRNs) require efficient spectrum sensing and channel selection for dynamic spectrum access.
• Decentralized and distributed algorithms are crucial for CRNs operating in complex, real-world scenarios.
• Existing methods often rely on centralized control, which can be a bottleneck and single point of failure.

Purpose of the Study:

• To propose a novel distributed algorithm for spectrum sensing and channel selection in CRNs.
• To leverage a consensus strategy within a multi-agent reinforcement learning framework.
• To enable decentralized collaboration for optimal joint spectrum sensing and channel selection.

Main Methods:

• A consensus-based strategy implemented over a sparse, time-varying communication network.
• Multi-agent reinforcement learning to facilitate decentralized decision-making.
• Analysis of algorithm characteristics including denoising, coordinated actions, and convergence rates.

Main Results:

• The proposed algorithm enables agents to calculate optimal joint spectrum sensing and channel selection strategies without individual optimality.
• The approach is scalable and robust to node and link failures.
• Simulations show high effectiveness, closely mimicking centralized schemes even with sparse communication.

Conclusions:

• The consensus-based distributed algorithm offers a viable and effective solution for spectrum sensing and channel selection in CRNs.
• Decentralized operation enhances network robustness and scalability.
• The algorithm achieves near-optimal performance comparable to centralized systems in practical CRN environments.