Mode-Aware Radio Resource Allocation Algorithm in Hybrid Users Based Cognitive Radio Networks

In cognitive radio networks (CRNs), primary users (<i>PU</i>s) have the highest priority in channel resource allocation. Secondary users (<i>SU</i>s) can generally only utilize temporarily unused channels of <i>PU</i>s, share channels with <i>PU</i>s, or cooperate with <i>PU</i>s to gain priority through the interweave, underlay, and overlay modes. Traditional optimization schemes for channel resource allocation often lead to structural wastage of channel resources, whereas approaches such as reinforcement learning-though effective-require high computational power and thus exhibit poor adaptability in industrial deployments. Moreover, existing works typically optimize a single performance metric with limited scenario scalability. To address these limitations, this paper proposes a CR network algorithm based on the hybrid users (HU) concept, which links the Interweave and Underlay modes through an adaptive threshold for mode switching. The algorithm employs the Hungarian method for <i>SU</i> channel allocation and applies a multi-level power adjustment strategy when <i>PU</i>s and <i>SU</i>s share the same channel to maximize channel resource utilization. Simulation results under various parameter settings show that the proposed algorithm improves the average signal to interference plus noise ratio (SINR) of <i>SU</i>s while ensuring <i>PU</i> service quality, significantly enhances network energy efficiency, and markedly improves Jain's fairness among <i>SU</i>s in low-power scenarios.