Abstract
This paper investigates a multi-channel access strategy for cognitive radio networks (CRNs) under bursty traffic conditions, with a focus on congestion control. The proposed approach integrates cross-layer factors including channel fading, user activity, and finite cache capacity and models heterogeneous burst service arrivals using a two-state Markov-modulated Bernoulli process (MMBP-2). A dual-threshold mechanism is implemented in the node buffer to effectively manage congestion. System states are mapped onto a two-dimensional discrete Markov chain, where state transitions are characterized by a high-dimensional transition matrix. Through steady-state analysis, key performance metrics such as average queue length, throughput, delay, and packet loss rate are derived. Simulation results confirm that the model achieves stable operational performance. Building upon this framework, this paper proposes a multi-channel access strategy that maximizes average throughput while minimizing packet loss rate by employing a genetic algorithm. The results show that, in comparison with traditional strategies, the burst flow control model developed in this study effectively meets data access requirements in highly bursty environments. Furthermore, simulation experiments explore how system performance varies with changes in the number of channels and cognitive users, and the key operational threshold is determined. These findings offer valuable guidance for channel access design and capacity planning in burst communication scenarios.