Research on multi-channel access strategy based on congestion control with burst traffic in CRNs
Qianyu Xu, Qi Zhang, Yang Bi, Jaafar Gaber
TL;DR
This paper proposes a new multi-channel access strategy for cognitive radio networks that manages congestion during bursty traffic, improving performance in high-demand communication scenarios.
Contribution
A novel dual-threshold congestion control model with genetic algorithm optimization for multi-channel access in bursty environments.
Findings
The proposed model achieves stable performance with improved throughput and reduced packet loss in bursty traffic.
Simulation experiments show the model effectively adapts to varying numbers of channels and users.
The framework provides guidance for optimizing channel access design in burst communication scenarios.
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…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Network Traffic and Congestion Control · Wireless Networks and Protocols