A New Probabilistic Target Channel Selection Approach for Load Balancing in Cognitive Radio Networks

Abstract

Cognitive radio is a technology targeting better utilization for the available frequency bands by allowing the unlicensed secondary users (SUs) to access the licensed frequency bands of the primary users (PUs). The SU vacates the channel at the presence of the PU and selects a target channel reactively or proactively to complete his interrupted transmission. Target channel sequence order can greatly affect the performance of the network in terms of delay, capacity, and utilization. The probabilistic sequence target channel selection approach was shown to distribute the load smoothly among the available channels resulting in good load balancing characteristics and increasing network capacity and stability for an appropriate distribution function. In this paper, a new proactive probabilistic sequence approach to determine the target channel for the interrupted SUs is proposed to achieve load balancing. The pre-emptive resume priority M/M/1 queueing network model is used to analyse the proposed approach to evaluate the latency and load balancing performance. Moreover, a new quantitative performance measure for balancing is introduced. The proposed approach is compared to other alternative spectrum handoff strategies and numerical results show the advantage of the proposed probabilistic sequence approach by improving balancing and channel capacity. Moreover, the extended data delivery time is improved at low network loads.