Optimal sensing energy and sensing interval in cognitive radio networks with energy harvesting

Abstract

Summary Cognitive radio networks with energy harvesting aim to improve spectrum utilization and allow users to move freely. Cognitive radio technology enhances the utilization of the spectrum by permitting the unlicensed user (secondary user) to use the channel during the absence of the licensed user (primary user). A major issue in this approach is that the secondary user needs to sense the channel frequently, which leads to energy wasting. This problem can be dealt with using the notion of sensing interval where the channel is sensed every multiple of time slots. In this paper, we consider the problem of determining the optimal sensing energy and the optimal sensing interval that maximize the secondary user throughput and minimize both the consumed energy and the interference to the primary user. By formulating this optimization problem as a mixed observable Markov decision process, a dynamic policy for the secondary user is generated taking into account the total accumulated secondary user reward. Numerical results show that the proposed policy outperforms existing results, especially when the secondary user prefers the transmission mode to the energy‐saving mode. Additionally, the numerical results show that applying sensing interval concept for both idle and busy sensing results is better than applying it for the idle case only.

A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network

“…In [19], the harvesting interval and the transmission interval are optimized to maximize the total achievable throughput of cognitive radio networks to obtain the maximum total achievable throughput. The sensing interval problem of the idle and busy channels in the EHbased CR network was formulated in [20]. A Markov chain was developed to find the energy state transition probability to solve the energy wastage problem.…”

“…From the literature, it is seen that spectrum sensing optimization has been extensively studied, and most of the researchers emphasized optimizing the trade-off for spectrum sensing and throughput in EHCRN by solving it as a convex optimization problem [18]- [20]. Despite the advances mentioned above, there is still a trade-off between throughput and sensing in EHCRN with constraints on interference and energy [21].…”