Optimal Sensing Policy for Energy Harvesting Cognitive Radio Systems

Abstract

Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario. Index Terms-Energy harvesting, cognitive radio, optimal processing scheduling, offline power allocation policies, online power allocation policies.

Opportunistic Throughput Optimization in Energy Harvesting Dynamic Spectrum Sharing Wireless Networks

“…In this paper, we investigated an energy-harvesting wireless network where multiple SARs perform CSS to make decisions regarding the presence of PU within a limited time Norm. transmission time (T t /T F ) Energy efficiency (bits/J/Hz) Proposed EH-CCRN [23] OSA-ES [24] OP-QMF [25] TO-HDA [26] FIGURE 12. The comparison of EE in energy-surplus mode against normalized transmission time between the proposed algorithm and algorithms EH-CCRN in [23] and OSA-ES [24], OP-QMF [25], TO-HDA in [26] for N = 20, T F = 10 ms duration.…”

“…It is important to note that sensing quality is of paramount importance in OSA networks. Therefore, the problem should be solved while considering the sensing performance constraints (24) and (25), which can be formulated as follows:…”

“…Figure 12 presents a comparison of EE in energy-deficit mode between our proposed algorithm and the algorithms EH-CCRN [23], OSA-ES [24], OP-QMF [25], and TO-HDA [26] with parameters set to N = 20 and T F = 10 ms. Initially, for short transmission time slots where our proposed algorithm faces the decision of energy harvesting or transmission, it exhibits lower EE compared to the other two algorithms.…”

“…In [24], the authors delve into optimizing spectrum sensing intervals to balance energy harvesting and spectrum access, taking into account PU traffic statistics. Furthermore, an analysis of an OSA network, where SARs harvest energy while accessing the licensed spectrum, presents a model to maximize SAR network throughput [25], [26].…”

An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

“…For CRSNs nodes, lower false alarm probability means a greater chance of leveraging idle licensed spectrum for communication. Thus, guaranteeing high detection probability and low false alarm probability plays a key role in enhancing the system performance 6 . In order to alleviate the negative impact of imperfect spectrum sensing on the network performance, by giving full consideration to spectrum sensing capability of CRSNs nodes, an imperfect spectrum sensing-based multi-hop clustering routing protocol (ISSMCRP) is proposed for CRSNs in this paper.…”