L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Abstract

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary-and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channelavailability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks.

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

“…The learning process always needs to find a relation between the features to increase the learning regression accuracy [99]. For instance, using features of a physical layer, i.e., CAP, CQ, and LI can train it to estimate the link-throughput [88]. Hence, supervised first must train the algorithm on this data to learn how to estimate link-throughput for the smart routing protocol and that it can make the CU smarter to select the higher throughput link and can increase the routing efficiency.…”

“…The physical layer must be vigilant to any varying in licensed channel availability. Because of the mobility of CU and PU, physical layer jobs to detect the PU signal on the licensed channel become a daunting task [88]. Numerous studies have focused on spectrum sensing to identify PU signal [12].…”

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

“…For the proper operation of the CRN, the SU nodes must be able to sense the environment, select the best channel from the available choices for utilization, coordinate handoff in case of arrival of PU and ensure uninterrupted services [16]- [17]. It is also important for the SU to maintain the Quality of Service (QoS) above the minimum threshold during all these operations.…”

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

“…However, some literatures have considered channel quality as well as channel availability as important metrics for channel selection in CRNs. In [32], authors showed that channel availability as well as channel quality are the two significant metrics that should be considered when designing an efficient CSS. In their work, an L-CAQ channel selection scheme was design, which selects channel that jointly maximizes channel availability probability as well as channel quality.…”