Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

Abstract

A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

Optimized channel prediction and auction‐based channel allocation for personal cognitive networks

“…The delay in findings vacant channel is represented as delay analysis in Figure 9. The existing channel selection algorithms like EXGPCSA 23 and GPCSA 23 are considered for comparative analysis. It can be observed from the results that the proposed auction‐based channel allocation algorithm performs better than existing methods.…”

“…The spectrum monitoring and prediction model reported in previous studies [22][23][24][25] addresses the spectrum mobility issues. The presented model considers reactive approach and its imperfect spectrum monitoring, and single packet loss issues for analysis.…”

Multi‐criteria approach for handling sophisticated data transmission over gateways in blockchain and Internet of Things (IoT) federated networks

“…Each time the proposed FDM scheduler invokes AHP based multi‐criteria method to find the best link for the transmission of data. The multi‐criteria approaches are proposed in literature to get the most appropriate resource (Bojovi'c et al, 2016; Tlouyamma & Velempini, 2021). The FDM scheduler in our proposed work attempts to attain fairness by optimizing throughput, packet delivery rate, data transmission rate and queuing delay.…”

“…In Bojovi'c et al (2016), the authors attempt to select the optimal channel for forwarding the IoT traffic to enhance the data transmission speed. In Tlouyamma and Velempini (2021), a new mechanism is proposed for forwarding the IoT traffic through diverse gateways using deep learning. The results obtained are promising for high‐volume data.…”

Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things

“…that can be used for aggregating information of occupancy in practical environment [9]. Another unique technique reported in existing scheme is Channel State Information (CSI) which is also capable of identifying the presence of human [10] [11]. Adoption of CSI not only ensures user privacy but also can aggregate multidimensional data associated with an occupant.…”