DCCC-MAC: A Dynamic Common-Control-Channel-Based MAC Protocol for Cellular Cognitive Radio Networks

Abstract

We propose a novel dynamic common control channel-based medium access control (DCCC-MAC) protocol for cellular (centralized) cognitive radio networks. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. During a given transmission frame, the common control channel (CCC) is selected by a cooperating set of secondary users (SUs) by using support vector machine (SVM)-based learning technique. In the DCCC-MAC protocol, the frame duration is divided into four main phases as follows: spectrum sensing phase, CCC selection phase, data transmission phase, and beaconing phase. The secondary users (SUs) who participate in the common control channel selection process are allocated channels for data transmission during a frame interval using a scheduling process, while the other SUs have to contend for accessing the channels. We present an analytical approach to calculate the minimum required number of mini-slots in the transmission frame for a given number of SUs in the CCC selection process. The saturation throughput of the proposed MAC protocol is analyzed in closed-form. To this end, the numerical and simulation results are presented to quantify the performance of the proposed DCCC-MAC protocol. We also compare the performance of the the DCCC-MAC protocol with that of two other state-of-theart cognitive radio MAC protocols which use common control channels.

A survey on MAC protocols for complex self-organizing cognitive radio networks

“…In the first phase, the coverage of the cognitive network is maximized while maintaining the constrained signal-to-interference-plus-noise ratio of primary transmissions. Thilina et al (2016) presents a dynamic CCC-based MAC protocol for centralized cellular CR networks. The CCC is dynamically selected by a fix number of SUs which participates in the CCC selection process.…”

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

“…As for the SVM's benefit in assisting decision making, in [227], a common control channel selection mechanism was conceived for SUs during a given frame relying on an SVM-based learning technique proposed for a cognitive radio network, which was capable of implicitly and cooperatively learning the surrounding environment cooperatively in an online way. Moreover, Yang et al [228] investigated the spoofing attack detection problem based on the spatial correlation of received signal strength gleaned from network nodes, where a cluster-based SVM mechanism was developed for determining the number of attackers.…”

Stochastic geometry model to analyze 5G energy efficiency based on a novel dynamic spectrum access scheme

“…Consequently, the problem of the SU collision faced in the work of Bohli and Bouallegue is avoided through the use of the DCCMC, leading to the synchronization of the entire SUs. Moreover, the faced issue in the work of Thilina et al is resolved thanks to the use of the CM responsible for the detection of the entire IS.…”

“…Consequently, the problem of the SU collision faced in the work of Bohli and Bouallegue 15 is avoided through the use of the DCCMC, leading to the synchronization of the entire SUs. Moreover, the faced issue in the work of Thilina et al 16 is resolved thanks to the use of the CM responsible for the detection of the entire IS. Indeed, when a typical user (MUE or SUE) detects that its SINR is under an SINR threshold, the sensing of its surrounding environment starts waiting for receiving the broadcasted beacons.…”