Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks

Abstract

The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of quality of service (QoS) are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This paper presents a novel, Bayesian, non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bitrate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.Index Terms-Multi-channel cognitive radio networks, quality of service levels, multimedia transmissions, channel clustering.

Trekking based distributed algorithm for opportunistic spectrum access in infrastructure-less network

“…, N − 1 i.e., gap between i th and (i+1) th channel statistics and assume ∆ i > 0. Similar assumptions have been made in many recent works [2], [3], [7]- [10], [13], [15], [18], [20].…”

“…In this section, we present the well-known network model for infrastructure-less (or ad-hoc) CRN which has been considered in many recent works including [2], [3], [7]- [18], [20]. It consists of U SUs and N channels in the wideband licensed spectrum such that N ≥ U .…”

“…T HE need to increase the utilization of an electromagnetic spectrum has always been a concern for the service operators. With emerging paradigms such as the Internet of Things (IoTs) and smart grids consisting of thousands of devices transmitting intermittently, it will be difficult to follow static spectrum allocation policies due to limited spectrum below 6 GHz and high spectrum costs [1]- [3]. Hence, industry as well as academia are exploring various approaches such as opportunistic spectrum access (OSA), device-to-device communications, cellular-to-WiFi offloading and LTE-unlicensed (LTE-U) to meet the spectrum needs of these paradigms [1]- [5].…”

“…The OSA becomes more challenging in dynamic networks where the SUs can enter or leave the network anytime without prior agreement. Such network are also referred to as cognitive adhoc and sensor networks consisting of hundreds of transmitting devices (analogous to SUs), but only a few of them are active at a time [2], [3]. We develop algorithms to enable collisionfree communication between such devices without the need of any control channel or coordination via the central controller.…”

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

“…A secondary opportunistic access pattern to the shared licensed bands exacerbates the time-varying feature of the free channels. This is considered as a severe technical issue to perfectly match multimedia content with the channel resources, which increases PLR and reduce QoE [293]. Therefore, the available channels change dramatically and the reliability of the multiple accessed CR bands are also time-varying.…”