Call-level performance evaluation and QoS provisioning in cognitive radio networks

Abstract

This paper investigates the call-level performance of a cognitive radio network for dynamic spectrum access providing multimedia service to secondary users. The serving system is modeled by a two-dimensional state-transition diagram and a novel approximate but computationally efficient analytical approach for solving its state probabilities is developed. A new precise formula for evaluation of the call dropping probability of the secondary users is derived. Channel limitation as a method for quality of service provisioning is suggested. Extensive simulation experiments are performed to validate the proposed analytical solutions. Numerical results are presented and some insightful conclusions are drawn.

Review on cross‐layer design for cognitive ad‐hoc and sensor network

“…Further, the delay Table 2 Overview of cross-layered survey Paper Year Network type Cross-layers parameters Observations [2] 2013 cognitive radio networks physical layer: spectrum sensing higher activity by PUs diminishes the chances of more transmission by SUs MAC layer: CSMA with collision avoidance [3] 2016 cognitive radio sensor network (CRAHSN) physical layer: spectrum sensing energy conservation, higher packet delivery ratio with adaptability to node mobility and PUs activity MAC layer: scheduling [4] 2012 cognitive radio networks physical layer: cooperative beam forming achieved cooperative diversity gain and improve QoS for SUs without consuming additional idle timeslots or temporal spectrum holes MAC layer: opportunistic scheduling scheme [5] 2016 MIMO wireless physical layer: AMC system performance is greatly dependent on traffic characteristic. However, they considered single user scheme for the ON-OFF arrival traffic model and queuing effect was the only data link layer factor chosen MAC layer: queuing [7] 2014 multihop cognitive radio ad-hoc network (CRAHNs) physical layer: spectrum sensing improved performance in terms of end-to-end delay, packet delivery ratio and energy consumption per packet MAC layer: spectrum decision network layer: spectrum selection [8] 2017 CRAHN physical layer: spectrum sensing simulation results demonstrated enhanced overall network performance by choosing the best channel based on SOP MAC layer: opportunistic link discovery network layer: opportunistic data transmission [9] 2014 CR mesh network physical layer: power control this cross-layer optimisation method took multi-radio CR model into account and considered number of usable orthogonal frequency channels, average estimated traffic between multiple source and destination nodes, and the effective capacity of the logical channels under SINR conditions MAC layer: channel allocation network layer: routing [10] 2015 CRAHN physical layer: power control achieved minimal interference to PUs, for given routing session in CRAHNs with a constraint of end-to-end average data rate for SU's communication MAC layer: channel assignment Network layer: routing [11] 2010 CR ad-hoc networks dynamic spectrum assignment, routing, power allocation and scheduling this can outperform simpler solutions having inelastic traffic [12] 2011 ad-hoc and infrastructure based CRNs physical layer: spectrum sensing considers CR throughput, QoS, call drop probability for SU and transmission delay. An improved technique to evaluate call dropping probability of SU is found MAC layer: CR capacity provisioning application layer: QOS provisioning [13] 2012 CR sensor network (CRAHSN) physical layer: sensing the performance of the spectrum sensing function can be enhanced with a collaborative multi-receptor system development MAC layer: decision making application layer: interface for data communication [14] 2016 cognitive 4G users physical layer: sensing used cross-layer optimisation to provide better throughput for SUs in the presence of PU channel occupancy MAC layer: opportunistic spectrum access and decision application layer: provide user requirement to l...…”

“…The paper [12] introduces the traffic capacity and the QoS provisioning in CR networks for dynamic spectrum access along with the spectrum overlay approach. Authors developed a crosslayer analytical model that jointly considers CR capacity and throughput, QoS provisioning, SU call dropping probability and maximum allowable transmission delay in the CRN.…”