Performance Analysis of a Cognitive Radio Network with a Buffered Relay (original) (raw)

Impact of Interference from Primary User on the Performance of Cognitive Radio Networks

2012

This thesis report presents background knowledge about cognitive radio network (CRN) and investigates performance of underlay cognitive radio networks based on an adaptive power allocation policy of secondary transmitter (SU-Tx). In particular, it has been assumed that SU-Tx and primary user transmitter (PU-Tx) are equipped with a single antenna, while the corresponding receivers are equipped with multiple antennas. Additionally, SU-Tx operates under the joint constraint of its peak transmission power and outage constraint of the primary network. The probability density function (PDF) and cumulative density function (CDF) of the signal to interference and noise ratio (SINR) of SU over Rayleigh fading channel are derived. Using these two functions, a closed-form expression for the outage probability and an approximate expression for ergodic capacity of the considered system are obtained. Matlab simulation results are provided to validate the correctness of the analyses. The results s...

Primary User Aided Cognitive Radio Network With Optimum Location Of Relay

International Journal of Scientific & Technology Research, 2020

In this paper, we compare the normalized channel capacity of single user and multiuser models of primary user (PU) aided cognitive radio network (CRN). The single user model gives the best performance; however, it often fails to represent the real life environment. On the other hand, multi-user model represents more realistic scenarios of wireless links; however, its performance is worse than that of single user. Therefore, we propose a new communication model for selecting the appropriate PUs as relay nodes that is based on the geometry of service area, the offered traffic of PUs and the existence probability of PUs in an appropriate position. Through simulation experiment, we prove that the proposed model gives more realistic results compared to the previous works on multi-user model in CRN.