Operating characteristics of underlay cognitive relay networks

Understanding the performance of cognitive relay networks (CRNs) is of great interest. Recently, stochastic geometry is being used to model and characterize the performance of CRNs. It is a known fact that sensing is an integral part of the CRN, however, in most cases it is not perfect. Moreover, the model inaccuracies caused by simplifications and/or approximations when deriving the analytical expressions for characterizing CRNs may distort their true performance. With no sensing in the system, we determine a lower performance bound (LPB) that can be used to judge the reliability of other systems that include sensing and model approximations. Based on the LPB, the operating characteristics (OC) for the CRN are obtained, which determine the joint performance of the primary and secondary system. Finally, OC are used to investigate the system performance under different scenarios.

[1]  Jiaru Lin,et al.  Transmission Capacity of Cognitive Radio Networks with Interference Avoidance , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[2]  Sachitha Kusaladharma,et al.  Impact of Beacon Misdetection on Aggregate Interference for Hybrid Underlay-Interweave Networks , 2013, IEEE Communications Letters.

[3]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[4]  Friedrich Jondral,et al.  Cognitive Relay: Detecting Spectrum Holes in a Dynamic Scenario , 2013, ISWCS.

[5]  Kerstin Vogler,et al.  Table Of Integrals Series And Products , 2016 .

[6]  Friedrich Jondral,et al.  On the deployment of Cognitive Relay as underlay systems , 2014, 2014 9th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM).

[7]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[8]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

[9]  Martin Haenggi,et al.  Interference and Outage in Poisson Cognitive Networks , 2012, IEEE Transactions on Wireless Communications.

[10]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[11]  M. Haenggi,et al.  Interference in Large Wireless Networks , 2009, Found. Trends Netw..

[12]  Danpu Liu,et al.  Spatial Throughput Characterization in Cognitive Radio Networks with Threshold-Based Opportunistic Spectrum Access , 2013, IEEE Journal on Selected Areas in Communications.

[13]  Holger Jaekel,et al.  Adaptive Frequency Hopping in Ad Hoc Networks with Rayleigh Fading and Imperfect Sensing , 2012, IEEE Wireless Communications Letters.

[14]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[15]  Martin Haenggi,et al.  Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[16]  Amir Ghasemi,et al.  Interference Aggregation in Spectrum-Sensing Cognitive Wireless Networks , 2008, IEEE Journal of Selected Topics in Signal Processing.

[17]  Sachitha Kusaladharma,et al.  Aggregate Interference Analysis for Underlay Cognitive Radio Networks , 2012, IEEE Wireless Communications Letters.