Multi-parameter performance analysis for decentralized cognitive radio networks
In this paper, we investigate the impact of primary user activity, secondary user activity, interface switching, channel fading and finite-length queuing on the performance of decentralized cognitive radio networks. The individual processes of these service-disruptive effects are modeled as Markov chains based on cross-layer information locally available at the network nodes. A queuing analysis is conducted and various performance measures are derived regarding the packet loss, throughput, spectral efficiency, and packet delay distribution. Numerical results demonstrate the impact of various system parameters on the system performance, providing insights for cross-layer design and autonomous decision making in decentralized cognitive radio networks.
KeywordsCognitive radio Decentralized networks User activity Interface switching Channel fading Adaptive modulation and coding Performance analysis
This paper has been partially funded by the CROSSFIRE (MITN 317126) and co-financed by the EU (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund.
- 3.Jondral, F. K. (2005). Software-defined radio—Basics and evolution to cognitive radio. EURASIP Journal on Wireless Communications and Networking, vol 2005(3). Article ID 652784.Google Scholar
- 6.Hossain, E., Niyato, D., & Han, Z. (2009). Dynamic spectrum access and management in cognitive radio networks. Cambridge: Cambridge University Press, ISBN: 978-0-521-89847-8.Google Scholar
- 19.Shankar, S. (2007). Squeezing the most out of cognitive radio: a joint MAC/PHY perspective. IEEE Conference on Acoustics Speech and Signal Processing (ICASSP), 4, 1361–1364.Google Scholar
- 21.Shi, Z., Beard, C., & Mitchell, K. (2012). Competition, cooperation, and optimizaion in multi-hop csma networks with correlated traffic. International Journal of Next-Generation Computing, 3(3).Google Scholar
- 22.Shi, Z., Beard, C., & Mitchell, K. (November 2011). Competition, cooperation, and optimization in multi-hop CSMA networks. In Proceedings of the 8th ACM symposium on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (PE-WASUN ‘11).Google Scholar
- 23.Felegyhazi, M., & Hubaux, J.-P. (2006). Game theory in wireless networks: A tutorial. EPFL—Switzerland, EPFL Technical report.Google Scholar
- 25.Nakagami, M. (1960). The m-distribution—A general formula for intensity distribution of rapid fading. In W. G. Hoffman (Ed.), Statistical methods in radio wave propagation. Oxford: Pergamon Press.Google Scholar
- 26.Cinlar, E. (1975). Introduction to stochastic processes. Englewood Cliffs: Prentice-Hall, ISBN: 0-13-498089-1.Google Scholar