







Vol.4 , No. 6, Publication Date: Dec. 26, 2017, Page: 44-50
[1] | Mohan Premkumar, Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India. |
[2] | Muthappa Perumal Chitra, Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India. |
In this paper capacity of a cognitive radio (CR) system enabled with multiple input multiple output (MIMO) technology is derived and simulated under flat fading channel situation. Multiple input multiple output cognitive radio system provides effective spectrum utilization along with increased diversity gain as more number of antennas are used in the transmitting and receiving terminals. Derivation of capacity is done in flat fading channel situation following Rayleigh distribution. From the derived capacity formula, simulation is performed for different transmitting and receiving antennas and it is analyzed for the amount of information bits which a flat fading channel can process. This analysis will contribute for designing and developing MIMO CR systems which can be employed for wireless applications in smart cities.
Keywords
Capacity, MIMO, Cognitive Radio, Flat Fading Channel
Reference
[01] | J. Mitola III “Cognitive Radio”, Licentiate Proposal, KTH Stockhom, Sweden 1998. |
[02] | J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: Making Software Radios more personal”, IEEE Personal Communications, vol. 6. no. 4, pp-13-18, August 1999. |
[03] | S. Haykin, “Cognitive radio: Brain-empowered wireless communications”, IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, Feb 2005. |
[04] | S. Alamouti,”A simple transmit diversity technique for wireless communications” IEEE J. Sel. Areas commun., vol. 16, no. 8. pp. 1451-1458, Oct 1998. |
[05] | C. X. wang, X. Hong, H. H. Chen and J. Thompson, “On capacity of cognitive radio networks with average interference power constraints” IEEE Transactions on Wireless Communications, vol. 8, no. 4, pp. 1620-1625, April 2009. |
[06] | E. A. Gharavol, Y. C. Liang, K. Mouthaan, “Robust Cooperative Nonlinear Transceiver Design in Multi-Party MIMO Cognitive Radio Networks with Stochastic Channel Uncertainty”, IEEE 72nd Vehicular Technology Conference Fall (VTC 2010-Fall), Sept 2010. |
[07] | Asaduzzaman H. Y. Kong, “Ergodic and outage capacity of interference temperature-limited cognitive radio multi-input multi-output channel” IET Communications, vol 5, issue: 5, pp. 652-659, March 2011. |
[08] | Z. Xiong, C. Gong, L. Wu, K. Cumanan; S. Lambotharan,“Capacity Balancing for Multiuser MIMO Cognitive Radio Network”, IEEE Vehicular Technology Conference (VTC Fall), Sept 2011. |
[09] | J. T. Wang, “Maximum–Minimum Throughput for MIMO Systems in Cognitive Radio Networks”, IEEE Transactions on Vehicular Technology vol. 63, issue: 1, pp. 217-224, Jan. 2014. |
[10] | M. Hefnawi and A. Abubaker, “Channel capacity maximization in MIMO-SDMA based cognitive networks”, in Proceedings of International Conference on Multimedia Computing and Systems (ICMCS), April 2014. |
[11] | P. S. Kale and P. Lohiya, “Performance Enhancement of cognitive radio spectrum sharing (CRSS) with MIMO system”, in Proceedings of International Conference on Computer, Communication and Control (IC4), Sept 2015. |
[12] | M. Hefnawi,“Large-Scale Multi-Cluster MIMO Approach for Cognitive Radio Sensor Networks”, IEEE Sensors Journal vol. 16, issue: 11, pp. 4418-4424, June, 2016. |
[13] | N. I. Miridakis, T. A. Tsiftsis, G. C. Alexandropoulos, M. Debbah, “Simultaneous Spectrum Sensing and Data Transmission for Multi-User MIMO Cognitive Radio Systems” in Proceedings of IEEE Global Communications Conference (GLOBECOM), Dec 2016. |
[14] | S. Haykin, “Digital Communication”, John Wiley & Sons, 1998. |
[15] | D. Tse and P. Viswanath, “Fundamentals of Wireless Communication”, Cambridge University Press, 2005. |
[16] | S. M. Kay, “Statistical Signal Processing: Vol. 1 Estimation Theory”, Prentice Hall, 1993. |