Antenna Selection and Detection Performance on Correlation Based Detection Systems

Abstract

The increase data size transmitted in digital communication systems necessitates the use of high-capacity communication channels for the transmission of these data. To increase the channel capacity, MIMO systems are widely used. Antenna selection in MIMO systems is very important for efficient use of communication sources. Cognitive Radio systems generally aim to increase spectrum efficiency by using MIMO systems. Therefore, Cognitive Radio must find empty spectrum regions with spectrum detection methods. Spectrum detection performance is related to the antenna numbers in MIMO systems. Therefore, this study examines the effect of antenna numbers on detection performance in correlation-based detection method. Thanks to this study, the relationship between channel capacity, detection performance and antenna number for MIMO systems was investigated.

Keywords

• Antenna selection
• Cognitive radio
• Correlation based Spectrum Sensing
• MIMO systems

References

1. [1] B. B. Pradhan and L. P. Roy, “Ergodic capacity and symbol error rate of distributed massive MIMO systems over Rayleigh-inverse Gaussian fading channels using ZF detectors,” Phys. Commun., vol. 38, no. 77, p. 7777, Feb. 2020, doi: 10.1016/J.PHYCOM.2019.100906.
2. [2] E. Dahlman, S. Parkvall, and J. Skold, 4G: LTE/LTE-Advanced for Mobile Broadband. 2013.
3. [3] M. Kang and M. S. Alouini, “Capacity of MIMO Rician channels,” IEEE Trans. Wirel. Commun., vol. 5, no. 1, pp. 112–122, 2006, doi: 10.1109/TWC.2006.1576535.
4. [4] Y. Li, J. H. Winters, and N. R. Sollenberger, “MIMO-OFDM for wireless communications: Signal detection with enhanced channel estimation,” IEEE Trans. Commun., vol. 50, no. 9, pp. 1471–1477, 2002, doi: 10.1109/TCOMM.2002.802566.
5. [5] M. Kowal, S. Kubal, P. Piotrowski, and R. Zieliński, “A simulation model of the radio frequency MIMO-OFDM system,” Int. J. Electron. Telecommun., vol. 57, no. 3, pp. 323–328, 2011, doi: 10.2478/v10177-011-0043-6.
6. [6] C. Çiflikli and F. Y. Ilgin, “Covariance Based Spectrum Sensing with Studentized Extreme Eigenvalue,” Tech. Gaz., vol. 25, no. 6, pp. 100–106, 2018.
7. [7] J. Wang, M. Ghosh, and K. Challapali, “Emerging cognitive radio applications: A survey,” IEEE Commun. Mag., vol. 49, no. 3, pp. 74–81, 2011, doi: 10.1109/MCOM.2011.5723803.
8. [8] M. Matthaiou, M. R. McKay, P. J. Smith, and J. A. Nossek, “On the condition number distribution of complex wishart matrices,” IEEE Trans. Commun., vol. 58, no. 6, pp. 1705–1717, 2010, doi: 10.1109/TCOMM.2010.06.090328.

9. [9] X. Liu, X. Zhang, H. Ding, and B. Peng, “Intelligent clustering cooperative spectrum sensing based on Bayesian learning for cognitive radio network,” Ad Hoc Networks, vol. 94, 2019, doi: 10.1016/j.adhoc.2019.101968.
10. [10] P. D. Arapoglou, K. Liolis, M. Bertinelli, A. Panagopoulos, P. Cottis, and R. De Gaudenzi, “MIMO over satellite: A review,” IEEE Commun. Surv. Tutorials, vol. 13, no. 1, pp. 27–51, 2011, doi: 10.1109/SURV.2011.033110.00072.
11. [11] N. Promsuwanna, P. Uthansakul, and M. Uthansakul, “Performance of antenna selection in MIMO system using channel reciprocity with measured data,” Int. J. Antennas Propag., vol. 2011, 2011, doi: 10.1155/2011/854350.
12. [12] Y. Zeng and Y. C. Liang, “Spectrum-sensing algorithms for cognitive radio based on statistical covariances,” IEEE Trans. Veh. Technol., vol. 58, no. 4, pp. 1804–1815, 2009, doi: 10.1109/TVT.2008.2005267.
13. [13] R. A. Davis, O. Pfaffel, and R. Stelzer, “Limit theory for the largest eigenvalues of sample covariance matrices with heavy-tails,” Stoch. Process. their Appl., vol. 124, no. 1, pp. 18–50, 2014, doi: 10.1016/j.spa.2013.07.005.
14. [14] Y. Zeng and Y. C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” IEEE Trans. Commun., vol. 57, no. 6, pp. 1784–1793, 2009, doi: 10.1109/TCOMM.2009.06.070402.
15. [15] Y. He, T. Ratnarajah, E. H. G. Yousif, J. Xue, and M. Sellathurai, “Performance analysis of multi-antenna GLRT-based spectrum sensing for cognitive radio,” Signal Processing, vol. 120, pp. 580–593, 2016, doi: 10.1016/j.sigpro.2015.10.018.