Cluster-based cooperative subcarrier sensing using antenna diversity-based weighted data fusion

Article English OPEN
Mughal, Bushra ; Hussain, Sajjad ; Ghafoor, Abdul (2016)

Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme.
  • References (30)
    30 references, page 1 of 3

    1. Mitola J. III. Software radios-survey, critical evaluation and future directions. In: IEEE National Telesystems Conference; 1992; Washington, DC, USA: IEEE. pp. 15 - 23.

    2. Mitola J, Maguire GQ. Cognitive Radio: Making Software Radios More Personal. IEEE Personal Communications 1999; 6: 13 - 18.

    3. Cordeiro C, Challapali K, Birru D, Shankar N. IEEE 802.22: The rst worldwide wireless standard based on cognitive radios. In: IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks Conference; 2005; Baltimore, USA: IEEE. pp. 328 - 337.

    4. Digham F, Alouini M, Simon M. On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications 2003; 5: 3575 - 3579.

    5. Sobron I, Diniz PSR, Martins WA, Velez M. Energy Detection Technique for Adaptive Spectrum Sensing. IEEE Transactions on Communications 2015; 63: 617 - 627.

    6. Urkowitz H. Energy detection of unknown deterministic signals. Proceedings of the IEEE 1967; 55: 523 - 531.

    7. Xuan F, Ying Z, Jian Y, Yifan Z, Zhiyong F. Simpli ed cyclostationary detector using compressed sensing. In: Wireless Communications and Networking Conference; 2015; New Orleans, LA: pp. 259 - 264.

    8. Eduardo, Avendano F, Caballero, Rene G G. Experimental evaluation of performance for spectrum sensing: Matched lter vs energy detector. In: IEEE Colombian Conference on Communications and Computing; 2015; Popayan, Colombia: pp. 1 - 6.

    9. Dhope TS, Simunic D. Performance analysis of covariance based detection in cognitive radio. In: International Convention MIPRO; 2012; Opatija: pp. 737 - 742.

    10. Zeng Y, Liang Y, Pham T. Spectrum Sensing for OFDM Signals Using Pilot Induced Auto-Correlations. IEEE Journal on Selected Areas in Communications 2013; 31: 353 - 363.

  • Similar Research Results (1)
  • Metrics
    No metrics available
Share - Bookmark