Optimization of ber in cognitive radio networks using fhss and energy-efficient obrmb spectrum sensing Article

Aulakh, IK, Singh, S, Kaur, N et al. (2016). Optimization of ber in cognitive radio networks using fhss and energy-efficient obrmb spectrum sensing . 16(2), 297-324. 10.17654/EC016020297

cited authors

  • Aulakh, IK; Singh, S; Kaur, N; Vig, R



  • Radio spectrum management is a critical issue for future wireless communications. Spectrum management authorities such as FCC in USA are making efforts to release portions of fixed spectrum to telecom industry to provide unlicensed access for wireless services. However, just freeing up such spectrum would not be able to satisfy the needs of future communications. The existing technologies are not capable of utilizing the complete strength of spectrum. Cognitive radio is one technology which helps to optimally utilize the spectrum resources. As the world is becoming more and more aware of conserving energy, researchers are more interested in technologies which reduce the amount of energy required to provide desired services. In this paper, we have focussed our efforts on the energyefficiency aspect of spectrum sensing in cognitive radios. Also, a new bit-exchange mechanism is proposed for cooperative sensing which helps to reduce the control overheads. To complete the process of spectrum sensing, a pair of bit-request and bit-reply signal is required. As spectrum sensing is a continuous process, multiple bit-requests are required to be generated to get detection results at multiple instances. This significantly increases the overhead cost and consumes more energy as more request bits are transmitted. To reduce the transmission of unnecessary request bits, an improved spectrum sensing model is proposed in this paper. Also, the performance of a cognitive radio is analyzed when collaborated with some signal modulation technique such as DSSS and FHSS. One-bit cooperative spectrum sensing based on the proposed model is used as a mechanism to implement working of a cognitive radio.

publication date

  • June 1, 2016

Digital Object Identifier (DOI)

start page

  • 297

end page

  • 324


  • 16


  • 2