Advanced Spectrum Sensing Techniques

Advanced Spectrum Sensing Techniques

P. T. V. Bhuvaneswari Mohan (Anna University, India) and Bino Jesu Stephen (Anna University, India)
Copyright: © 2019 |Pages: 9
DOI: 10.4018/978-1-5225-5354-0.ch008

Abstract

Spectrum sensing plays an important role in cognitive radio networks. Based on the bandwidth considered for spectrum sensing, it can be classified into narrowband and wideband spectrum sensing. Traditional spectrum sensing methods are devised for narrow band as it focuses on narrow frequency a range that is the channel bandwidth is lesser than the coherence bandwidth of the channel. To provide more spectral opportunity to cognitive user and to increase the throughput, cognitive radio network needs techniques that exploits spectral opportunities over a wide frequency range. Wideband spectrum sensing techniques aim to sense a channel bandwidth that exceeds the coherence bandwidth of the channel. Narrowband sensing techniques cannot be directly employed to perform wideband spectrum sensing as they make a single binary decision. In this chapter, the advanced spectrum sensing techniques and their taxonomy are discussed in detail.
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Limitations In Narrow Band Sensing

Time consumption involved in sensing is considered as one of the major limitation of Narrowband sensing. It limits the local observations which lead to incomplete sensing information. The traditional narrowband spectrum sensing techniques are Matched filtering, energy detection, Cyclostationary detector and covariance based detector (Sun, Nallanathan, Wang, & Chen, 2013). Matched filtering technique (Jaiswal, Kumar Sharma & Singh, 2013) correlates the known reference signal with the received signal to detect the presence of Primary User (PU). However, it requires prior knowledge of PU signal and need to maintain synchronisation. In energy detection technique (Digham, Alouini, & Simon, 2007), the received signal energy is compared with a threshold to detect the presence of PU. When compared to the previous technique it does not require any prior information about PU. However due to the stochastic nature of wireless channel, it is cumbersome to differentiate PU from CR user. Cyclostationary detector (Sutton, Lotze, Nolan, & Doyle, 2007) utilizes the cyclic correlation property of modulated signals. It performs well in low SNR regions. It consumes more time for spectrum sensing and it is computationally complex. In covariance based detector (Zeng & Liang, 2009), covariance matrix of the received signal is used to detect the signals. It utilizes the dispersive nature of channels. It is also affected by noise uncertainty.

Significance of Wideband Sensing

Wideband spectrum sensing techniques aim to sense a channel bandwidth that exceeds the coherence bandwidth of the channel. The excessive bandwidth increases the hardware complexity. Wideband sensing can detect more amount of unused spectrum of PU and provides more spectral opportunities to CR users. This not only increases the throughput of CR user but also allows them to switch between various spectrum holes. This provides seamless communication and thereby enhances the QoS of CR users.

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