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Abstract:  Spectrum sensing problem is a most challenging issue in the cognitive radio network. This paper gives the survey of narrowband and wideband spectrum sensing techniques. Different types of spectrum sensing methods are studied and they are compared interms of accuracy and complexity of spectrum sensing.

Keywords- Spectrum sensing; Cognitive radio network; narrowband spectrum sensing; wideband spectrum sensing.


Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing opportunistic usage of the frequency bands that are not heavily occupied by licensed users. Spectrum sensing is the main task in cognitive cycle and the main challenge to the CRs. In spectrum sensing studying the spectrum and find the unused bands and sharing it while avoiding the spectrum that is occupied by Primary user. To enhance the detection probability many spectrum detection techniques can be used. Spectrum sensing techniques are classified as narrow band spectrum sensing and wideband spectrum sensing. These techniques are discussed in section II and section III.


Narrow band spectrum sensing technique sequentially senses one channel at a time [1]. In this the frequency range is sufficiently narrow such that the channel frequency response can be considered flat and the bandwidth of the interest is less than the coherence bandwidth of the channel. The implementation of these narrowband algorithms requires different conditions. There are mainly three algorithms for narrow band spectrum sensing which are given below.

i) Energy detection based spectrum sensing.

ii) Matched filter based spectrum sensing.

iii) Cyclostationary feature detection based spectrum sensing.

The above spectrum sensing methods are also called as Transmitter detection or Non-cooperative detection spectrum sensing technique.

i) Energy Detection Based Spectrum Sensing

Energy detection is not only the best method for detecting any signal but it can also be used to detect the spectrum in cognitive radio network. In energy detection method the received signal is determined by observing and analyzing the spectrum’s signal strength [2]. Figure 1 shows the block diagram of the energy detection method.

The received signal is filtered and converted to digital form using Analog to Digital converter. The digital signal is then squared using squared device and this signal is integrated. The output that comes out of the integrator is the energy of the filtered received signal. This signal is compared with the threshold value to decide the presence of the primary user.

Energy detection can be implemented without any a priori information of the primary user signals hence it is not optimal

Figure 1: Energy detection method [3].

but it is simple to implement, so widely adopted.

ii) Matched Filter Based Spectrum Sensing

The matched-filtering method is an optimal approach for spectrum sensing since it maximizes the signal-to-noise ratio (SNR) in the presence of additive noise. This advantage is achieved by correlating the received signal with a template for detecting the presence of a known signal in the received signal [3]. Matched filter detection uses a priori knowledge of the received signal, such as frequency, bandwidth, modulation type and pulse shaping. Figure 2 shows the block diagram of the matched filter based spectrum sensing.

Figure 2: Matched filter based spectrum sensing [3].

Here the received signal and pilot signals are correlated. The pilot signal has a prior knowledge of presence of the primary signal. The correlated signal is compared with the threshold value. The detected signal gives the information about the presence of primary signal.

Matched filter relies on prior knowledge of the PUs and requires cognitive radios to be equipped with carrier synchronization and timing devices, leading to increased implementation complexity.

iii) Cyclostationary Feature Detection Based Spectrum Sensing

A signal is said to be cyclostationary if its mean and autocorrelation are a periodic function. Cyclostationary feature detector is one of the techniques of spectrum sensing which can differentiate the modulated signal from the additive noise [3]. Figure 3 shows the block diagram of cyclostationary feature detection.

Figure 3: Cyclostationary Feature Detection Based Spectrum Sensing [3].

The received signal undergoes spectral transformation using fast Fourier transform (FFT). The spectral transformed signal is then correlated to estimate the spectral correlation function (SCF). The spectrum is analyzed by searching for the unique cyclic frequency matching the peak in the SCF and deciding whether the signal of primary users are detected.

Cyclostationary feature detection can distinguish PU signal from noise, and used at very low Signal to Noise Ratio (SNR) detection by using the information embedded in the PU signal that are not present in the noise. Complexity of calculation is the main drawback of this method.


Wide and spectrum sensing technique senses more than two channels simultaneously. Wideband spectrum sensing is an essential functionality for cognitive radio networks. It enables cognitive radios to detect spectral holes over a wideband channel and to opportunistically use under-utilized frequency bands without causing harmful interference to primary networks [4].

Narrowband sensing techniques cannot be directly used for performing wideband spectrum sensing, because they make a single binary decision for the whole spectrum and thus cannot identify individual spectral opportunities that lie within the wideband spectrum. Many techniques have been employed for wide band spectrum sensing, among them one of the technique called cooperative wide band sensing is explained below.

i) Cooperative Wide Band Spectrum Sensing

The main idea of cooperative sensing is to enhance the sensing performance by exploiting the spatial diversity in the observations of spatially located CR users [5]. CR cooperative spectrum sensing occurs when a network of CRs share the sense information they gain for PU detection. This provides a more accurate spectrum sensing over the area where the CRs are located. In Cooperative sensing improves the sensing performance in the fading, shadowing and noise uncertainty.

The Cooperative Sensing is classified into three types

a) Centralized sensing,

b) Distributed Sensing,

c) Relay Assisted Sensing.

a) Centralized sensing:

In centralized cooperative sensing a Fusion Centre (FC) controls the process of cooperative sensing as shown in Figure 4. All secondary users send their sensing results to FC via control channel, and then FC combines the received signals and finds out the presence of primary user and sends back the decision to secondary users cooperating.

Figure 4: Centralized sensing [6].

b) Distributed Sensing

Distributed cooperative sensing does not depends on Fusion Centre for making the cooperative decision. Figure 5 shows the distributive cooperative sensing. In this all CR’s communicate each other sends their sensing data to each other and decides whether primary user is present or not by using a local criteria. If the criteria are not matched secondary users keeps sending their results to each other until the decision is finalized. This method takes several iterations to reach to a decision.

Figure 5: Distributed cooperative sensing [6].

c) Relay Assisted Sensing

Both sensing channel and report channel are not perfect, can complement and cooperate with each other to improve the performance of cooperative sensing [6]. Figure 6 shows the relay assisted sensing.

Figure 6: Relay Assisted Sensing [6].


Tabel 1: Comparison Of Spectrum Sensing Techniques

Energy Detector Matched Filter Cyclostationary Feature Detection Cooperative sensing

No prior information  of primary signal prior information  of primary signal prior information  of primary signal prior information  of primary signal

Transmitter Detection method Transmitter  Detection method Transmitter Detection method Receiver Detection method

Less complex complex complex complex

Less spectrum sensing accuracy More accurate compared to energy detector and cyclostationary feature detection method More accurate than energy detector Accurate spectrum sensing


Spectrum is a very valuable resource in wireless communication systems, and it has been a focal point for research and development efforts over the last several decades. Cognitive radio, which is one of the efforts to utilize the available spectrum more efficiently, has become an exciting and promising concept. One of the important elements of cognitive radio is sensing the available spectrum. In this paper, the spectrum sensing techniques are re-evaluated by considering different types of spectrum sensing methods and their comparison is given.


[1] ] H. Sun, D. Laurenson, and C.-X. Wang, “Computationally tractable model of energy detection performance over slow fading channels,” IEEE Commun. Letters, vol. 14, no. 10, Oct. 2010, pp. 924–926

[2] Saqib Saleem and Khurram Shahzad,    “Performance Evaluation of Energy Detection Based Spectrum Sensing Technique for Wireless Channel”, International Journal of Multidisciplinary Sciences and Engineering, Vol. 3, no. 5, pp. 31-34, May 2012.

[3] Hemlata Patil, Dr A.J.Patil, Dr S.G.Bhirud, “Multichannel Cooperative Sensing in Cognitive Radio: A literature Review”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 5, May 2015.

[4] Z. Tian and G. Giannakis, “A wavelet approach to wideband spectrum sensing for cognitive radios,” in Proc. IEEE Cognitive Radio Oriented Wireless Networks and Commun., Mykonos Island, Greece, June 2006, pp. 1–5.

[5] Tevfik Y¨ucek and H¨useyin Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE communications surveys & tutorials, vol. 11, no. 1, first quarter 2009.

[6] K. Ben Letaief, W. Zhang, Cooperative communications for cognitive radio networks, Proceedings of the IEEE 97 (5) (2009) 878–893.

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