Review of Energy Detection for Spectrum Sensing in Various Channels and Its Performance for Cognitive Radio Applications

Abstract

Spectrum sensing is the basic and important operation in Cognitive Radio (CR) to find the unused spectrum. Energy detector is a popular sensing method because it doesn't require transmitted signal properties, channel information, of even the type of modulation. This study summarizes the performance result of energy detector over Additive White Gaussian Noise (AWGN), Rayleigh fading and Nakagami fading channels. Energy detection with soft decision and hard decision are also studied for different number of cognitive nodes as well as each cognitive node having multiple antennas. The performance of hard decision and soft decision are evaluated by means of complementary Receiver Operating Characteristic (ROC) curves. It clearly shows that the probability of missing detection decreases for increasing the number of antennas in cognitive node and also increasing of cooperated cognitive users.

Analysis of the effectiveness of object coordinates estimation in WSN using the RSS method

“…Let us take into account that in each sensor node, the processing of the signal coming from the observed object is based on the use of an energy receiver [21]. In addition, let us consider that the amplitude of the signal arriving at the k-th sensor can be represented in the form 𝑎 = 𝑃 𝑑 /𝑑 , where 𝑃 is the power of the signal arriving at the reference point with a known distance to the target 𝑑 .…”

Predicting the Probability of Spectrum Sensing with LMS Process in Heterogeneous LTE Networks

“…For this, the sensing metrics are presented such as the detection probability P d and false alarm probability P fa [18], [33], [34], [35], [36]:…”

“…The output of this method gives a decision value compared with  that represents the decision of the energy threshold; if it is above the threshold, then the sensing of the primary user is present. Furthermore, the aim of this detection is to distinguish the following binary hypotheses [17], [34], [35], [36] (20) where T is the sensing time interval (or observation time interval) in seconds, W bandwidth in Hz.…”