Performance analysis of energy detection algorithm in cognitive radio

Rapid growth of wireless applications and services has made it essential to address spectrum scarcity problem. if we were scan a portion of radio spectrum including revenue-rich urban areas, we find that some frequency bands in the spectrum are largely unoccupied most of the time, some other frequency bands are partially occupied and the remaining frequency bands are heavily used. This leads to a underutilization of radio spectrum, Cognitive radio (CR) technology attempts alleviate this problem through improved utilization of radio spectrum. Cognitive radio is a form of wireless communication in which a transceiver can intelligently detect which RF communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This optimizes the use of available radio-frequency (RF) spectrum while minimizing interference to other users. There two types of cognitive radio, full cognitive radio and spectrum-sensing cognitive radio. Full cognitive radio takes into account all parameters that a wireless node or network can be aware of. Spectrum-sensing cognitive radio is used to detect channels in the radio frequency spectrum. Spectrum sensing is a fundamental requirement in cognitive radio network. Many signal detection techniques can be used in spectrum sensing so as to enhance the detection probability. In this thesis we analyze the performance of energy detector spectrum sensing algorithm in cognitive radio. By increasing the some parameters, the performance of algorithm can be improved as shown in the simulation results. In cognitive radio systems, secondary users should determine correctly whether the primary user is absent or not in a certain spectrum within a short detection period. Spectrum detection schemes based on fixed threshold are sensitive to noise uncertainty, the energy detection based on dynamic threshold can improve the antagonism of noise uncertainty; get a good performance of detection while without increasing the computer complexity uncertainty and improves detection performance for schemes are sensitive to noise uncertainty in lower signal-to-noise and large noise uncertainty environments

Experimental detection using cyclostationary feature detectors for cognitive radios

© 2014 IEEE. Signal detection is widely used in many applications. Some examples include Cognitive Radio (CR) and military intelligence. Without guaranteed signal detection, a CR cannot reliably perform its role. Spectrum sensing is currently one of the most challenging problems in cognitive radio design because of various factors such as multi-path fading and signal to noise ratio (SNR). In this paper, we particularly focus on the detection method based on cyclostationary feature detectors (CFD) estimation. The advantage of CFD is its relative robustness against noise uncertainty compared with energy detection methods. The experimental result present in this paper show that the cyclostationary feature-based detection can be robust compared to energy-based technique for low SNR levels

Sensitive White Space Detection with Spectral Covariance Sensing

This paper proposes a novel, highly effective spectrum sensing algorithm for cognitive radio and whitespace applications. The proposed spectral covariance sensing (SCS) algorithm exploits the different statistical correlations of the received signal and noise in the frequency domain. Test statistics are computed from the covariance matrix of a partial spectrogram and compared with a decision threshold to determine whether a primary signal or arbitrary type is present or not. This detector is analyzed theoretically and verified through realistic open-source simulations using actual digital television signals captured in the US. Compared to the state of the art in the literature, SCS improves sensitivity by 3 dB for the same dwell time, which is a very significant improvement for this application. Further, it is shown that SCS is highly robust to noise uncertainty, whereas many other spectrum sensors are not

Evaluation of Multi-Antenna Based on GLRT Approach on Cognitive Radio

Wireless traffic in fact is increasing, while spectrum already allocated. This problem can be solve using maximizing utilization band spectrum. One of technology that can maximizing spectrum is cognitive radio. Spectrum sensing is one component in cognitive radio (CR). There are 3 kind of detector that usually used in cognitive radio. That are, Matched Filter, Energy Detector and cyclostationary. The best one is energy detector. But, this sensing algorithm that have disadvantages, which is very sensitive to noise power uncertainty. Then formed a new method based on the GLRT approach that resist with noise power uncertainty. Evaluation algorithm GLRT Approach in this thesis such as; TAGM, TGLR, TSTBCGLRT, TEMR. In this thesis we analysis 4 aspect. First, analysis several parameter GLRT Approach on cognitive radio. Second, compare the energy detection methods with several algorithms. Third, GLRT approach analyzing advantages and disadvantages of each algorithm. Last, the algorithm uses a bootstrap type-3 are TAGM and TSTBCGLRT. The result are GLRT approach work be affected parameter such as; SNR, Number of Antenna, Channel Shape and STBC scheme. Algorithm GLRT approach can work under noise power uncertainty. It make algorithm GLRT approach solve the problem of Energy Detector. For all algorithm GLRT Approach, TSTBCGLRT is the best algorithm because suitable with PU signal and channel model. GLRT approach can be combined with bootstrap for detector type-3 to help determine Pd assumption. Furthermore, bootstrap can work without distribution H0 is known and fix threshold. Because, bootstrap is only resampling data from signal exist. In order hand, bootstrap can work with small of data number to get assumption Pd

A Real Time Radio Spectrum Scanning Technique Based On The Bayesian Model And Its Comparison With The Frequentist Technique

The proliferation of mobile devices led to an exponential demand for wireless radio spectrum resources. The current fixed spectrum assignment has caused some portions of the radio spectrum to be heavily used whereas others to be scarcely used. This has resulted in underutilization of spectrum resources, and, hence has demanded the need for solutions to address the spectrum scarcity problem. Cognitive radio was proposed as one of the solutions. One of the techniques involved in cognitive radio is the dynamic spectrum access technique. This technique requires the identification of free channels in order to allow secondary users to exploit the spectrum resources. The process of identification of free channels is known as radio spectrum scanning, which is performed by sensing a particular channel in the radio spectrum to determine the presence or absence of a signal. In most of existing studies, the frequentist technique using energy detection with fixed threshold was used to scan the radio spectrum. However, this method comes with a major drawbacks. First, energy detection is unable to distinguish between signals and noise and suffer for high false detection rates. Second, energy detection has high false alarm probability. Finally, frequentist techniques are subject to uncertainty and do not provide real time monitoring/sensing. Therefore, the goal of this thesis is to develop a more efficient scanning technique that deals with uncertainty and scans the radio spectrum in real time and determines its occupancy levels. An enhanced spectrum scanning approach is developed using an efficient spectrum sensing technique: an uncertainty handling Bayesian model along with a Bayesian inferential approach. Two Bayesian models are developed: 1) a simplified model, and 2) an improved model to incorporate the Bayesian inferential approach to estimate the spectrum occupancy level. The performance evaluation of the proposed technique has been done using simulations as well as real experiments. For this purpose, two metrics were used: probability of detection and probability of false alarm. Furthermore, the efficiency of the proposed technique was compared to the efficiency of the frequentist technique, which uses only a spectrum sensing technique to identify the occupancy of the spectrum channels. As expected significant improvements in the spectrum occupancy measurements have been observed with the proposed Bayesian inference method

A HOS-Based Blind Spectrum Sensing in Noise Uncertainty

Spectrum sensing for cognitive radio is a challenging task since it has to be able to detect the primary signal at a low signal to noise ratio (SNR). At a low SNR, the variance of noise fluctuates due to noise uncertainty. Detection of the primary signal will be difficult especially for blind spectrum sensing methods that rely on the variance of noise for their threshold setting, such as energy detection. Instead of using the energy difference, we propose a spectrum sensing method based on the distribution difference. When the channel is occupied, the distribution of the received signal, which propagates under a wireless fading channel, will have a non-Gaussian distribution. This will be different from the  Gaussian noise when the channel is vacant. Kurtosis, a higher order statistic (HOS) of  the  4th order,  is used as normality test for the test statistic. We measured the detection rate of the proposed method by performing a simulation of the detection process. Our proposed method's performance proved superior in detecting a real digital TV signal in noise uncertainty

Hard Decision Fusion based Cooperative Spectrum Sensing in Cognitive Radio System

Cooperative spectrum sensing was proposed to combat fading, noise uncertainty, shadowing, and even hidden node problem due to primary users (PUs) activity that is not spatially localized. It improves the probability of detection by collaborating to detect PUs signal in cognitive radio (CR) system as well. This paper studies cooperative spectrum sensing and signal detection in CR system by implementing hard decision combining in data fusion centre. Through computer simulation, we evaluate the performances of cooperative spectrum sensing and signal detection by employing OR and AND rules as decision combining. Energy detector is used to observe the presence of primary user (PU) signal. Those results are compared to non-cooperative signal detection for evaluation. They show that cooperative technique has better performance than non-cooperative. Moreover, signal to noise ratio (SNR) with greater than or equal 10 dB and 15 collaborated users in CR system has optimal value for probability of detection