Comparative Study of Energy Detection and Matched Filter Based Spectrum Sensing Techniques

2020 ◽  
Author(s):  
A Parvathy ◽  
Gayathri Narayanan
Keyword(s):  
Comparative Study ◽  
Spectrum Sensing ◽  
Matched Filter ◽  
Energy Detection

scholarly journals Estimation of Best Spectrum Sensing Concept in Cognitive Radio Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 1159-1163
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Specific Energy ◽  
Spectrum Sharing ◽  
Matched Filter ◽  
Energy Detection ◽  
Radio Networks ◽  
Radio System ◽  
Spectrum Detection

Cognitive radio is a versatile and sharp radio system learning that can naturally recognize accessible divert in a remote range and change correspondence parameters empower more data to run at the same time. Psychological radio is estimated as a point towards which a product characterized radio stage ought to create. The significant elements of CR incorporate Spectrum detecting, Spectrum portability, Spectrum choice, Spectrum sharing. Range detecting frames the base of subjective radios and is one of the principle strategies that empower the intellectual radios to improve the range use. Range detecting is for the most part done in the recurrence and time area. In this paper we will analyze about and investigate four noteworthy range detecting systems to be specific Energy detection, Matched filter spectrum detection, Cyclostationary spectrum detection and Waveform based spectrum detection. In view of the similar outcomes we can appraise the best spectrum detection for remote portable applications

scholarly journals Spectrum Sensing using AMC and TFT

2019 ◽  
Vol 9 (2) ◽  
pp. 898-902
Keyword(s):  
Decision Making ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Matched Filter ◽  
Energy Detection ◽  
Primary User ◽  
Modulation Classification ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Channel Conditions

Spectrum Sensing (SS) is a foremost step to implement next generation Cognitive Radio (CR) systems. The primary goal of a SS technique is to examine whether the Primary User (PU) is in active state or not by analyzing the surrounding radio environment. Traditional methods such as energy detection and Matched Filter Detection (MFD) schemes along with decision making circuits are generally used in SS. However, these techniques are developed under cooperative scenarios and they are used to sense single PU (narrowband sensing). In non-cooperative scenarios and fading channel conditions, traditional techniques produce higher false alarm. If Secondary User (SU) is occupied in the channel then SS task is more difficult. In order to overcome these limitations, a narrowband and wideband SS algorithm using Automatic Modulation Classification (AMC) and Time-Frequency Transform (TFT) is developed in this paper. The performance analysis of proposed AMC and TFT based SS technique under various channel conditions which is also described in this paper.

An Overview of the Different Principal Spectrum Sensing Techniques in Cognitive Radio Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 342-347
Author(s):  
Asmaa Maali ◽  
Hayat Semlali ◽  
Sara Laafar ◽  
Najib Boumaaz ◽  
Abdallah Soulmani
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Operational Characteristic ◽  
Main Function ◽  
Maximum Eigenvalue ◽  
Smoothing Factor

Cognitive radio is a technology proposed to increase the effective use of the spectrum. This can be done through the main function of cognitive radio technology, which is the spectrum sensing. In our work, we propose an analysis of the following spectrum sensing techniques: the matched filter detector, the cyclostationary feature detector, the energy detector and the maximum eigenvalue detector. More attention is given to blind sensing techniques that they do not need any knowledge of the primary user signal characteristics, namely the energy detection and maximum eigenvalue detection. These methods are evaluated in terms of Receiver Operational Characteristic curves and detection probability for various values of Signal to Noise Ratio based on Monte Carlo simulations, using MATLAB. As a result of this study, we found that the energy detection offers a good performance only for high SNR. Furthermore, with the maximum eigenvalue detector, the noise uncertainty problem encountered by the energy detection is solved when the value of the smoothing factor L ≥ 8 and. Finally, a summary of the comparative analysis is presented.

Review of Spectrum Sensing Techniques in Cognitive Radio Networks

2019 ◽  
pp. 85-107
Author(s):  
Ala Eldin Omer
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Matched Filter ◽  
Internal State ◽  
Energy Detection ◽  
Detection Sensitivity ◽  
Detection Techniques ◽  
Radio Technology ◽  
Cyclostationary Feature Detection

Most frequency spectrum bands are licensed to certain services to avoid the interference between various networks, but the spectrum occupancy measurements show that few portions of this spectrum are fully efficiently used. Cognitive radio is a future radio technology that is aware of its environment, internal state, and can change its operating behavior (transmitter parameters) accordingly. Through this technology the unlicensed users can use the underutilized spectrum without causing any harmful interference to the licensed users. Its key domains are sensing, cognition, and adaptation. The spectrum sensing problem is one of the most challenging issues in cognitive radio systems to detect the available frequency bands. This chapter introduces the concepts of various transmitter detection techniques, namely energy detection, matched filter detection, and cyclostationary feature detection. The chapter also discusses other sensing techniques that are introduced to enhance the detection performance of the conventional energy detector. Additionally, the introduced sensing techniques are implemented using extensive MATLAB simulations and their performances are evaluated and compared in terms of sensing time, detection sensitivity, and ease of implementation. The implementation is based on BPSK and QPSK modulation schemes under various SNR values for AWGN noisy channel with Rayleigh fading.

scholarly journals Matched Filter Based Spectrum Sensing Technique for 4G Cellular Network

2019 ◽  
Vol 38 (4) ◽  
pp. 973-978
Author(s):  
Shadab Kalhoro ◽  
Fahim Aziz Umrani ◽  
Mustahsan Khanzada ◽  
Liaquat Ali Rahoo
Keyword(s):  
Spectrum Sensing ◽  
Multiple Access ◽  
Matched Filter ◽  
Research Work ◽  
Energy Detection ◽  
Secondary Users ◽  
Single Carrier ◽  
Term Evolution ◽  
Theoretical Results

Modern and fast developments of wireless technologies have directed to a great demand for resources. It can be seen in the study that the range of existing spectrum is not used effectively, therefore the frequency band should be observed to ensure proper usage and to have the information of primary or licensed user is very much essential. In this research work uplink of LTE (Long-Term Evolution) is observed through MF (Matched Filter) spectrum sensing technique of CR (Cognitive Radio) network. This method examines the existence of signals in minimum possible time, reduces the hindrances between secondary users, increases accurateness of sensing and provides finest choice of threshold. In Uplink the System model which is used is known as SC-FDMA (Single Carrier Frequency Division Multiple Access). Entire simulation/results are prepared in MATLAB environment. This study also provides graphical contrast of simulated and theoretical results of matched filter and energy detection technique.

Sign in / Sign up

Export Citation Format

Share Document