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 Analysis of Cognitive Radio Spectrum Sensing Techniques over Nakagami-m Fading Channel

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Fading Channel ◽  
Feature Detection ◽  
Energy Detection ◽  
Radio Spectrum ◽  
Awgn Channel ◽  
Novel Approach ◽  
Spectrum Scarcity ◽  
Cyclostationary Feature Detection

As the demand of wireless communication increases exponentially, with the same ratio scarcity of spectrum also originates. To overcome this spectrum scarcity a novel approach, Cognitive Radio (CR) shows development of an opportunistic and promising technology. This paper explores implementation and analysis of the CR spectrum sensing techniques such as Matched filtering, Energy detection and Cyclostationary feature detection on MATLAB platform by simulation. We analyze performance of these techniques over, Nakagami-m fading channel with AWGN channel for both the BPSK and QPSK modulation.

Adaptive Two-Stage Sensing Based on Energy Detection and Cyclostationary Feature Detection for Cognitive Radio Systems

2013 ◽  
Vol 411-414 ◽  
pp. 1521-1528 ◽  
Author(s):  
Yu Yang ◽  
Yan Li Ji ◽  
Han Hui Li ◽  
Du Lei ◽  
Meng Rui
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Two Stage ◽  
Probability Of False Alarm ◽  
Detection Model ◽  
Radio Systems ◽  
Cyclostationary Feature Detection

In this paper, we investigate the features of energy detection and cyclostationary feature detection for spectrum sensing. In order to combine their advantages, we propose an adaptive two-stage sensing scheme which performs spectrum sensing using an energy detector first in cognitive radio networks. Then in the second stage, this scheme decides whether or not to implement cyclostationary feature detection based on the sensing results of the first stage. On the premise of meeting a given constraint on the probability of false alarm, the goal of our proposed scheme is to optimize the probability of detection and sensing speed at the same time. In order to obtain the optimal detection thresholds, we can formulate the detection model as a nonlinear optimization problem. Furthermore, the simulation results show that the proposed scheme improves the performance of spectrum sensing compared with the ones where only energy detection or cyclostationary feature detection is performed.

scholarly journals Noise uncertainty effect on multi-channel cognitive radio networks

2020 ◽  
Vol 10 (5) ◽  
pp. 4818
Author(s):  
Amira Osama ◽  
Heba A. Tag El-Dien ◽  
Ahmad A. Aziz El-Banna ◽  
Adly S. Tag El-Dien
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Feature Detection ◽  
Matched Filter ◽  
Energy Detection ◽  
Radio Networks ◽  
Main Process ◽  
Fluctuation Effect ◽  
Noise Uncertainty ◽  
Channel Throughput

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

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

Improved Two-Stage Spectrum Sensing for Cognitive Radio Networks

2019 ◽  
Vol 23 (6) ◽  
pp. 1052-1062
Author(s):  
Fidel Wasonga ◽  
Thomas O. Olwal ◽  
Adnan Abu-Mahfouz ◽  
◽  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Detection Accuracy ◽  
Two Stage ◽  
Probability Of Error ◽  
Average Spectrum ◽  
Sensing Time

Cognitive radio employs an opportunistic spectrum access approach to ensure efficient utilization of the available spectrum by secondary users (SUs). To allow SUs to access the spectrum opportunistically, the spectrum sensing process must be fast and accurate to avoid possible interference with the primary users. Previously, two-stage spectrum sensing methods were proposed that consider the sensing time and sensing accuracy parameters independently at the cost of a non-optimal spectrum sensing performance. To resolve this non-optimality issue, we consider both parameters in the design of our spectrum sensing scheme. In our scheme, we first derive optimal thresholds using an optimization equation with an objective function of maximizing the probability of detection, subject to the minimal probability of error. We then minimize the average spectrum sensing time using signal-to-noise ratio estimation. Our simulation results show that the proposed improved two-stage spectrum sensing (ITSS) scheme provides a 4%, 7%, and 6% better probability of detection accuracy rate than two-stage combinations of energy detection (ED) and maximum eigenvalue detection, energy detection and cyclostationary feature detection (CFD), and ED and combination of maximum-minimum eigenvalue (CMME) detection, respectively. The ITSS is superior also to single-stage ED by 19% and shows an improved average spectrum sensing time.

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