VLSI Architecture for Cyclostationary Feature Detection Based Spectrum Sensing for Cognitive-Radio Wireless Networks and Its ASIC Implementation

2016 ◽  
Author(s):  
Mahesh S. Murty ◽  
Rahul Shrestha
Keyword(s):  
Wireless Networks ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Vlsi Architecture ◽  
Cyclostationary Feature Detection

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.

Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes

2015 ◽  
pp. 122-152
Author(s):  
Mahsa Derakhshani ◽  
Tho Le-Ngoc ◽  
Masoumeh Nasiri-Kenari
Keyword(s):  
Performance Evaluation ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Feature Detection ◽  
Cognitive Radios ◽  
Final Decision ◽  
Low Snr ◽  
Cyclostationary Detection ◽  
Simple Implementation ◽  
Cyclostationary Feature Detection

Spectrum sensing is one of the key elements in the establishment of cognitive radio. One of the most effective approaches for spectrum sensing is cyclostationary feature detection. Since modulated signals can be modeled as cyclostationary random signals, this feature can be used to recognize the cyclostationary modulated signal in a background of stationary noise even at low SNR regimes. This chapter reviews non-cooperative cyclostationary sensing approaches and reports recent advances in cooperative cyclostationary sensing algorithms. New results for cooperative cyclostationary spectrum sensing are then presented, which ensure better performance as well as faster and simpler operation. In the proposed schemes, each Secondary User (SU) performs Single-Cycle (SC) cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the PU is explored to improve its performance. Furthermore, this chapter presents another look at the performance evaluation of cyclostationary detectors in terms of deflection coefficients.

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.

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