Performance analysis of spectrum sensing techniques for cognitive radio

Spectrum sensing is a key element for cognitive radio and is process of obtaining awareness about the radio spectrum in order to detect the presence of other users. In this paper we study the performance of different spectrum sensing techniques in terms of detection performance and required SNR, based on theoretical expressions. Keywords- cognitive radio; spectrum sensing; energy detection; matced filter detection; cyclostationary feature detectio

Wideband Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks

Improved Sensing Accuracy using Enhanced Energy Detection Algorithm with Secondary User Cooperation in Cognitive Radios

Spectrum sensing is indispensable for cognitive radio to identify the available white spaces. Energy detection is considered as a preferred technique for spectrum sensing in cognitive radio networks. It is because of its simplicity, applicability and low computational complexity, energy detection is employed widely for spectrum sensing. This paper proposes an enhanced energy detection based spectrum sensing algorithm which incorporates the features of traditional energy detection and cooperative detection. The false alarm and detection probabilities of the proposed algorithm are derived theoretically under AWGN channel conditions. The performance of the proposed algorithm is evaluated analytically for various decision thresholds. The performance evaluations indicate that the proposed enhanced energy detection algorithm outshines the traditional energy detection algorithm and greatly improves the spectrum sensing accuracy under varying SNR conditions

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

Development of Adaptive Sensing Algorithm for Minimizing Energy and Bandwidth Consumption in Cooperative Spectrum Sensing Technology

Optimized consumption of energy and bandwidth is crucial for efficient utilization of the limited electromagnetic spectrum for telecommunication purposes. Cognitive radio is one of the dynamic spectrum management applications with numerous benefits related to the management of available spectrum. But it has the challenge of high energy and bandwidth usage when the cooperative scheme of spectrum sensing is applied for accurate sensing. In this paper, an adaptive spectrum sensing algorithm was developed to minimize energy and bandwidth consumption in cognitive radio spectrum sensing while ensuring accurate spectrum sensing. The adaptive algorithm was developed based on the signal-to-noise ratio conditions of the channel. Results reveal that the energy and bandwidth usage by the cooperative spectrum sensing can be significantly reduced without negatively affecting the performance and detection of the cognitive radio in varying noisy condition

Adaptive Techniques to Detect White Spaces Using Spectrum Sensing In Cognitive Radio

Spectrum sensing is one of the key technologies to realize dynamic spectrum access in cognitive radio systems. Cognitive radios have been proposed as a possible solution to improve spectrum utilization by enabling opportunistic spectrum sharing. The main requirement for allowing CR�s to use proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. Spectrum sensing allows cognitive users to autonomously identify unused portions of the radio spectrum. In this work, energy detection technique is considered for spectrum sensing and uses the cost-function that depends upon a single parameter which gives the aggregate information about the present or absent of licensed users. The process of threshold selection for energy detection is addressed by the constant false alarm method and selection is carried out considering present conditions of noise levels. In this paper, simulation results shown that if we dynamically adjust the detection threshold based on noise level present during the detection process. The detection of white spaces will be higher at lower sampling time as compared with probability of detection and false alarm rate

An Investigation into Cognitive Radio System Performance

The objective of this thesis is to explore cognitive radio performance through an in-depth literature review and an implementation of a software-defined radio prototyping system. Specifically, this thesis investigates the spectrum-sensing aspect of cognitive radio by comparing two spectrum-sensing methods. It was found in the literature review that a system utilizing matched filter detection would provide higher probability of detection in low signal-to-noise ratio environments when compared to a system utilizing energy detection. These spectrum sensing methods were thus implemented and compared in the cognitive radio systems presented in this thesis. Additionally, experiments were conducted to determine the most efficient intervals for the spectrum sensing and cycle interval periods. Therefore, system performance was measured on the basis of probability of successful primary user signal detection and maximum throughput capabilities, quantified by bit error rate. It was found that a cognitive radio system based on matched filter detection was more robust, given that the transmitted signal of interest was previously known. However, compared to a system based on energy detection, the implementation of the matched filter required more complex algorithms and computational power. These results are consistent with the findings in the literature review