Signal Detection for QPSK Based Cognitive Radio Systems using Support Vector Machines

Cognitive radio based network enables opportunistic dynamic spectrum access by sensing, adopting and utilizing the unused portion of licensed spectrum bands. Cognitive radio is intelligent enough to adapt the communication parameters of the unused licensed spectrum. Spectrum sensing is one of the most important tasks of the cognitive radio cycle. In this paper, the auto-correlation function kernel based Support Vector Machine (SVM) classifier along with Welch's Periodogram detector is successfully implemented for the detection of four QPSK (Quadrature Phase Shift Keying) based signals propagating through an AWGN (Additive White Gaussian Noise) channel. It is shown that the combination of statistical signal processing and machine learning concepts improve the spectrum sensing process and spectrum sensing is possible even at low Signal to Noise Ratio (SNR) values up to -50 dB

Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

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

Improved Double Threshold Energy Detection in Cognitive Radio Networks

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such new and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the efficient utilization of the radio spectrum. One of the most crucial challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum specific geographic location and at a particular time. To enhance the reliability of detecting primary users in case of hidden terminal problem, we consider cooperative spectrum sensing in cognitive radio systems. Based on conventional single-threshold energy detection algorithm, we discuss about double-threshold version of Energy detector in cognitive radio system, and then, we analyse the detection probability, false alarm probability, probability of miss detection and their relationships. Further, we use two more parameters for performance evaluation: the probability of collision between the cognitive user and the primary user, and the probability of spectrum unavailable spectrum for the cognitive user. Comparing to the single, double-threshold energy detection algorithm, simulation results show that the double-threshold energy detection algorithm can make a lower collision probability between the primary user and the cognitive user, despite a little increasing of the spectrum unavailable probability. Finally by proposed Improved Double threshold Energy detector, by which reduction in the probability of detection is less reduced

spectrum sensing schemes for cognitive radio networks

The growing demand of wireless applications has put a lot of constraints on the usage of available radio spectrum which is limited and precious resource. Cognitive radio is a promising technology which provides a novel way to improve utilisation efficiency of available electromagnetic spectrum. Spectrum sensing used to detect the spectrum holes (underutilised bands of the spectrum) providing high spectral resolution capability. In this report, studied of spectrum sensing techniques is presented. The issues and challenges involved in implementation of spectrum sensing technique energy detection are discussed in detail. We implemented matched filter spectrum sensing technique and studied about cyclostationary detection in spectrum sensing .we implemented OFDM in spectrum sensing techniques as OFDM solves many problems in cognitive radio

Sensing opportunities in UMTS spectrum

The UMTS radio frequency spectrum is a highly expensive commodity. While the UMTS standards make very efficient use of the allocated bands there is however opportunity for further advancements. This paper focuses on opportunistic use of the UMTS spectrum as a means of ensuring that the maximum possible use of this valuable resource is made. In particular we focus on the local detection of UMTS TDD signals through the use of a cyclostationary feature detector. Simulation results for the use of this detector in the presence of multipath propagation and shadowing effects are presented

Performance Evaluation of Cognitive Radio Spectrum Sensing Techniques through a Rayleigh Fading Channel

In recent years, there has been a steep rise in the demand for bandwidth due to a sharp increase in the number of devices connected to the wireless network. Coupled with the expected commercialization of 5G services and massive adoption of IoT, the upsurge in the number of devices connected to the wireless network will continue to grow exponentially into billions of devices. To accommodate the associated demand for wireless spectrum as we step into this new era of wireless connectivity, traditional methods of spectrum utilization based on fixed and static allocation are no longer adequate. New innovative forms that support dynamic assignment of spectrum space on as-per-need basis are now paramount. Cognitive radio has emerged as one of the most promising techniques that allow flexible usage of the scarce spectrum resource. Cognitive radio allows unlicensed users to opportunistically access spectrum bands assigned to primary users when these spectrum bands are idle. As such, cognitive radio reduces the gap between spectrum scarcity and spectrum underutilization. The most critical function of cognitive radio is spectrum sensing, which establishes the occupation status of a spectrum band, paving the way for a cognitive radio to initiate transmission if the band is idle. The most common and widely used methods for spectrum sensing are energy detection, matched filter detection, cyclostationary feature detection and cooperative based spectrum sensing. This dissertation investigates the performance of these spectrum-sensing techniques through a Rayleigh fading channel. In a wireless environment, a Rayleigh fading channel models the propagation of a wireless signal where there is no dominant line of sight between the transmitter and receiver. Understanding the performance of spectrum sensing techniques in a real world simulation environment is important for both industry and academia, as this allows for the optimal design of cognitive radio systems capable of efficiently executing their function. MATLAB software provides an experimental platform for the fusion of various Rayleigh fading channel parameters that mimic real world wireless channel characteristics. In this project, a MATLAB environment test bed is used to simulate the performance for each spectrum sensing technique across a range of signal-to-noise values, through a Rayleigh fading channel with a given set of parameters for channel delay, channel gain and Doppler shift. Simulation results are presented as plots for probability of detection versus signal-tonoise ratio, receiver operating characteristics (ROC) curves and complementary ROC curves. A detailed performance analysis for each spectrum sensing technique then follows, with comparisons done to determine the technique that offers the best relative performance

Improved Double Threshold Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio

In this paper, we focus on cooperative spectrum sensing (CSS) for double threshold improved energy detector. In this method, the improved energy detector compares positive power operation p of the amplitude of received signals at each secondary user (SU) with two thresholds to make binary decision about presence or absence of primary user (PU). The energies lying between upper and lower threshold are considered unreliable and are not considered in cooperation. The decisions are forwarded over an imperfect reporting channel to a fusion center where final decision on presence or absence of PU is taken. We combine double threshold approach with improved energy detection. Two step optimization is performed where cooperative probability of detection is maximized as a function of threshold difference in double threshold and then highest value of maximized cooperative probability of detectionis found as a function of power operation p, average signal-to-noise ratio at SUs, number of cooperating SUs and cooperative probability of false alarm. Also, we find the optimum fusion rule at fusion center along with optimum power corresponding p to the lowest value of the minimized total error rate using two step optimization. Then weanalyse the effect of errors introduced in reported decisions due to imperfect reporting channel.Defence Science Journal, 2013, 63(1), pp.34-40, DOI:http://dx.doi.org/10.14429/dsj.63.376