Cyclostationary Feature Detection Research Articles

ED-SS based Cognitive Radio (CR) over Various Fading Channels for Modern Wireless Communications

Cognitive Radio (CR) has already been proved to provide the better solution for the problem of spectrum insufficiency in which every wireless system occupies the limited bandwidth spectrum (LBS) allotting to the licensed users. Sometimes, this LBS is empty in a certain place at a certain time that is called as spectrum holes estimated by spectrum sensing (SS). The spectrum sensing is the main technique used in cognitive radio and various spectrum sensing techniques are available in the literature such as: energy detection (ED), matched filter detection (MFD) and cyclo-stationary feature detection (CFD). In the present work, energy detection-spectrum sensing (ED-SS) based cognitive radio system has been implemented which can analyze the energy of the spectrum. The various fading channels such as additive white gaussian noise (AWGN), Rayleigh, and Nakagami have been incorporated in the present work over which the performance of cognitive radio has been analyzed and compared using the ED-SS technique. The experimental results show thatt the detection of the presence of primary user signal using proposed ED-SS technique is easier with low computational complexities in Cognitive Radio (CR) networks. The present work also demonstrates when the performance of proposed ED-SS technique over aforesaid three fading channels are compared, Nakagami-m fading channels exhibits superior improvement in Pd compared to Rayleigh and AWGN fading channels

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.

COMPARATIVE ANALYSIS OF NON-COOPERATIVE SPECTRUM SENSING TECHNIQUES IN COGNITIVE RADIO NETWORK

Cognitive radio (CR) is an integral system in telecommunications technology that gives unlicensed users access to licensed spectrums via dynamic spectrum access (DSA), to promote spectral efficiency. A significant operation in cognitive radio system is spectrum sensing. This paper evaluates and compares two of the major non-cooperative sensing techniques (energy and cyclostationary feature detector (CFD)) in order, to determine which gives better performance. Matlab Simulink was used as modeling and simulating tool for the evaluations. From the results, energy detector was simpler and faster but unlike CFD exhibited poor performance in corrupt channels.

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio.

Cognitive radio technology was introduced as a possible solution for spectrum scarcity by exploiting dynamic spectrum access. In the last two decades, most researchers focused on enabling cognitive radios for managing the spectrum. However, due to their intelligent nature, cognitive radios can scan the radio frequency environment and change their transmission parameters accordingly on-the-fly. Such capabilities make it suitable for the design of both advanced jamming and anti-jamming systems. In this context, our work presents a novel, robust algorithm for spectrum characterisation in wideband radios. The proposed algorithm considers that a wideband spectrum is sensed by a cognitive radio terminal. The wideband is constituted of different narrowband signals that could either be licit signals or signals jammed by stealthy jammers. Cyclostationary feature detection is adopted to measure the spectral correlation density function of each narrowband signal. Then, cyclic and angular frequency profiles are obtained from the spectral correlation density function, concatenated, and used as the feature sets for the artificial neural network, which characterise each narrowband signal as a licit signal with a particular modulation scheme or a signal jammed by a specific stealthy jammer. The algorithm is tested under both multi-tone and modulated stealthy jamming attacks. Results show that the classification accuracy of our novel algorithm is superior when compared with recently proposed signal classifications and jamming detection algorithms. The applications of the algorithm can be found in both commercial and military communication systems.

Performance analysis of cyclostationary spectrum sensing with dynamic thresholding using artificial neural network under varying signal to noise ratio and noise variance conditions

A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB.

Enhancement of spectral access of optical OFDM system using cognitive radio

Abstract This work presents the research on spectrum sensing in optical OFDM (orthogonal frequency division multiplexing) systems. Spectrum sensing is crucial for cognitive radio networks to efficiently utilize available spectral resources. Various spectrum sensing techniques are explored, including energy detection (ED), cyclostationary feature detection (CD), and matched filter detection (MF). These techniques enable the detection of occupied and unoccupied subcarriers in optical OFDM systems, facilitating dynamic spectrum access and spectrum sharing. The research highlights the importance of accurate spectrum sensing in maximizing spectral efficiency, optimizing network performance, and enabling coexistence of multiple users in optical OFDM-based cognitive radio networks. The findings contribute to the development of future wireless communication systems. The parameters such as probability of detection (pd), probability of false alarm (pfa), bit error rate (BER), and power spectral density (PSD) is analysed using computer simulation. The simulation results reveal that the MF achieved a gain of 2.8 dB and 3.6 dB as compared with conventional spectrum sensing algorithms.

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the AmBC. Novel Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inverse covariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBC to detect the presence of users at low power levels. The performance of the three detection techniques is measured using the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of Missed Detection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significant improvement via the HFDI technique for all the parameters.

Sustainable Non-Cooperative User Detection Techniques in 5G Communications for Smart City Users

The 4G network is not sufficient for achieving the high data requirements of smart city users. The 5G network intends to meet these requirements and overcome other application issues, such as fast data transmission, video buffering, and coverage issues, providing excellent mobile data services to smart city users. To allocate a channel or spectrum to a smart city user for error-free transmission with low latency, the accurate information of the spectrum should be detected. In this study, we determined the range of non-cooperative detection techniques, such as matched filter detection with inverse covariance approach (MFDI), cyclostationary feature detection with inverse covariance approach (CFDI), and hybrid filter detection with inverse covariance approach (HFDI); based on the results of these methods, we provided highly accurate spectrum information for smart city users, enabling sustainable development. To evaluate the performance of the proposed detection techniques, the following parameters are used: probability of detection (PD), probability of false alarms (Pfa), probability of miss detection (Pmd), sensing time, and throughput. The simulation results revealed that the HFDI detection method provided sustainable results at low signal-to-noise ratio ranges and improved channel detection and throughput of approximately 17% and 10%, respectively.

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

An enhancement to the existing cyclostationary feature detection in CRN

An enhancement to the existing cyclostationary feature detection in CRN

An optimised hardware architecture of the angular-domain cyclostationary detector for cognitive radio communications

This paper proposes a novel method to improve the performance and reduce the implementation design complexity of the angular domain cyclostationary feature detection (CFD). This method implements Orthogonal Frequency Division Multiplexing (OFDM) signal detection for cognitive radio applications. The originality of this work is based on using a selective sampling process for the OFDM signal detection by using only samples related to the cyclic prefix (CP). Using only the first NCP (CP length) angles of the angular domain cyclic autocorrelation function and eliminating the remaining angles has significantly improved the detector sensitivity. Results from Matlab simulations revealed that the proposed method reaches a detection probability of 100% at an SNR (signal-to-noise ratio) equal to −16 dB in the case of an additive white Gaussian noise (AWGN) channel. Moreover, the proposed method outperforms the other CFD methods with a performance range up to 5 dB. The proposed algorithm is coded using the VHDL language and implemented with the minimum of counters and comparators on Field-programmable gate array (FPGA) platform without using multipliers and DSP blocks. The proposed design requires a lesser power, logic and memory bits by 5.95, 60.96 and 87.5%, respectively, compared to the conventional angular domain CFD design. The proposed hardware architecture can be more efficient for spectrum sensing in many communication systems, such as LTE-Advanced and the L-band Digital Aeronautical Communication System (LDACS).

Comprehensive review on spectrum sensing techniques in cognitive radio

In wireless communication, spectrum sensing (SS) is one of the important techniques to study about the radio environment. Spectrum sensing phenomenon is used to estimate the unutilized frequency band of whole radio spectrum. Utilization of available spectrum can be improved by enhancing the spectrum sensing techniques. The performance of available spectrum sensing techniques may degrade substantially in low signal to noise ratio, multipath fading & shadowing environments. This may lead to increase in probability of false alarms and decrease in probability of detection. This paper gives the categorization of different spectrum sensing techniques (SST) and it also explores the different methods used in spectrum sensing. Comparison of energy detector (ED), matched filter detection (MFD), cyclostationary feature detection (CFD), waveform detection (WD) and eigen value detection (EVD) based on their methodology and accuracy is also present in this paper. Various issues associated with these spectrum sensing techniques are highlighted here and futuristic solutions proposed for improvement in existing spectrum sensing techniques.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

Blind detection of cyclostationary signals based on multi‐antenna beamforming technology

Cyclostationary feature detection is one of the widely used spectrum sensing techniques. Its greatest advantage is that it can effectively separate the signal from the noise under the condition of a low signal-noise ratio (SNR). Although cyclostationary feature detection is not affected by noise uncertainty like energy detection (ED), conventional cyclic stationary feature detection needs to know the prior conditions of signal cycle frequency (CF), cycle period, and so on. Moreover, the communication signals of authorized users are generally weak, which greatly affects the detection efficiency of cyclic stationary features. Therefore, this paper proposes a blind detection technique of cyclostationary characteristics based on conventional beam synthesis multi-antenna. The principle is to enhance the signal receiving intensity of the antenna by using beamforming technology, and realize the blind detection of cyclostationary signals by forming the direction of arrival angle. Not only that, the authors also make full use of the Wilk's approximation theorem and the generalized likelihood ratio test (GLRT) to derive the detection probability and false alarm probability. The simulation results show that the proposed cyclic stationary signal detector based on beam synthesis multi-antenna can realize blind detection of unknown cycle signals in a low SNR environment.

Comparative study between cognitive radio techniques in FM broadcasting band

Optimum usage of the existing frequency spectrum is a major requirement due to the large increase in the number of subscribers at the same frequency. The cognitive radio scheme has become an important application used to optimize spectrum utilization, detects spectrum bands that are not occupied by primary users to create communication links between secondary users in the same band. These non-occupied bands are described as white space. In this paper, a spectrum sensing method that is an important stage in cognitive radio communications is discussed. Various spectrum sensing methods such as energy detection (ED) and cyclostationary feature detection (CFD) are used to sense the spectrum in the FM broadcasting band. A comparison of energy detection and cyclostationary feature detection spectrum sensing methods is made to determine the empty bands in the FM broadcasting band. This enables secondary users to transfer information in these empty bands without affecting the primary users of the FM broadcasting system.

A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

A Novel Low-Complexity Cyclostationary Feature Detection Using Sub-Nyquist Samples for Wideband Spectrum Sensing

Fast Cyclostationary Feature Detection Method Based on CUDA

Aiming at the problem of poor real-time performance of cyclostationary feature detection, a fast cyclostationary feature detection algorithm based on the Unified Computing Device Architecture (CUDA) is proposed. This method moves the data processing part to the GPU, and uses the advantages of a large number of GPU stream processors and powerful multi-threaded concurrent execution functions to accelerate the FAM algorithm in parallel, thereby realizing fast signal processing. Experimental results show that compared with traditional serial algorithms, this method reduces the time overhead of the algorithm and improves the real-time performance of the algorithm.

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

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.