Energy Detection Research Articles

Effective energy detection using machine learning techniques for cooperative sensing in cognitive radio networks

Effective energy detection using machine learning techniques for cooperative sensing in cognitive radio networks

Average Energy Detection With Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems

Average Energy Detection With Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems

An Optimization of RSRP on Wi-Fi and LTE-LAA Coexistence Networks

Over the last decade, mobile traffic has grown at an exponential rate, resulting in a scarcity and incompetent use of licensed spectrum by mobile network operators. Therefore, it is important to maintain effective communication to avoid disconnecting signals. The technique of regulating the power of a transmitter in order to improve the communication signal or entire quality of service is known as power control. It is mostly used to improve the performance of a communication device by controlling the transmission power. Power control, in most scenarios, allows the transmission or signal power to be scaled, leading to improved signal quality. On the other hand, power control methods are aimed at avoiding unnecessarily high-power transmission. Hence, particle swarm optimization (PSO) is a computational method for optimizing the reference signal received power (RSRP) by iteratively attempting to improve an unnecessarily high-power transmission solution in terms of a given quality measure. The objective of this research is to evaluate the optimum RSRP value and proposed ideal energy detection threshold for Wi-Fi and LTE-LAA in order to obtain fair coexistence between two networks. Thus, simulations for PSO are carried out using MATLAB software. Based on the results obtained, there are improvement in throughput for both networks after PSO implementation therefore, an ideal energy detection threshold is suitable to use in order to get fair coexistence between Wi-Fi and LTE-LAA networks. Consequently, all the results obtained will be used in following research work in order to evaluate user’s handover in various scenarios.

Pilot spoofing attack detection and channel estimation for secure massive MIMO

AbstractPilot spoofing attack (PSA) is an active eavesdropping attack in massive multiple‐input multiple‐output systems, where the eavesdroppers transmit the same pilot sequence as the legitimate user does to the base station to confuse the normal channel estimation during the uplink channel training phase. With the contaminated channel estimations, more information will be leaked to eavesdroppers in the downlink transmission phase. However, it is a challenging issue to detect the PSA attack due to the similarity of the received signals and the variations of the wireless channels. Here, a new PSA detection scheme by using the difference of two different estimators is presented, that is, the least square estimator and the minimum mean square error estimator, without requiring any priori of eavesdroppers. Following that, a new data‐aided channel estimation scheme is proposed to eliminate the PSA effect. Simulation results demonstrate that the proposed PSA detection scheme outperforms the conventional energy detector, and more accurate legitimate channel estimation and higher sum secrecy rates can be obtained with the proposed scheme.

Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar

AbstractMain lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Quantum cryptography in the security of cognitive radio networks

Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.

EVALUATING THE CHARACTERISTICS OF THE VTSM SPECTRUM SENSING METHOD IN COGNITIVE RADIO NETWORKS

The article investigates the complexities associated with spectrum analysis in cognitive radio networks (CRNs). It begins by acknowledging the evolving challenges of spectrum sensing due to the dynamic nature of wireless environments and the increasing demand for efficient spectrum utilization. The study thoroughly examines various traditional spectrum sensing methods, including Energy Detector Based Sensing, Waveform-Based Sensing, Cyclostationarity-Based Sensing, and Matched Filtering. The focus of the research is on the Variable Time Segment Monitoring (VTSM) method, a novel approach that optimizes spectrum sensing by dynamically adjusting the time segments used for analyzing spectral characteristics. The paper highlights the VTSM method's ability to enhance detection accuracy and reduce false alarms by adapting to different signal environments, making it particularly suited for complex and dynamic CRN scenarios. Furthermore, the article compares VTSM with traditional methods across key performance metrics such as detection accuracy, false alarm rate, latency, and computational complexity. The analysis reveals that while traditional methods have their strengths, VTSM offers a balanced approach, combining flexibility, accuracy, and moderate computational demands, thereby providing a versatile solution for modern spectrum sensing challenges. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions, which are crucial for optimizing network performance and ensuring reliable communication in increasingly congested and complex wireless environments. The article concludes by emphasizing the importance of selecting the appropriate spectrum sensing method based on the specific requirements of the CRN application, considering factors such as accuracy, computational resources, and environmental dynamics. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions.

Development of a large NaI(Tl) detection system

A large NaI(Tl) detection system was developed to address the increasing demand for high detection efficiency and high energy resolution detectors across various applications. Through Geant4 simulation and experimental research, critical characteristics of the detector prototype were examined, encompassing energy response, energy nonlinearity, and energy resolution. Given the substantial impact of variations in Tl concentration and the complex process of photon propagation on the detection performance of large NaI(Tl) crystals, the detection efficiency and energy resolution with the radioactive source placed at different incident spots were studied to evaluate its spatial response uniformity. The results demonstrate that the detector's energy response range spans from 50 keV to 3 MeV with an energy nonlinearity of 1.5%. The developed large NaI(Tl) detection system achieved an overall energy resolution of 7.93%, along with excellent uniformity in its resolution and detection efficiency, satisfying the application requirements for large dimensions and high detection efficiency.

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection

Deep learning-based gamma spectroscopic analysis considering multiple variables for in situ applications

Gamma spectroscopy in environments with fluctuating temperatures, magnetic fields, and radiation doses can lead to inaccurate material analysis and increased radiation exposure for workers. In this manuscript, we propose a deep learning model that reduces human intervention and remains robust against variables in uncontrolled environments, thereby enhancing the applicability of gamma spectroscopy in the field. The process of establishing a dataset to train, validate, and test the deep learning model involved simultaneous consideration of multiple variables, including gain shifts, detector energy resolutions, the number of mixed radioisotopes (RIs), mixed RI ratios, and statistical fluctuations of the spectrum. The model was trained using multi-label learning to perform RI identification and quantification simultaneously. Specifically, the RI quantification output of our model structure was designed to predict full-energy peak areas and mixed ratios for each RI. Highlighting the importance of a high-performance training dataset, the training and validation results of the gamma spectroscopy model were analyzed in depth. In the test results, organized by RI type and number of mixed RIs, the model demonstrated that following: even under complex conditions with multiple variables, all outcomes for RI identification and quantification were predicted with high accuracy and precision, exceeding 98%.

Design and Analysis of a Moon-Based Earth-Radiation Measurement System

This research project envisions using a lunar observation platform to measure the full-wave (0.2~100 μm) and shortwave (0.2~4.3 μm) radiation of the Earth, achieving an accurate estimation of the overall radiation budget of the Earth. Based on the lunar platform, the system analyzes Earth’s radiation characteristics and geometric attributes, as well as the sampling properties of observation times. Informed by these analyses, an Earth-facing optical radiation measurement system tailored to these specifications is designed. The optical system adopts an off-axis three-mirror configuration with a secondary image plane, incorporating a field stop at the primary image plane to effectively suppress solar stray light, scattered lunar surface light, and background radiation from the instrument itself, ensuring the satisfactory signal-to-noise ratio, detection sensitivity, and observation duration of the instrument. At the same time, stringent requirements are imposed for the surface treatments of instrument components and temperature control accuracy to further ensure accuracy. Simulation analyses confirm that the design satisfies requirements, achieving a measurement accuracy of better than 1% across the entire optical system. This Moon-based Earth-radiation measurement system, with capabilities for Earth-pointing tracking, radiation energy detection, and stray-light suppression, furnishes a more comprehensive dataset, helping to advance our understanding of the mechanisms driving global climate change

Bi-directional Long Short-Term Memory with Bird Mating Optimizer based Spectrum Sensing Technique for Cognitive Radio Networks

Cognitive radio networks (CRN) enable the wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables for satisfying the QoS needs by reducing the energy utilization. In cognitive radio, the detection of principal user signals is crucial for secondary users in order to make the best use of available spectrum (CR). The problem with conventional spectrum sensing approaches is that they have a high rate of missed detections and false alarms, which makes it difficult to make effective use of the spectrum. In instruction to recover the correctness of the detection of free spectrum, deep learning-based spectrum sensing is employed. For resolving the drawbacks of traditional energy detection models, this paper presents a new spectral sensing technique for cognitive radio networks (SST-CRN). Recently published research in spectrum sensing has placed a high value on deep learning that is model-agnostic as a result of this. In particular, long-short term memory (LSTM) networks perform exceptionally well at extracting spatial and temporal information from input data in deep learning. The proposed model, Bidirectional Long Short-Term Memory (Bi-LSTM) with Bird Mating Optimization (BMO), makes it possible to create nonlinear threshold-based systems more quickly and easily than previously possible. The proposed Bi-LSTM with BMO technique involves two stages of operations namely offline and online. The offline stage creates the non-linear threshold value to detect energy. In addition, the online stage automated selects a decision function saved in offline stage for determining the existence of primary user. The experiments were carried out and the results were analyzed using the help of the RadioML2016.10b dataset. A greater spectrum detection performance over previous sensing models has been obtained using a combination of the B-LSTM model and the BMO model, it has been discovered.

Calibration of a gamma ray Compton camera for radioactivity measurements

AbstractA dual-plane Compton imaging detector previously developed for prompt gamma imaging has been further tested and calibrated for quantitative radioactivity determination, specifically to assess radioactive debris from a reactor fuel element. The debris piece contains uranium isotopes and several long-lived fission products, including cesium-137 (Cs-137). The detector consists of two pixelated planes of cadmium-zinc-telluride (CZT) crystals which record gamma ray events interacting within these volumes. In addition to the energy spectrum obtained by pulse height analysis, the time-stamped events from each detector pixel are sorted by location into 3 categories for post-processing: intra-plane, inter-plane, and coincidence. An image reconstruction software enables spatial localization of the select gamma ray peak(s) from the spectrum, thereby separating the gamma ray by specific isotopes. We used the camera to image a small piece of debris for demonstration purposes. A measurement was performed alongside a high purity germanium (HPGe) detector, which could determine the isotopes' absolute activities and be used as a reference for the camera. In addition, a known isotopic point source was used to calibrate the camera’s energy detection efficiency in the same measurement geometry, correlating the image pixel intensity to the isotopic activity.

Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale

Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.

Mesofractal Modeling of Biosystems & Organic Spintronics

Mesoscopic modeling of complex systems involves thermodynamic nonequilibrium of discrete scaling. Further from quantum correlation on a chip retrieved quantum nonlinear optics with single photons enabled by strongly interacting atoms. Accompanied by mesofractals as the development of meso & micro size fractal structures is required to mimic various biological systems for various functions. Showed through fluorapatite in gelatin‐based nanocomposite, fractal in DNA knots driven by balance of fission & fusion in mtDNA/mitochondrial DNA mechanism, for optical engines for light energy detection described the proportional integral derivative [PI(D)]‐controller set in microbial cells to HCCI/Homogeneous Charge Compression Ignition.

Validation of the FLUKA code to simulate response functions of organic liquid scintillator for high‐energy neutrons

In this research, the FLUKA code was employed to calculate the response functions of the BC501A organic scintillation detector for energies ranging from 25 to 800 MeV. Using the AUXSCORE, EVENTBIN and TCQUENCH cards, the response function of the BC501A organic scintillation detector to mono-energetic neutrons in the energy range of 25–800 MeV was computed. The comparison confirms that the simulation results obtained from the FLUKA code show a promising agreement with the results obtained from experiments, other codes, and the analytical method.

An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM

Abstract The ever-increasing demand for bandwidth in optical networks necessitates efficient spectrum utilization. To address this challenge, this paper proposes a novel hybrid spectrum sensing algorithm tailored explicitly for 256-QAM (Quadrature Amplitude Modulation) optical communication waveforms. The proposed algorithm combines the strengths of energy detection and cyclostationary feature detection to overcome the limitations of individual methods. Energy detection (ED) provides fast and low-complexity sensing, while cyclostationary feature detection offers higher accuracy and sensitivity. First, ED is employed for rapid initial spectrum assessment. Subsequently, matched filter (MF) detection is selectively applied only to frequency bands identified as potentially occupied by primary users based on the energy detection results. This selective approach significantly reduces computational complexity while maintaining high detection accuracy. The results demonstrate significant improvements in detection accuracy, sensitivity, and computational efficiency compared to existing methods. In particular, the hybrid algorithm performs better in scenarios where weak 256-QAM signals coexist with strong primary users, showcasing its effectiveness in dynamic spectrum-sharing applications. This work contributes significantly to optical spectrum sensing by offering an efficient and accurate solution for advanced radio systems. The proposed hybrid algorithm paves the way for improved spectrum utilization and facilitates the development of high-performance, next-generation optical networks. The projected method obtained a gain of −200 as compared with the existing methods.

IXPE Gas Pixel Detector characterization with the X-ray calibration facility

The X-ray Calibration Facility (XCF) is a table-top, open-design irradiation setup for research, available at the Physics Department of the University of Turin. It offers beams of photons at different energies and with various spatial and polarization configurations. These characteristics allow XCF to be a suitable facility for calibration and test of energy, position and polarization sensitive detectors.

A Platform for Ultra-Fast Proton Probing of Matter in Extreme Conditions.

Recent developments in ultrashort and intense laser systems have enabled the generation of short and brilliant proton sources, which are valuable for studying plasmas under extreme conditions in high-energy-density physics. However, developing sensors for the energy selection, focusing, transport, and detection of these sources remains challenging. This work presents a novel and simple design for an isochronous magnetic selector capable of angular and energy selection of proton sources, significantly reducing temporal spread compared to the current state of the art. The isochronous selector separates the beam based on ion energy, making it a potential component in new energy spectrum sensors for ions. Analytical estimations and Monte Carlo simulations validate the proposed configuration. Due to its low temporal spread, this selector is also useful for studying extreme states of matter, such as proton stopping power in warm dense matter, where short plasma stagnation time (<100 ps) is a critical factor. The proposed selector can also be employed at higher proton energies, achieving final time spreads of a few picoseconds. This has important implications for sensing technologies in the study of coherent energy deposition in biology and medical physics.