Traditional Energy Detection Research Articles

Adaptive Double-Threshold Cooperative Spectrum Sensing Algorithm Based on History Energy Detection

Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.

A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing

The traditional energy detection algorithm has been widely used in the field of signal detection, and a variety of improved algorithms have been derived. In the case of low signal-to-noise ratio, existing methods have shortcomings on achieving fast and accurate spectrum sensing that need to be resolved. This work proposes a normalized-variance-detection method based on compression sensing measurements of received signal. The discrete cosine transform sensing matrix is used to compress the signal, whose normalized variance is then calculated before being used as the testing variable for detecting the primary user signal. Taking the detection results as historical data into consideration, the classification model is obtained after training by applying a support vector machine for classifying and predicting test signals. Simulation results show that the proposed method outperforms the current state-of-the-art approaches by achieving faster and more accurate spectrum occupancy decisions.

A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic

In this paper, an interference detection algorithm based on fuzzy logic fusion is proposed for the interference monitoring system (IMS) of the global satellite navigation system (GNSS). In particular, for the problem of reduced detection performance caused by noise fluctuations in a complex electromagnetic environment, the proposed algorithm introduces the idea of fuzzy logic. According to the credibility of local detection results in different regions determined by noise uncertainty, we adopt a two-step cooperative detection mechanism in the fusion center (FC). When a definite detection result is obtained in the first step, the decision is finished. Otherwise, we will execute the decision algorithm based on the fuzzy logic fusion of the second step. The experimental results show that the proposed algorithm has the best detection performance, compared with the traditional energy detection algorithm and the energy detection algorithm based on soft data. Furthermore, compared with soft data, the system overhead of the proposed algorithm is also reduced without increasing the amount of calculation.

A directly seeking spectrum holes algorithm by compressive sampling

SummaryThe method of cognitive radio (CR) is a promising solution to the problem of spectrum scarcity. One of the crucial tasks in the CR is the spectrum hole (SH) detection. The energy detection (ED) method is the most dominant spectrum sensing approach owing to its low computational complexity and the ability to identify SHs without requiring a priori knowledge of primary transmission characteristics. But it will be accompanied by some problems such as the high sampling rates, the difficult setting for detection threshold, and the complexity. An improved algorithm with directly seeking SHs from compressive sampling via compressive sensing algorithm is proposed in this paper. The sampling rate is decreased by the process of the compressive sampling, and the computation complexity is reduced for the elimination of the construction stage in the proposed algorithm. Besides, the detection threshold is not needed to be set for the method of relative comparison. The simulation results show that the proposed algorithm has a similar probability of detection (POD) of SHs to the traditional ED algorithm and the compressive spectrum sensing algorithm with minimum sampling numbers and lower computation complexity on the condition that the signal noise ratio is beyond 0 dB. It means the proposed algorithm obtains the balance between computing complexity and the sampling rate, and the performance of SH detection is maintained at the same time.

Research on Improved Algorithm of Frame Detection for COFDM High-Definition Wireless Transmission System and FPGA Implementation

COFDM is one of the more advanced modulation technologies. COFDM wireless image transmission technology is adopted on high-speed mobile UAVs, which can meet the requirements of long-range aerial photography and monitoring. Frame detection is the first step in receiving data. The accuracy of the detection data directly affects the reliability and robustness of the reception performance. Through the research of traditional energy detection algorithm, a delay correlation algorithm based on 802.11a frame structure is adopted, which overcomes the shortcomings of large energy variation and rapid decrease of threshold under low SNR. It also achieves good frame detection performance and can be implemented on an FPGA. Combined with the simulation results of ISE, the frame detection algorithm is suitable for COFDM high-definition wireless image transmission system.

Adaptive Technique with Cross Correlation for Lowering Signal-to-Noise Ratio Wall in Sensor Networks

Energy detection is considered as most popular spectrum sensing technique. However, the impact of noise uncertainty in wireless environment limits the sensitivity of energy-based detector. It generally suffers from Signal-to-Noise Ratio (SNR) wall, which is defined as the minimum SNR in which robust detection is not possible. This paper presents an adaptive technique to improve SNR wall in sensor networks that combines cross-correlation scheme and dynamic threshold method to improve performance. An analytical expression of SNR wall is derived to show improved performance over traditional energy detector. Theoretical analyses and simulations validate effectiveness of the proposed method under noise uncertainty environment.

A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

Wideband signal detection for cognitive radio applications with limited resources

Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many applications, spectrum sensing in cognitive radio (CR) is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. Aiming at using small sample size, a statistical model of samples is given based on Student’s t distribution. However, the limited number of channel observations brings a reduction of confidence in the decision. A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer (D-S) decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method (ED) and the decision fusion method when small sample size is used.

Performance analysis of spectrum sensing schemes based on fractional lower order moments for cognitive radios in symmetric α-stable noise environments

Abstract Spectrum sensing schemes based on fractional lower order moment (FLOM) are often used in impulsive noise environments in which traditional energy detectors are not applicable. The performance of FLOM-based detectors operating in an α-stable noise environment is difficult to evaluate. This is because α-stable random variables can usually only be modeled by the characteristic function since closed-form expressions are not available, except for the special values of the characteristic exponent that correspond to the Cauchy and Gaussian noise distributions. In this paper, we derive closed-form expressions for the probability density function (PDF) and corresponding complementary cumulative distribution function (CDF) for a symmetric α-stable random variable with an arbitrary characteristic exponent α (0

Efficient Spectrum Sensing for Aeronautical LDACS Using Low-Power Correlators

Air traffic has seen tremendous growth over the past decade pushing the need for enhanced air traffic management schemes. The $L$ -band digital aeronautical communication system (LDACS) is gaining traction as a scheme of choice, and aims to exploit the capabilities of modern digital communication techniques and computing architectures. Cognitive radio-based approaches have also been proposed for LDACS to improve spectrum efficiency and communication capacity; however, these require intelligent compute capability in aircrafts that enforce limited space and power budgets. This paper proposes the use of multiplierless correlation to enable spectrum sensing in LDACS air-to-ground links, and its integration into the on-board LDACS system. The proposed architecture offers improved performance over traditional energy detection (ED) even at low signal-to-noise ratio (SNR) with lower energy consumption than a multiplier-based correlator, while also assisting in receiver synchronization. We evaluate the proposed architecture on a Xilinx Zynq field-programmable gate array and show that our approach results in 28.3% reduction in energy consumption over the multiplier-based approach. Our results also show that the proposed architecture offers 100% accuracy in detection even at −12-dB SNR without requiring additional circuitry for noise estimation, which are an integral part of ED-based approaches.

A joint optimization of energy harvesting and spectrum sensing for wireless sensor networks under Middleton Class A noise

With the growing popularity of wireless sensor networks, the environment in which the network is located becomes more undesirable. In addition, the problems of spectrum scarcity and the short sensor lifetime have become increasingly prominent. In this article, we incorporate the two technologies of cognitive radio and energy harvesting to solve the above problems of wireless sensor networks under impulsive noise. First, we use a Middleton Class A noise model to imitate the practical environment and the fractional lower order moments detector is employed to perform spectrum sensing for the sensors of wireless sensor networks, which are performing as the second users. Second, a new time-slots structure is proposed for the self-powered second user and the analytical expression of the second user’s average throughput is derived. Finally, we maximize the second user’s average throughput by a joint optimization of the sensing duration and data transmission duration while giving the primary user sufficient protection. Simulation shows that a much better performance can be achieved by fractional lower order moment detector than the traditional energy detector. Moreover, our optimization of the time-slots allocation is feasible and the maximum second user’s average throughput can be obtained.

Characteristics of <inline-formula> <tex-math notation="LaTeX">$p$</tex-math> </inline-formula>-Norm Signal Detection in Gaussian Mixture Noise

Performance of the $p$ -norm detector has been analyzed recently in additive white Gaussian noise and several analytical solutions were obtained. We have developed these solutions for the case of non-Gaussian noise. Expressions for the false alarm probability $P_{F}$ and the detection probability $P_{D}$ were found in series-form for an arbitrary index of power $p$ using an analysis based on the moment generating function. Series-form expressions for the average detection probability $\overline {{P_D}} $ in the non-Gaussian environment for Nakagami- $m$ , $k- {\mu} $ , and $\eta - {\mu} $ fading channels were derived as well. Recently proposed $p$ -law selection and $p$ -law combining schemes for antenna diversity reception were analyzed. The detection characteristics of a $p$ -norm detector were compared with a traditional energy detector and with an optimal detector. All analytical results were verified with a statistic simulation.

Threshold selection method for UWB TOA estimation based on wavelet decomposition and kurtosis analysis

In wireless sensor networks, ranging or positioning via ultra-wideband (UWB) has caused widespread research interests where the non-coherent energy detection (ED) method with low sampling rate and low complexity is widely studied. However, the traditional energy detection methods only analyze the signal energy in the time domain, so their error is relatively large. In this paper, the simulation results show that most of the signal energy concentrates in the low-frequency band, so a novel threshold selection method for time of arrival (TOA) estimation is proposed that analyzes the signals in both time domain and frequency domain. In this method, the received signal is decomposed by “db6” wavelet and the kurtosis of energy blocks of the low-frequency wavelet coefficients (Kc) is analyzed. At last, the mapping relationship between Kc and the normalized threshold for TOA estimation is created using polynomial fitting with degree 3. The simulation results show that the TOA estimation error of the proposed method is significantly less than the method without wavelet decomposition.

Multi-antenna Spectrum Sensing Based on Stochastic Resonance

Aiming at the problem of spectrum sensing in cognitive radio under low signal to noise ratio (SNR) and channel fading, proposed a spectrum sensing method based on Stochastic Resonance (SR) and multi-antenna. The received signal of each antenna will pass through SR, to enhance the source signal using noise energy. Then the output of multi-antenna will be combined by Equal Gain Combining (EGC), and Differential Energy Detection (DED_SR) and Periodogram Energy Detection (PED_SR) will be adopted. Simulation experiments compare the traditional Energy Detection (ED), DED_SR and PED_SR. The results show that the detection probability of PED_SR and DED_SR perform higher than ED, but the sensing time also perform higher.

Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional Energy Detection and Matched Filter

Heterogeneous Spectrum Sensing in Cognitive Radio Network using Traditional Energy Detection and Matched Filter

Performance analysis of spectrum sensing techniques over TWDP fading channels for CR based IoTs

In this paper, the performance of spectrum sensing techniques over two wave diffused power (TWDP) fading channels has been investigated for CR based IoT devices. The ubiquitous objects based on IoTs with cognitive capabilities are the future that would enable intelligent decisions to achieve any time, any place, interference-free and on-demand services. In this work, new closed-form expressions in terms of Marcum-Q function and Whittaker function for the spectrum detection probability over TWDP fading channels have been derived for different sensing techniques. The expressions are then used to optimize the decision threshold for IoT sensor nodes describing the optimal behavior of sensing over TWDP channels. The error probabability and Receiver Operating Characteristic (ROC) curves have been plotted and the operating point and limiting values of threshold for optimal performance have been identified. The performance loss of traditional energy detection (ED), cyclostationary based detection (CSD) and matched filtering detector (MFD) has been analyzed. The analytical results thus obtained are validated through monte-carlo simulations.

An UWB ranging method based on wavelet packet decomposition

In wireless sensor networks (WSNs), precise ranging or positioning using Ultra-Wideband (UWB) signal attracts a wide research interest. The advantages of non-coherent energy detection (ED) are low sampling rate and low complexity, but the traditional energy detection schemes only analyze UWB signal in time domain, so the ranging or positioning error is large. In this paper, a novel ranging method is proposed which analyzes UWB signal in joint time-frequency domain. In this method, wavelet packet decomposition (WPD) is used to get the energy distributions in different sub-bands, and then back propagation artificial neural network (BP-ANN) is utilized to establish the mapping relationship between energy distributions and transmission ranges. The simulation results show that the ranging error of the trained BP-ANN in this method is significantly less than the traditional ED methods.

Cooperative Sensing With Joint Energy and Correlation Detection in Cognitive Radio Networks

Cognitive radio networks (CRNs) are a promising solution to address the spectrum shortage issue. Spectrum sensing plays a key role to make such a scenario practical. In this letter, we propose a solution for the problem of spectrum sensing in CRNs aiming to increase the performance of the cooperative spectrum sensing. We design a cooperative spectrum sensing with detection based on two summary statistics, including energy and first-order correlation of the received samples of the signal. The probabilities of detection and false alarm are obtained as criteria for evaluating the performance of the CRNs. The performance evaluation is presented through simulations. The results show that the proposed method significantly improves the detection performance in cooperative spectrum sensing. It also highly decreases the probability of miss-detection in comparison with the traditional energy detection method.

Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

Subband Energy Based Reduced Complexity Spectrum Sensing Under Noise Uncertainty and Frequency-Selective Spectral Characteristics

The present work proposes a subband energy detection method that performs efficiently under noise uncertainty (NU) and frequency-selective channels. The critical impact of detrimental modeling uncertainties, such as NU, is analytically quantified and it is shown that the introduced method is robust to both NU and frequency-selectivity conditions. This is also the case for eigenvalue based sensing techniques, in contrast to traditional energy detector based sensing. Connections of the subband energy based approach and existing eigenvalue based methods are established analytically, which leads to a novel reduced complexity processing technique based on the difference between maximum and minimum subband energies. The proposed method is capable of providing accurate and robust performance with low signal-to-noise ratios (SNR) in the presence of NU. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due to the primary signal spectrum and/or the transmission channel. The validity of the offered expressions is justified through comparisons with respective results from computer simulations. The sensing performance is evaluated in different communication scenarios, with different frequency-selective channel models and primary user waveforms. The offered results indicate that the proposed methods provide quite significant savings in complexity, e.g., 78% reduction in the considered example case, while also improving the detection performance at low SNRs and in the presence of NU.