Performance Of Energy Detector Research Articles

New statistics on FTR fading over dynamic scenario and energy detector performance

New statistics on FTR fading over dynamic scenario and energy detector performance

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.

Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

<p>Spectrum sensing techniques are implemented for effective use of spectrum resources in the cognitive-radio. In this research work attempt has been made for the performance of energy detector with cooperative spectrum sensing using double dynamic threshold on the MATLAB software. Additive white gaussian noise is used and the frequency range between 54 MHz to 862 MHz is considered with wideband. Findings in receiver operative curve have been observed to analyze probability-of-detection (𝑃𝑑) under different values of probability-of-false alarm (𝑃𝑓) condition and diverse ranges of signal to noise ratio with different number of samples of input signal. Presence and absence of primary user has been marked by using a hypothetical model based on Neyman pearson approach. From the results, it has been observed that more the number of sample values, better is the probability-of-detection (𝑃𝑑) value as achieved for 32-QAM signal as compared to 16-QAM signal. Also, better results have been witnessed at -9db signal to noise ratio value as compared to -15db and -20db. This work provides almost 10% of enhancement in the probability of detection at -9db signal to noise ratio for 16-QAM modulated signal as compared to the existing model where the implementation of energy detector spectrum sensing technique through simulink model.</p>

Spectrum sensing performance of wireless cognitive radio sensor network with hard‐decision fusion over generalizedα−μfading channels

SummaryThe analysis of spectrum sensing performance of energy detection (ED)‐based wireless cognitive radio sensor network (ED‐WCRSN) with hard‐decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED‐WCRSN, considering channel errors, noise, and generalized fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed‐form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED‐WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found.

Multi-Antenna Assisted Spectrum Sensing for Cognitive Radio in Nakagami-M Fading Channel

CR (Cognitive Radio) is a key technology that enables the limited and inefficiently used frequency bands to be used more effectively with an opportunistic approach. Communication performance and continuity in cognitive radio networks are highly dependent on whether the spectrum sensing function is performed correctly or not. Spectrum sensing is a critical issue of cognitive radio technology because of the shadowing, fading, and time-varying natures of wireless channels. To sense the limited or unused frequency bands, different methods for spectrum sensing have been proposed. Here, improved energy detection is used for this work. Energy detection is a spectrum sensing technique based on measuring the received signal energy and deciding the presence or absence of the primary user by comparing the received energy level with a threshold. Fading channels shows that the speed of the SU increases, the energy detection performance decreases in deterioration in detection probability

Spectral minima tracking for improving the energy detection under noise power uncertainty

Spectrum sensing involves identifying the unused license bands in Cognitive Radio Networks. Energy Detection (ED) is the most widely studied form of spectrum sensing. ED detects potentially unknown signals and it is mainly characterized by its low complexity. However, the ED performance is considerably deteriorated by the presence of noise uncertainty (NU). To increase the robustness of ED in NU scenarios, in this work we propose to use an adaptive detection threshold, which is calculated from the noise power estimate obtained at each sensing interval. The noise power is estimated using the technique called Spectral Minima Tracking (SMT). Moreover, a correlation analysis is conducted to quantify the degree of relationship between the parameters of the SMT technique and the resulting noise power estimate. The findings made in the correlation analysis allowed to adjust the parameters of the SMT technique, and thus giving place to the improvement of the quality of the noise power estimate. Simulation results show an improvement in the detection rate of the adaptive ED due to the reduction of the bias in the noise power estimate, as compared to the non-adaptive ED.

Spatial Spectral Enhancement of Broadband Signals in a Towed Array Using Deconvolved Subband Peak Energy Detection

Conventional energy detection is a robust method that is usually applied to underwater broadband acoustic signal processing for towed arrays. Due to its low resolution, the weak target detection performance of conventional energy detection is severely degraded in shallow sea environments with strong acoustical reverberation. Subband peak energy detection is an effective method to improve the display resolution of conventional energy detection. However, subband peak energy detection produces false alarms due to the presence of high sidelobe levels. In order to improve the underwater target detection performance, a deconvolved subband peak energy detection method for towed arrays is proposed in this paper. Compared with conventional beamforming, minimum-variance distortionless response with forward–backward averaging and diagonal loading algorithm and subband peak energy detection, the proposed method could robustly provide higher-resolution results and suppress the fake peaks induced by subband peak energy detection. The performance of the proposed method was evaluated with simulation results, and the sea experimental data processing results show that the proposed method is effective in engineering applications.

Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning

<p>To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (<em>P</em><sub>od</sub>), probability of false alarm (<em>P</em><sub>ofa</sub>) is estimated to know threshold value. From results, it is clear that good performance in terms of <em>P</em><sub>ofa</sub> versus <em>P</em><sub>od</sub> in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.</p>

Analysis of Beam Sweeping Techniques for Cell-Discovery in Millimeter Wave Systems

Cell discovery is the procedure in which a user equipment (UE) finds a suitable base station (BS) to establish a communication link. When beamforming with antenna arrays is done at both transmitter and receiver, cell discovery in mm wave systems also involves finding the correct angle of arrival – angle of departure alignment between the UE and the detected BS. First, we consider the single BS scenario and present the mathematical model for the cell discovery problem. We present the details of the beam sweepingbased techniques and explain the beam combining scheme to reduce the training overhead. We analytically characterize the performance of energy detector at the UE for both beam sweeping and beam combining methods under some channel assumptions. Further, we consider the multiple BS case where BSs transmit synchronization signals along with directional beamforming. We analyze the performance of the energy detector in this case as well. We finally present simulation studies with channels generated using generic mm wave channel emulators and draw inferences on the role of various parameters on the cell discovery performance.

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems.

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

Design of Intelligent Reflecting Surface (IRS)-Boosted Ambient Backscatter Systems

Passive technologies like ambient backscatter communication (AmBC) and intelligent reflecting surface (IRS) hold enormous potential for ubiquitous connectivity of Internet of Things (IoT) devices in future wireless networks. However, despite many research efforts, AmBC has so far been unable to fulfil its potential for widespread deployment, due to the weakness of backscatter signal in the presence of strong direct-link interference from the original RF source. This limits the bit error rate (BER) and hence the transmission rate and range of AmBC systems. Meanwhile, the IRS offers new degrees of freedom in enhancing a variety of systems by transforming their propagation medium and signals. In this work, we devise a novel scheme to improve the detection performance of an AmBC system using an IRS located in its proximity. The IRS augments the backscatter signal quality at the receiver by adjusting its phase shifts to balance signal strengths, ultimately improving the performance of energy detection at the receiver. Our results clearly show that an IRS of reasonable size can considerably improve the BER performance of ambient backscatter, which is an important improvement for low power IoT systems.

Performance Analyses of Energy Detection Based on Square-Law Combining in MIMO-OFDM Cognitive Radio Networks.

Cognitive radio (CR) technology has the potential to detect and share the unutilized spectrum by enabling dynamic spectrum access. To detect the primary users’ (PUs) activity, energy detection (ED) is widely exploited due to its applicability when it comes to sensing a large range of PU signals, low computation complexity, and implementation costs. As orthogonal frequency-division multiplexing (OFDM) transmission has been proven to have a high resistance to interference, the ED of OFDM signals has become an important local spectrum-sensing (SS) concept in cognitive radio networks (CRNs). In combination with multiple-input multiple-output (MIMO) transmissions, MIMO-OFDM-based transmissions have started to become a widely accepted air interface, which ensures a significant improvement in spectral efficiency. Taking into account the future massive implementation of MIMO-OFDM systems in the fifth and sixth generation of mobile networks, this work introduces a mathematical formulation of expressions that enable the analysis of ED performance based on the square-law combining (SLC) method in MIMO-OFDM systems. The analysis of the ED performance was done through simulations performed using the developed algorithms that enable the performance analysis of the ED process based on the SLC in the MIMO-OFDM systems having a different number of transmit (Tx) and receive (Rx) communication branches. The impact of the distinct factors including the PU Tx power, the false alarm probability, the number of Tx and Rx MIMO branches, the number of samples in the ED process, and the different modulation techniques on the ED performance in environments with different levels of signal-to-noise ratios are presented. A comprehensive analysis of the obtained results indicated how the appropriate selection of the analyzed factors can be used to enhance the ED performance of MIMO-OFDM-based CRNs.

Analytical framework for the performance of energy detectors over α‐μ fading model with MRC diversity

SummaryThe advancements towards the 5G technology are significantly reliant on the effective utilization of the radio spectrum. The scarcity of the spectrum is a barrier to the employment of emergent wireless multimedia. The cognitive radio (CR) technology has materialized as a reassuring remedy to the present problem of spectral scarcity. This study explores the statistical characterization of energy detector‐based cognitive radio device alongside diversity reception. The analysis is carried out with the postulation that the transmitting signal encounters fading, characterized by α‐μ fading channel. Initially, we present a PDF expression of the α‐μ fading model when subjected to maximal ratio combining (MRC) diversity reception. Utilizing this expression, the closed‐form expression of the average probability of detection (Pd) and the average area under the receiver operating characteristics curve (AUC) are derived in the terms of Fox's H function. The detection threshold has been optimized to improve the detection performance by curtailing the probability of error. Additionally, asymptotic results for Pd with various diversity combining cases are also obtained under high signal‐to‐noise‐ratio (SNR) regime. Furthermore, the result of Pd is applied to explore the execution of cooperative spectrum sensing (CSS) with diversity reception. The accuracy and validation of the presented results are assured by comparison with the Monte Carlo simulation.

Improved Detection Performance of Energy Detection Based Spectrum Sensing in Cognitive Radio Networks

Background: In cognitive radio networks, spectrum sensing plays an important role in identifying the underutilized spectrum bands. Conventional spectrum sensing using energy detection method uses single detection threshold, which degrades the detection performance. Method: Therefore double detection threshold has been proposed for spectrum sensing in the literature to improve the detection performance, but the performance depends on the region between two thresholds termed as confusion state. Hence to improve the overall detection performance new re-sensing scheme has been proposed in this paper by varying the difference between thresholds by an improvement factor K. Results: The proposed method improves the detection performance compared to the single threshold method and double threshold method. Conclusion: Simulation results show that the proposed method operates better than the single threshold energy detection method and improves the detection performance at low signal to noise ratios.

Algorithm for Evaluating Energy Detection Spectrum Sensing Performance of Cognitive Radio MIMO-OFDM Systems.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.

Performance of energy detector based spectrum sensing over millimetre band communication channel with diversity reception

Performance of energy detector based spectrum sensing over millimetre band communication channel with diversity reception

Comparison of Passive Sonar Narrowband Detection and Wideband Detection Performance

Conventional passive sonar uses wideband beam energy for target detection. In the case of low signal-to-noise ratio and strong target interference, the performance of wideband energy detection is rapidly degraded. In view of the relatively stable line spectrum of the radiated noise of underwater targets, the performance of narrowband detection and wideband detection is considered. In stable line spectrum underwater target detection, narrowband detection has performance advantages over wideband detection. However, its actual performance has disadvantages such as sensitivity to noise frequency bands. In this paper, based on the sonar equation, the theoretical methods of wideband detection and narrowband detection are analyzed and the main factors affecting detection performance are given. Simulation and sea trial data processing results verify the effectiveness of the method.

Spectrum Sensing in Non-Gaussian Noise

Background: Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. Objectives: Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. Methods/findings: A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection. Keywords: NonGaussian noise; Fuzzy logic; Spectrum sensing; noise uncertainty

Spectrum sensing based on adaptive sampling of received signal

Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

On effective rate and energy detection based spectrum sensing over cascaded FTR fading channel

The detrimental effects of multipath fading and multi-hope/keyhole propagation of millimetre–wave (mm) on the effective rate (ER) and the detection performance of energy detector (ED) have been investigated in this article. Cascaded fluctuating two ray fading model have been used to characterize the mm-wave propagation environment. Utilizing probability density function-based approach, novel closed form mathematical expressions for ER, average detection probability (ADP) and average area under the receiver operating characteristics (AUC) for the single and multi-antennae receiver-based system have been derived. To this end, novel mathematical expression for instantaneous signal to noise ratio (SNR) at the output of maximal ratio combiner (MRC) is also obtained. Subsequently obtained results are employed to compute performance metrics for the collaborative spectrum sensing where each individual receiver consists of multiple antennae. Obtained expressions are useful to evaluate ER and ED performance over different fading scenarios impaired due to cascading effects, and also helps to maintain ER versus ADP trade-off by exploiting time-bandwidth product relation. Analysis shows a considerable degradation in ER and ADP as the channel cascading effect increases and employing diversity reception is useful to overcome the degraded performance. In the end, Monte-Carlo simulations are provided to validate the numerical accuracy of theoretically derived expressions.