Improved Energy Detector Research Articles

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.

Utilizing double-shadowed Rician fading over energy detector-based spectrum sensing with diversity reception

This study explores the capabilities of an Energy Detector (ED)-based Cognitive Radio (CR) device in a double shadowed (DS) Rician distribution environment for spectrum sensing. The effectiveness of the ED is evaluated using metrics such as average probability of detection (PD) and average area under the receiver operating characteristics curve (AUC). The investigation extends to single-input-multiple-output (SIMO) channel conditions, considering maximal ratio combining (MRC) and square-law selection (SLS) diversity techniques. To provide a comprehensive understanding of the system, the study also includes the asymptotic performance analysis of diversity techniques, including MRC, equal gain combining (EGC), and selection combining (SC). The findings indicated a notable enhancement in detection performance through the application of diversity combining techniques. Among the evaluated schemes, MRC demonstrated the most favorable results, followed EGC, while SC yielded comparatively weaker outcomes. To further minimize error probability, the detection threshold is optimized, resulting in improved energy detection statistics, even in scenarios involving diversity. The formulations are additionally applied to cooperative spectrum sensing (CSS). It further noted that fading parameter directly influence the array gain, while diversity gain only depends on the number of diversity branches. The accuracy and validity of the proposed results are confirmed through Monte-Carlo simulation.

Performance Estimation of Improved Cooperative Spectrum Sensing under Fading Environment

To deal with spectrum scarcity, Cognitive radio has been considered as a resolving technology. Energy detection(ED) is the most preferable sensing technique due to its lower complexity, ease of working and non-dependency on primary user data requirements. Although having many advantages, ED has some practical limitations like low SNR, shadowing, erroneous reporting channels and multipath fading. Here, a comparative study is done to check the effect of such parameters. And with simulation, it is proven that Cooperative spectrum sensing can reduce the effect of these confines. In this paper, we have also simulated the improved version of ED where decision making is done cooperatively. The results show that the performance of Improved Energy Detection (IED) is more efficient than the Classical ED, not only for AWGN, but also under fading environment. Cooperative spectrum sensing enhances the performance further. Under fading environment, the majority logic with centralized fusion centre performs better than other techniques. In this paper the performance estimation of Improved Energy Detection technique is done under Rayleigh fading environment and the positive effect of cooperation is also evaluated considering erroneous reports and imperfect reporting channels.

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

ANALYSIS OF ENERGY EFFICIENCY USING IED WITH SELECTION COMBINING IN PROPOSED CSS NETWORK OVER WEIBULL CHANNEL

In the Present Scenario, a message can be transmitted to the user, if the signal in which it contains must be efficient and accurate. There are many parameters like noise signals and unknown signals etc, which shows great impact on signal efficiency and its range. The removal of unknown signals is the primary objective in order to prevent loss of information from the signal. Unknown signals which causes disturbances in effective signal transmission are detected and its energy efficiency is compared before and after removal of unknown signals. This Research Paper mainly uses a Improved Energy Detector which consists of Cognitive nodes which helps the identification of unknown signals by applying various variations (i.e fading environment). Each nodes are subjected with some number of antennas. Probability functions are used to detect the false probability alarm signals which causes interrupts in the signal. Analysis of overall channel is done throughout the process of detecting and removing of unknown signals.

A Comprehensive Analysis of Energy Efficiency Using Cooperative Spectrum Sensing Network

The proposed cooperative spectrum sensing network presented in this paper has potential properties that could improve the detection probability. The proposed network consists of multiple cognitive radio (CR) nodes. Each CR node is equipped with several antennas, and selection combining is used to identify the highest value of sensing data associated with each antenna. Further, this sensing data is passed to fusion center and primary user activity is identified at the fusion centre using the fusion rules. This study mainly focuses on identifying unknown signals in a Rayleigh fading environment using an improved energy detector. Initially, the novel analytical closed-form of expressions for false alarm and missing detection probabilities was provided under Rayleigh fading with multiple antennas at each CR. Further, the performance of the total error rate is studied under single and multiple antenna scenarios, and a quantitative evaluation is offered to determine average throughput and energy efficiency performance. Later, to simulate the performance with a solid foundation of mathematical analysis, a simulation test-bed is created in MATLAB. Finally, the performance is improved with the proposed scheme compared to conventional scheme.

An Improved Energy Detection Scheme Based on Channel Prediction In CR Networks

Cognitive radio is considered as an intelligent wireless communication system proposed to improve the utilization of the radio electromagnetic spectrum. In CR technology the secondary users take the responsibility of dynamically sensing and accessing any unused channels in the spectrum allocated to the licensed users. As spectrum sensing consumes considerable energy, predictive methods for inferring the availability of spectrum holes can reduce energy consumption of the unlicensed users to only sense those channels which are likely to be idle. It also helps to improve the spectrum utilizations. Several prediction techniques have been used to predict spectrum utilization. However, most of current approaches do not consider seasonality in spectrum workload, for example most of the channels are busy during business hours in mobile phone bands. This paper; proposes a channel status predictor based on the multiplicative seasonal model called Holt-Winters’ method. The proposed prediction method has the ability to adapt with changes in trends and seasonal patterns of the sensing observations. Performance analysis and the accuracy of the channel status prediction schemes are investigated.

Quantitative models for friendly jammer trustworthiness evaluation in IoT networks

Due to the open nature of the wireless communication in an Internet of Things (IoT) network, transmitted messages are easy to eavesdrop. To address the eavesdropping attack, we consider an IoT-based smart home where an IoT device (IoT-D) is selected as a friendly jammer to send artificial noise (AN) to protect transmitted messages from being eavesdropped. However, the selected IoT-D may send partial (or even none) AN, which is called an untrusted jammer. To avoid selecting an untrusted jammer, we aim to design two quantitative models from the perspective of time and space to evaluate the trustworthiness of IoT-Ds. Firstly, we adopt an improved energy detector to better counter signal-to-noise ratio (SNR) fluctuation of AN detection. Secondly, based on the detection results, we adopt two trust models: (1) the spatial-oriented beta trust model, (2) the time-oriented hidden Markov model (HMM)-based trust model. In the first model, direct and indirect trust of an IoT-D are both calculated to obtain an integrated trust value. In the second model, the probability of trusted state is calculated as the trust value, and time intervals between detection results of an IoT-D are taken into consideration. Numerical results are presented to demonstrate the performance of our improved energy detection method and two trust models.

Improved Collaborative Spectrum Sensing Scheme for Maritime Cognitive Radio

Expeditious growth in wireless networks for numerous wireless services and applications lead to the increase in demand for radio spectrum in both terrestrial and marine wireless communications. Radio spectrum is scarce as the available spectrum is already been allocated to various applications. Cognitive radio technology is an optimistic solution for the spectral scarcity. In Cognitive Radio Networks (CRN), the unused licensed bands are dynamically accessed by the unlicensed secondary users for data transmission. Spectrum Sensing (SS) is the key technique to detect the presence or absence of the primary users. SS for terrestrial wireless communication have been studied vastly. This paper is aimed to study SS for Maritime Cognitive Radio Networks (MCRN) which is daunting as SS in MCRN depends on the sea state. Existing work on SS in MCRN deals with Classical Energy Detection (CED) which is a straight forward procedure with low complexity and can be applied generally to any signal irrespective of its format. Here we intend to perform SS in MCRN using Improved Energy Detection (IED) which surpasses the performance of CED without ruining its general attributes. Evaluations and analysis are carried out using detection probability performance metric for both CED and IED, simulated and compared for different sea states.

Throughput performance of cooperative spectrum sensing network with improved energy detectors and SC diversity over fading channels

Throughput performance of cooperative spectrum sensing network with improved energy detectors and SC diversity over fading channels

Analysis of energy‐efficient cooperative spectrum sensing with improved energy detectors and multiple antennas over Nakagami‐q/n fading channels

SummaryCognitive radio (CR) is an advanced technique used for the utilization of radio spectrum. A CR node uses the radio spectrum of a licensed user (called as primary user, PU) deviously. In this work, a cooperative sensing network (CSN) with improved energy detection (IED)‐based CR nodes is proposed. Each CR node consists of multiple antennas and perform themselves selection diversity (SC) technique. Each CR node senses a PU through erroneous sensing channel and reports its sensing data (1‐bit binary‐decision) to the fusion center (FC) through erroneous reporting channels. At FC, existence of PU (presence or absence) is identified using k‐out‐of‐N fusion rule. The novel expressions for detection probability over noise plus Nakagami‐q/n fading channels are derived and the mathematical frameworks for analysis of throughput and energy‐efficiency of the proposed network are developed. The simulation testbed is also developed and presented in this work to validate the analytically obtained performance characteristics. For several network parameters values, the investigation on comparison between IED and traditional energy detection method for various decision fusion rules is studied. Further, performance characteristics are analytically illustrated for throughput and energy‐efficiency over noise plus Nakagami‐q/n fading channels. The impact of diversity technique and effect of fading severity parameters on the throughput and energy‐efficiency are also investigated. The effect of channel error on total error performance for both proposed and conventional networks is shown. Finally, optimal fusion rule and optimal network parameters values correspond to maximum throughput and maximum energy‐efficiency are also identified.

Spectrum Sensing on High Density Cognitive Radio Vehicular Ad Hoc Network

Intelligent Transport System (ITS) has emerged as the most probable technology for improved transport experience more so in environments with high vehicular density. Effective vehicular communication is however hindered by spectrum scarcity due to the already crowded licensed spectrum. This has led to the emergence of Cognitive Radio (CR) systems as a solution to the spectrum scarcity problem. A crucial component in CR is spectrum sensing. Various spectrum sensing techniques including Cyclostationary, Matched Filter, and Energy detection have been proposed and applied with varied outcomes. In this paper, the above mentioned detection techniques are discussed and an improved energy detection based cooperative spectrum sensing scheme is proposed for improved communication in vehicular ad hoc networks (VANET). The proposed scheme showed an improvement in the performance of a network which in turn could lead to more efficient utilization of spectrum.

Analysis of energy detector with improved ED and variable threshold ED suitable for digital TV IEEE 802.22 WRAN

Now a days demand of wireless devices are increasing very rapidly by different users, while available spectrum resource is very limited. To overcome the need of different users, spectrum should be utilized efficiently. Cognitive Radio is introduced to access the spectrum dynamically by taking the permission of the licensed users for specific time interval when their spectrum is freely available. For this task of CR, many spectrum sensing techniques are introduced to sense the spectrum. Here in this paper, we have focused on energy detector(ED) spectrum sensing technique and overcome the problems with the ED at low SNR. Improved Energy Detector and Variable Threshold Energy Detector (VTED) give better results than normal ED technique which has been demonstrated through different receiver operating characteristics. Under Cooperative Spectrum Sensing (CSS) environment, it has been shown that OR logic gives probability of detection greater than 0.9 rather than the AND logic for optimized number of cooperative users 10. Results have been shown that VTED technique overcome the low SNR region problem of normal ED technique with higher probability of detection i.e. greater than 0.9 for cognitive radio networks.

Artificial neural network design for improved spectrum sensing in cognitive radio

Dynamic Spectrum Access/Cognitive Radio systems access the channel in an opportunistic, non-interfering manner with the primary network. These systems utilize spectrum sensing techniques to sense the occupancy of the primary user. In this paper, an artificial neural network based hybrid spectrum sensing technique is proposed, which considers sensing as a binary classification problem to detect whether the primary user is idle or busy. The proposed scheme utilizes energy detection and likelihood ratio test statistic as features to train the neural network. Moreover, we demonstrate the impact of hyperparameter tuning and carry out the detailed study of it, yielding a combination of best-suited hyperparameters. The performance of the proposed sensing scheme is validated on primary signals of various real world radio technologies acquired with an empirical testbed setup. We conclude that the best performing configuration results in an increase of approximately 63% in detection performance compared to classical energy detection and improved energy detection sensing schemes when averaged over all the radio technologies considered in this work.

Optimized cooperative spectrum sensing network analysis in nonfading and fading environments

SummaryThe proposed cooperative spectrum sensing (CSS) network is equipped with multiple antennas and an improved energy detector (IED) scheme at each cognitive radio (CR). Each CR in the network receives the information about the primary user (PU) in the form of binary decisions at multiple antennas. Diversity technique called selection combining (SC) scheme is used at multiple antennas to select the maximum value of sensing information present at multiple antennas. Finally, sensing information will be passed to the fusion center (FC) through reporting channel, and the final decision about PU is made at FC using fusion rules. Initially, we have derived the novel missed detection probability expressions for AWGN channel, Rayleigh, and Rician fading environments. Later, the closed form of optimized expressions for proposed CSS network parameters are derived to achieve an optimal performance. The closed form of optimized expressions such as number of CR users (Nopt), normalize threshold value (λn,opt), and an arbitrary power of the received signal (popt) are derived under various fading environments. The performance is evaluated using complementary receiver operating characteristics (CROC) and total error rate curves. The MATLAB‐based simulations are evaluated with the strong support of theoretical expressions. Finally, various simulation parameters such as sensing channel SNR, the error rate in reporting channel, threshold value, and number of antennas at each CR are considered in the simulation to show the effect on the performance of proposed CSS network.

Detection of Primary User in Wide-band Cognitive Radio Networks over Fading Channel using Compressed Sensing

In wideband cognitive radio networks, Nyquist sampling rate is very challenging problem. It required expensive high speed analog to digital converter and large storage spaces. Lately, compressive sensing has been emerged as significant solution to crack the conventional sampling rate requirements. It proved the ability to sample below Shannan-Nyquist criteria and reconstructing back the signal after considerable dimensional reduction. Mostly in cognitive radio networks, energy detection is widely used due to its simple implementation and blind detection property. However, regardless that energy detection is subject to noise uncertainty as well as shadowing and fading which deteriorate its detection performance. Several articles have been published to improve energy detection performance using large number of measurements. In this paper, since, the detection performance using small number of measurements or compressed measurements achieved significant performance using energy detection under additive white Gaussian noise channel. This motivated us to investigate the performance of compressed measurements-based detection over fading channels which has not been studied yet. The proposed algorithm has been implemented using MATLAB. We also studied the tradeoff between the compression ratios and using fraction of transmitted signal and its impact on detection performance and threshold choice. In comparison with the ordinary compressed energy detection over the Rayleigh fading channel the results reveal that the proposed enhanced compressed measurements-based energy detection is better in performance of detection.

Reinforcement Learning Based Full-Duplex Cognitive Anti-jamming Using Improved Energy Detector

In this paper, reinforcement learning (RL) based cognitive anti-jamming system employing full duplex tactical radio is investigated under electromagnetic spectrum warfare scenario. Firstly, the analytical expressions of jamming sensing based on improved energy detection are derived to calculate the reward metric. Then, we propose the multidomain anti-jamming strategies based on different learning algorithm and the accurate reward. Simulation results indicate that learning-based cognitive anti-jamming strategies may increase about 25% of the throughput of tactical radio. Moreover, the upper confidence bound and Thompson sampling strategies almost have the same performance and they are superior to other RL anti-jamming schemes.

Cooperative Spectrum Sensing Over Imperfect Reporting Channel using an Improved Energy Detector in Multiple Antenna Based Cognitive Radio

The presentation research of agreeable variety detecting with the help of a complicated energy finder is displayed in this paper. Subjective radios contain of severa reception apparatuses. each highbrow radio distinguishes essential customer sign with improved energy location, i.E., control 'p' of the adequacy of important examples. each CR takes its very very own selection and advances to the aggregate focus. The combination hobby melds all of the stop, and sincerely the final end of the nearness or nonattendance of the vital purchaser is executed. The articulations for the synthetic alert opportunity and the likelihood of left out region were determined, and the complete mistake price is determined. Streamlining of the all out amount of CR, strength discovery control 'p' and the quantity of reception apparatuses at every CR is completed with the help of diagram and articulations thru diminishing the all out blunder fee. With the assist of numerous reception apparatuses with low SNR amongst PU-CR joins, it is validated that we can accomplish a base blunder price

Limited length coding and improved energy detection for underwater photoelectric signal recovery

In underwater wireless optical communications (UWOC), low signal-to-noise ratio (SNR) and baseline drift problems caused by the attenuation of seawater channels and limited bandwidth of the transceiver system deteriorate the quality of the received signal and result in a higher bit error rate (BER). The transmission characteristics of underwater optical communication systems are analyzed, and a scheme combining length-limited coding and improved energy detection is proposed to improve the detection performance under low SNR and baseline drift. Experiments show that the scheme can improve the performance of the intensity-modulated direct-detection nonreturn-to-zero UWOC system. The BER performance of 3 × 10 − 4 can be achieved at SNR = 5.14 dB, which lays the foundation for long-distance UWOC.

Improved Energy Detection With Laplacian Noise in Cognitive Radio

Contaminated by Laplacian noise, the performance of energy detection (ED) degrades. To address this problem, an improved energy detection (i-ED) is proposed. Instead of the square of the received signal amplitude in the ED, an arbitrary exponent varying from 0 to 2 is adopted in the proposed algorithm. By deriving the expressions of the false alarm probability and detection probability, we propose an approach to determine the optimal exponent for a given cognitive radio system scenario. Both theoretical analysis and simulation results demonstrate that, by selecting the optimal exponent, the proposed i-ED outperforms ED and the absolute-value cumulating detection in terms of detection performance and robustness to the noise uncertainty.