Probability Of Missed Detection Research Articles

Energy-Efficiency-Based Optimal Relay Selection Scheme With a BER Constraint in Cooperative Cognitive Radio Networks

Due to the energy constraint of each cognitive user and potential secondary transmission errors, energy efficiency is very important for cognitive nodes in spectrum sensing and cooperative transmissions. In this paper, an energy awareness optimal relay selection (EAORS) scheme is proposed, in which the number of optimal relays is determined by a weighted objective function considering energy consumption in both spectrum sensing and the cooperative transmission phases, under the constraints of the global misdetection requirement and secondary cooperative transmission bit error rate (BER). In the proposed EAORS scheme, the authors investigate the tradeoff between detection accuracy and energy efficiency and construct the objective function as a weighted convex function of relay numbers with the consideration of global missing detection probability and cooperative transmission BER. This nonlinear convex problem is solved by applying numerical analysis to obtain the optimal relay numbers. Energy consumption analysis is conducted to show the effectiveness of the proposed EAORS scheme. Numerical results show that the proposed scheme outperforms a compressed sensing (CS)-based energy-efficient cooperative detection scheme and provides a tradeoff between detection accuracy and energy efficiency.

Cooperative spectrum sensing based on side information for cognitive radio sensor networks in internet of things applications

In the internet of things IoT applications, cooperative spectrum sensing of cognitive radio sensor networks CRSN may use electronic objects or sensor nodes to cooperatively detect the spectrum of primary user. How to effectively fuse the local detection data and make a global decision is critical for CRSN. In order to improve the detection accuracy of CRSN, we propose a cooperative spectrum sensing scheme based on side information, and the scheme uses a cooperative spectrum sensing framework and an efficient clustering algorithm that can meet the specific requirements of IoT applications. Though mathematical modelling, minimising the missing detection probability is converted into clustering nodes. The clustering algorithm is to be used to find the optimal distance and localisation of nodes. Simulation results show that the proposed cooperative spectrum sensing scheme has better performance than the conventional cognitive radio models such as equal gain combining, and maximal ratio combining algorithm. In addition, the influence of sensor nodes' density and distance range on the sensing performance is also analysed.

Multi-windows Threshold-based Frame Synchronization in Variable Coded Modulation Service

In this paper, an innovative and reliable frame synchronization scheme is proposed for second generation digital video broadcasting (DVB-S2) system in Variable Coded Modulation (VCM) service. Frame synchronization in VCM suffers many difficulties from large frequency offset, extremely low SNR and variable physical layer frame (PLFRAME) lengths. To cope with variable PLFRAME lengths, multi-windows are proposed and utilized. Multi- windows threshold-based search algorithm is presented with theoretical analysis to decrease the missed detection probability. Experimental results demonstrate that our proposed method is more robust and reliable than the existing approaches, especially in low signal to noise ratios (SNRs) and large frequency offsets.

Sensing‐throughput optimisation for multichannel cooperative spectrum sensing with imperfect reporting channels

The problem of multiband cooperative spectrum sensing with imperfect reporting channels is considered. It is assumed that the secondary users (SUs) make use of an energy detection scheme to sense a number of frequency subbands (assigned to primary users) and report their sensing results to a fusion centre through noisy channels. Exact expressions for the opportunistic throughput, false alarm and missed detection probabilities are derived in terms of the overall sensing-plus-reporting time, sensing subintervals, reporting subintervals and decision thresholds for each SU in each subband. Then, an optimisation problem is formulated that aims to maximise the opportunistic throughput of the cognitive radio network so as to maintain the interference incident on the primary network below a given level. The constraints of the optimisation problem are shown to be non-convex and the conditions under which these constraints become convex are examined. It is also shown that the objective function of the problem is non-convex and two suboptimal techniques are introduced to effectively solve the problem using the algorithms developed for solving convex optimisation problems.

Unified Analysis of Low-SNR Energy Detection and Threshold Selection

For spectrum sensing in cognitive radio networks, the IEEE 802.22 standard requires the detection of primary signals with a signal-to-noise ratio (SNR) as low as −20 dB and receiver sensitivity as low as −116 dBm. Under such low-SNR levels, the performance of a conventional energy detector is analyzed in this paper. The analysis includes novel expressions for missed-detection probability and area under the receiver operating characteristic (ROC) curve. Thus, a unified framework covering fading channels, square-law diversity combining, and cooperative spectrum-sensing scenarios is developed. The detection threshold is optimized to minimize the total error rate subject to bounded false-alarm and missed-detection probabilities, which outperforms traditional detection threshold selection. Numerical results and Monte Carlo simulation results with the IEEE 802.22 sensing requirements are provided and discussed.

An optimal fault detection threshold for early detection using Kullback–Leibler Divergence for unknown distribution data

The incipient fault detection in industrial processes with unknown distribution of measurements signals and unknown changed parameters is an important problem which has received much attention these last decades. However most of the detection methods (online and offline) need a priori knowledge on the signal distribution, changed parameters, and the change amplitude (Likelihood ratio test, Cusum, etc.). In this paper, an incipient fault detection method that does not need any a priori knowledge on the signals distribution or the changed parameters is proposed. This method is based on the analysis of the Kullback–Leibler Divergence (KLD) of probability distribution functions. However, the performance of the technique is highly dependent on the setting of a detection threshold and the environment noise level described through Signal to Noise Ratio (SNR) and Fault to Noise Ratio (FNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability). Thanks to this model, an optimisation procedure is applied to optimally set the fault detection threshold depending on the SNR and the fault severity. Compared to the usual settings, through simulation results and experimental data, the optimised threshold leads to higher efficiency for incipient fault detection in noisy environment.

Joint sensing and power allocation in wideband cognitive radio networks

This paper investigates the optimization problem in wideband cognitive radio networks. It focuses on jointly optimizing the decision threshold, sensing time and transmission power to maximize the aggregate throughput under the constraints of local interference, false-alarm probability, missed detection probability and maximal total power of each secondary user. Sequential parameter optimization strategy is introduced to deal with different optimization parameters in the optimization problem. It starts with power allocation first, with the optimal power allocation vector obtained by adaptive power allocation strategy, the gold section search method combined with Taguchi algorithm is proposed to find the optimal sensing time with the optimal power allocation vector, and the optimal decision threshold with the optimal power allocation vector and sensing time is optimized through iterative process. Simulation results show that the proposed algorithm can achieve a better performance and is effective in solving such optimization problems.

Dust storm detection using random forests and physical-based approaches over the Middle East

Dust storms are important phenomena over large regions of the arid and semi-arid areas of the Middle East. Due to the influences of dust aerosols on climate and human daily activities, dust detection plays a crucial role in environmental and climatic studies. Detection of dust storms is critical to accurately understand dust, their properties and distribution. Currently, remotely sensed data such as MODIS (Moderate Resolution Imaging Spectroradiometer) with appropriate temporal and spectral resolutions have been widely used for this purpose. This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying surface. Therefore, classification-based approaches could be deployed as an alternative. In this paper, the most relevant bands are chosen based on the physical effects of the major classes, particularly dust, cloud and snow, on both emissive infrared and reflective bands. In order to verify the capability of the methods, OMAERUV AAOD (aerosol absorption optical depth) product from OMI (Ozone Monitoring Instrument) sensor is exploited. In addition, some small regions are selected manually to be considered as ground truth for measuring the probability of false detection (POFD) and probability of missing detection (POMD). The dust class generated by RF is consistent qualitatively with the location and extent of dust observed in OMERAUV and MODIS true colour images. Quantitatively, the dust classes generated for eight dust outbreaks in the Middle East are found to be accurate from 7% and 6% of POFD and POMD respectively. Moreover, results demonstrate the sound capability of RF in classifying dust plumes over both water and land simultaneously. The performance of the physical-based approaches is found weaker than RF due to empirical thresholds that are not always true.

Selective-Reporting-Based Cooperative Spectrum Sensing Strategies for Cognitive Radio Networks

Cooperative spectrum sensing may cause a high sensing overhead to secondary users (SUs) and induce much interference to primary users (PUs) due to detection result reporting, although it can improve the detection probability. In this paper, we propose two novel selective-reporting-based cooperative spectrum sensing strategies, called random-selection reporting (RSR) and best-selection reporting (BSR), aimed at reducing the sensing overhead of SUs and limiting the induced interference to PUs. By considering the interference impact on decision reporting from PUs, we derive closed-form expressions of false-alarm probability and detection probability over Rayleigh fading channels for traditional, RSR, and BSR strategies, respectively. We also analyze the interference impact on PUs from cooperative SUs and then show that such interference can be limited by power control to satisfy a given quality-of-service (QoS) requirement of primary transmissions. Finally, numerical and simulation results reveal that the proposed strategies can remarkably reduce the sensing overhead without degrading the detection performance compared with the traditional case. Moreover, we show that both RSR and BSR can achieve minimized miss detection probability and sensing overhead by adjusting the sensing time allocation.

Temporal Spectrum Sensing for Optical Wireless Scattering Communications

Due to the unlicensed spectrum nature of the optical wireless communication, for a transceiver pair, the receiver needs to periodically sense the communication media, aiming to better schedule its transmission. Considering that the data symbols or channel parameters are unknown, we adopt a generalized likelihood ratio test (GLRT), which is known to be optimal with unknown parameters based on finite samples. This paper proposes two detection rules on whether there are optical signals falling into the receiver field of view within certain time slot(s), based on the sum number of received photons over all sensing slots, as well as the number of received photons in each slot. The formal one is named the sum-counting test and the latter one is named as the general GLRT. We design the corresponding detection schemes for both on-off keying (OOK) and pulse-position modulation (PPM) signal formats. We also provide asymptotic results on the miss detection probability. Numerical results show that, for OOK signal format, the general GLRT outperforms the sum-counting test at the cost of higher computational complexity, but for PPM signal format both detection schemes perform virtually the same.

Performance analysis of BDS for regional services with consideration on weighted factors

At the current stage, the service performance analysis of BeiDou satellite navigation system (BDS) can provide important references for BDS applications. Accuracy, integrity, availability, and continuity are important indicators of the service capability of the satellite navigation system. In this paper, the relationships among them are analyzed firstly. Secondly, for describing accuracy, position dilution of precision (PDOP) is selected and the quantitative calculation formulas of its availability and continuity are established. Then PDOP of BDS in Asia-Pacific region is simulated and analyzed, the results show that the average availability and continuity of PDOP in the service area during 24 h are up to 99.60% and 0.9947/h, respectively. Thirdly, this paper has a brief introduction to weighted least square algorithm of receiver autonomous integrity monitoring (RAIM) and analyzes the relationships among the probability of false alert, the probability of missed detection, threshold, the minimum detectable error and the number of visible satellites. The threshold and the minimum detectable error will increase with the increasing number of visible satellites when given the false alert probability and missed detection probability, respectively. Finally, the quantitative calculation formula of RAIM availability and continuity is presented and RAIM availability and continuity are simulated and analyzed as well, the results show that the average availability and continuity in the service area are up to 99.95% and 0.9984/h, respectively.

Asymptotic Performance of Energy Detector in Fading and Diversity Reception

Missed-detection probability expressions for energy detectors often involve infinite series and do not provide quick insights into the effects of operating conditions. To overcome these limitations, we develop novel asymptotic analyses by proposing an approximate probability density function (PDF) of a random variable $\beta$ , which, in general, can characterize fading channels in diverse operating conditions. The coefficients of the proposed approximate PDF of $\beta$ are obtained by matching the coefficients of the approximate PDF's series expansion (or coefficients of the approximate PDF's moment generating function (MGF)) with those of the exact PDF (or MGF) of $\beta$ . By using the proposed approximation, a unified closed-form asymptotic missed-detection probability is derived. Its usefulness is then demonstrated for fading channels without and with antenna diversity, for cooperative detection, and in co-channel interference. For each case, the sensing gain, which reveals the effect of the operating conditions on the detection performance, is determined explicitly. Furthermore, the asymptotic complementary area under the receiver operating characteristic curve, an alternative performance metric, is derived, and found to reveal the sensing gain. Numerical results verify the accuracy of our derived asymptotic expressions over a wider signal-to-noise ratio (SNR) range compared to the existing asymptotic solution, which is accurate only for high SNRs.

A preload cooperative sensing scheme with low overhead in cognitive radio networks

AbstractIn cognitive radio networks (CRNs), users can collaborate to improve the accuracy of spectrum sensing, but a large number of secondary users reporting their local sensing results may create significant overhead. In this paper, we propose a new pre‐sensing scheme, called preload cooperative sensing (PCS), which not only attains the given sensing accuracy for CRNs but also reduces the whole sensing time T. In order to reduce the sensing overhead in CRNs, the proposed scheme adopts two key technologies: selective reporting technology and pre‐sensing sequential detection technology. Selective reporting technology implies that only those users, which detect the presence of primary users, need to report the results, while pre‐sensing sequential detection technology is an asynchronous parallel scheme, which sets a threshold to determine the presence of primary users. Considering the preload sensing slots, we derive a formula to express the overall miss detection probability, and at a given Quality of Service (QoS) value, the sensing overheads of PCS are analyzed over Rayleigh fading channel. Also, we consider the overhead minimization problems in PCS. Simulation results show the superiority and efficiency of the PCS scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Cooperative Spectrum Sensing with Double Threshold and Censoring in Rayleigh Faded Cognitive Radio Network

The performance of cooperative spectrum sensing (CSS) with double threshold and censoring of cognitive radios (CRs) has been evaluated using maximal ratio combining (MRC) in the presence of Rayleigh faded sensing (S) and reporting (R) channels. Each CR senses the primary user through sensing channel and evaluates the energy of the received signal at its sensing channel input and performs double threshold based spectrum sensing. The qualified CRs are censored and only the selected CRs report their decisions to fusion centre. Censoring of CRs is done on the basis of the quality of R-channel. Typically two types of censoring such as rank-based and threshold-based censoring are considered to select the best CR based on the quality of the R-channels. The censoring of CRs not only improves the detection performance but also reduces the transmission overhead. The performance of CSS has been investigated in terms of average missed detection probability, total error probability, average number of transmitted bits and agility gain of the network with respect to relevant network parameters such as number of selected CRs, censoring thresholds, fail sensing probabilities and S and R-channel signal to noise ratios under both the perfect and imperfect channel estimation. The performance has been studied for hard decision fusion rule (majority) and soft decision fusion rule (MRC). The impact of channel variation rate on the sensing performance is also indicated. Further the performance of double threshold based CSS with censoring is compared with that of single threshold based CSS with censoring.

Novel Coarse Timing Synchronization Methods in OFDM Systems Using Fourth-Order Statistics

In this paper, the problem of coarse timing synchronization in orthogonal frequency-division multiplexing (OFDM) systems is investigated, and two new timing metrics with better performance are presented. The new metrics take advantage of two novel differential normalization functions that are based on the fourth-order statistics and are designed depending on the value of carrier frequency offset (CFO). The proposed timing metrics are theoretically evaluated using two different class separability criteria. It is shown that the new schemes considerably increase the difference between the correct and wrong timing points in comparison with previous methods. The computational complexity of the new methods is obtained, and their superior detection performances are also demonstrated in terms of probabilities of false alarm and missed detection. The results indicate that due to a significant improvement in missed detection probability (MDP), the new methods offer a considerably wider range of acceptable thresholds.

Achieving Antenna and Multipath Diversities in GLRT-Based Burst Packet Detection

Robust detection of the arrival of a packet is challenging in burst wireless communications, because the detection performance is vulnerable to channel fading. In this paper, we study strategies to achieve diversity to combat with channel fading in the detection regime. The contributions of this paper are twofold. (i) The cyclic-delay diversity (CDD) combined with multiple-antenna generalized-likelihood-ratio-test (GLRT) detection is proposed to achieve both antenna and multipath diversities in the detection regime, in both time domain (TD) and frequency domain (FD), where the multipath diversity is obtained by CDD. (ii) GLRT detectors with multiple antennas under frequency-selective multipath fading channel in TD and FD, i.e., TD-GLRT and FD-GLRT, are derived. Miss-detection probability (MDP) and false-alarm probability (FAP) of TD-GLRT and FD-GLRT are analyzed, based on which, the diversity gains of both detectors are obtained. Results show that full diversity, offered by both independent antennas and multipath components, is achieved with the proposed approach for both TD-GLRT and FD-GLRT, no matter whether the assisted sequence has a perfect autocorrelation property or not. In addition, for TD-GLRT, assisted sequences with a non-perfect autocorrelation property may lead to a significant SNR loss due to multipath, while for FD-GLRT, the SNR loss is insignificant.

Multiobjective Optimization of Linear Cooperative Spectrum Sensing: Pareto Solutions and Refinement.

In linear cooperative spectrum sensing, the weights of secondary users and detection threshold should be optimally chosen to minimize missed detection probability and to maximize secondary network throughput. Since these two objectives are not completely compatible, we study this problem from the viewpoint of multiple-objective optimization. We aim to obtain a set of evenly distributed Pareto solutions. To this end, here, we introduce the normal constraint (NC) method to transform the problem into a set of single-objective optimization (SOO) problems. Each SOO problem usually results in a Pareto solution. However, NC does not provide any solution method to these SOO problems, nor any indication on the optimal number of Pareto solutions. Furthermore, NC has no preference over all Pareto solutions, while a designer may be only interested in some of them. In this paper, we employ a stochastic global optimization algorithm to solve the SOO problems, and then propose a simple method to determine the optimal number of Pareto solutions under a computational complexity constraint. In addition, we extend NC to refine the Pareto solutions and select the ones of interest. Finally, we verify the effectiveness and efficiency of the proposed methods through computer simulations.

Energy Detection Technique for Adaptive Spectrum Sensing

The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage. The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum. Such strategy works in cognitive radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not. The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users. The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms. In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms. For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 0.1.

Optimal Cooperative Spectrum Sensing in Interference-Aware Cognitive Radio Networks

In order to protect the primary user from being interfered, most of the related works focus only on the restriction of the missed detection probability, which may cause over-protection of the primary user. Thus the interference probability is defined and the interference-aware sensing model is introduced in this paper. The interference-aware sensing model takes the spatial conditions into consideration, and can further improve the network performance with a good spectrum reuse opportunity. Meanwhile, although the detection performance can be further improved by the cooperative spectrum sensing, it also introduces additional reporting time corresponding to the number of cooperative users (CUs), which may decrease the throughput of the CRN. Motivated by the above, in this paper, we study the throughput tradeoff for interference-aware cognitive radio networks. For cooperative spectrum sensing, the Logic-OR fusion rule is used. By jointly optimizing the sensing time and the number of the CUs, the maximum throughput can be achieved. Theoretical analysis is given to prove the feasibility of the optimization and numerical simulations also show that the maximum throughput can be achieved when the sensing time and the number of the CUs are both optimized.

Improved Iterated-corrector PHD with Gaussian mixture implementation

Many filter algorithms based on the probability hypothesis density (PHD) filter have been proposed to solve the multi-target tracking (MTT) problem. Most of them are applied to single-sensor case. As a simple and feasible multi-sensor filter algorithm, the Iterated-PHD filter is influenced by the order of the sensor updates and the probability of detection. In this paper, an improved algorithm with a modified update formula is proposed to deal with the above problems. In this algorithm, the original detection probability is divided into two parts: the improved miss-detection probability and the improved detection probability, which take the order of the sensor updates and the original detection probability of each sensor into consideration simultaneously. The effectiveness of the proposed algorithm is verified by the simulation results.