Probability Of Missed Detection Research Articles

Using Trust to Establish Cooperative Spectrum Sensing Framework

Cooperative spectrum sensing in cognitive radio networks need many users to carry out because sensing performace is influenced by the credibility of a single user. A trsut based cooperative spectrum sensing framework is put forward for establishing the trust value of each user in view of local sensing differences, sensing location factors, control channel conditions, which can accurately weigh a single user's information at the CR base station. Simulation results show that while applied to concrete models the proposed framework has lower missed detection probability under a certain false alarm probability.

Optimal Scheduling of Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio (CR) networks, secondary users can be coordinated to perform spectrum sensing so as to detect primary user activities more accurately. However, more sensing cooperations for a channel may decrease the transmission time of the secondary users, or lose opportunities for exploiting other channels. In this paper, we study this tradeoff by using the theory of oartially observable Markov decision process (POMDP). This formulation leads to an optimal sensing scheduling policy that determines which secondary users sense which channels with what miss detection probability and false alarm probability. A myopic policy with lower complexity yet comparable performance is also proposed. We further analytically study the properties and the solution structure for the myopic and the optimal policies under a simplified system model. Theoretical results reveal that under certain conditions, some simple but robust structures of the value function exist, which lead to an easy way to obtain the solution of POMDP. Moreover, the cooperative sensing scheduling problem embedded in our POMDP, which is generally a hard combinatorial problem, can be analyzed in an efficient way. Numerical and simulation results are provided to illustrate that our design can utilize the spectrum more efficiently for CR users.

Wide-Angle Lens Array Spatial Acquisition With Ultrawide Field of View in Optical Links Under Atmospheric Scintillation and Background Radiation

We propose a wide-angle lens array scheme, which can provide spatial acquisition with ultrawide field of view (FOV) in free-space optical (FSO) links. This acquisition array is composed of sub-arrays, with each subarray including receiving elements. The elements in a subarray are arranged in the same direction to receive the laser beacon together, while the subarrays are arranged in different directions to cover an ultrawide FOV (a hemisphere with a solid angle of ). The elements in a subarray can increase the total aperture size to restrict the laser power fluctuation induced by atmospheric scintillation. The subarrays can disperse the ultrawide FOV to reduce the received background power on each element. Therefore, the elements in a subarray and the subarrays can both contribute to the improvement of the SNR, which can improve the spatial acquisition performance of the wide-angle array. The missed detection probability (MDP), as a parameter to describe the spatial acquisition performance, is investigated for the wide-angle lens array spatial acquisition scheme under atmospheric scintillation and background radiation. Results show that the proposed array scheme can improve the spatial acquisition performance degraded by atmospheric scintillation and background radiation. The acquisition performance can be further enhanced with the increase of element number in a subarray and subarray number , where the elements in a subarray and the subarrays are designed to suppress atmospheric scintillation and background radiation, respectively.

Weighted RAIM for APV: The Ideal Protection Level

International standards require the use of a weighted least-squares approach to onboard Receiver Autonomous Integrity Monitoring (RAIM). However, the protection levels developed to determine if the conditions exist to perform a measurement check (i.e. failure detection) are not specified. Various methods for the computation of protection levels exist. However, they are essentially approximations to the complex problem of computing the worst-case missed detection probability under a weighted system. In this paper, a novel approach to determine this probability at the worst-case measurement bias is presented. The missed detection probabilities are then iteratively solved against the integrity risk requirement in order to derive an optimal protection level for the operation. It is shown that the new method improves availability by more than 30% compared to the baseline weighted RAIM algorithm.A version of this paper was first presented at the US Institute of Navigation (ION) GNSS 2009 Conference in Savannah, Georgia.

Error Performance of Channel Coding in Random-Access Communication

A new channel coding approach was proposed in [1] for random multiple access communication over the discrete-time memoryless channel. The coding approach allows users to choose their communication rates independently without sharing the rate information among each other or with the receiver. The receiver will either decode the message or report a collision depending on whether reliable message recovery is possible. It was shown that, asymptotically as the codeword length goes to infinity, the set of communication rates supporting reliable message recovery can be characterized by an achievable region which equals Shannon's information rate region possibly without a convex hull operation. In this paper, we derive achievable bounds on error probabilities, including the decoding error probability and the collision miss detection probability, of random multiple access systems with a finite codeword length. Achievable error exponents are obtained by taking the codeword length to infinity.

Identification of low-level point radioactive sources using a sensor network

Identification of a low-level point radioactive source amidst background radiation is achieved by a network of radiation sensors using a two-step approach. Based on measurements from three or more sensors, a geometric difference triangulation method or an N -sensor localization method is used to estimate the location and strength of the source. Then a sequential probability ratio test based on current measurements and estimated parameters is employed to finally decide: (1) the presence of a source with the estimated parameters, or (2) the absence of the source, or (3) the insufficiency of measurements to make a decision. This method achieves specified levels of false alarm and missed detection probabilities, while ensuring a close-to-minimal number of measurements for reaching a decision. This method minimizes the ghost-source problem of current estimation methods, and achieves a lower false alarm rate compared with current detection methods. This method is tested and demonstrated using: (1) simulations, and (2) a test-bed that utilizes the scaling properties of point radioactive sources to emulate high intensity ones that cannot be easily and safely handled in laboratory experiments.

Despreader for direct sequence spread spectrum system and its performance analysis

A new type of despreader for direct sequence spread spectrum signal is proposed. Compared with traditional despreaders, the new despreader does not contain hard decision ware or handle binary sequence any more, and the locally stored spread spectrum signals are pre-modulated baseband signals (such as Gaussian minimum shift keying (GMSK) signals), which are much more similar to the received spread spectrum signals. Moreover, the missed detection probability of the despreader is about one order of magnitude lower than that of traditional ones. Based on the maximum likelihood criterion and phase probability density function of demodulated signal, a new method of analyzing the despreaders’ performance is put forward, which is proved to be more accurate than traditional methods according to the numerical results. Finally, an adaptive despreader under different signal-to-noise ratios is given.

On the Accuracy of the Gaussian Approximation With Linear Cooperative Spectrum Sensing Over Rician Fading Channels

Linear cooperative spectrum sensing has been proposed for cognitive radio networks which are often limited by the achievable complexity of the devices involved. The method is based on the Gaussian approximation of the summary statistics sent to the fusion center. The goodness of this approximation is investigated in this letter in the case of block Rician fading. The impact of the number of secondary users, sampling times, and the Rician factor are assessed by an analytic method proposed to calculate the false-alarm and the missed-detection probabilities.

Cooperative Diversity of Spectrum Sensing for Cognitive Radio Systems

Spectrum sensing is a crucial issue in cognitive radio networks. To improve the sensing performance, cooperation among the secondary users can be utilized to collect space diversity. In this paper, we quantify the diversity order for various cooperative spectrum sensing strategies. We first notice that, in a spectrum sensing problem, diversity should reflect both the false alarm and missed detection behaviors since they respectively capture the efficiency and reliability of the overall cognitive system. Hence, we consider both false alarm and missed detection probabilities individually and jointly via the average error probability. With the knowledge of both the noise strength and the primary user signal strength, the threshold of the Neyman-Pearson detector can be varied to control the system performance in desirable ways. Interesting tradeoffs between system efficiency and reliability are found in various cooperative strategies and analytical results are presented to guide practical system designs with different preferences.

Analysis of interpolated channel estimation for mobile OFDM systems

In an OFDM system using pilots for channel estimation, time interpolation among pilots of different OFDM symbols is commonly used to improve the estimate. The estimated channel impulse response may be also windowed to further reduce noise and disturbances. However, for a transmission over a time-varying channel, suboptimal time interpolation, implemented with a filter having only a few taps not matched to the maximum Doppler frequency, degrades channel estimation. In this paper we provide an analysis of the effects of pilot time interpolation over time-varying channels. We show in particular that suboptimal time interpolation yields aliases in the estimated channel impulse response and we derive a closed-form expression of the aliases¿ variance. As aliases can lead to an erroneous estimate of channel length and consequent errors in windowing, we propose a technique to detect aliases and correct the channel length estimate. Parameters of the detection techniques are optimized by an analysis that provides closed-form expressions of the false alarm and miss detection probabilities. Numerical results show the merits of the proposed techniques in a mobile transmission of the terrestrial digital video broadcasting standard (DVB-T).

Mitigating selective forwarding attacks with a channel-aware approach in WMNS

In this paper, we consider a special case of denial of service (DoS) attack in wireless mesh networks (WMNs) known as selective forwarding attack (a.k.a gray hole attacks). With such an attack, a misbehaving mesh router just forwards a subset of the packets it receives but drops the others. While most of the existing studies on selective forwarding attacks focus on attack detection under the assumption of an error-free wireless channel, we consider a more practical and challenging scenario that packet dropping may be due to an attack, or normal loss events such as medium access collision or bad channel quality. Specifically, we develop a channel aware detection (CAD) algorithm that can effectively identify the selective forwarding misbehavior from the normal channel losses. The CAD algorithm is based on two strategies, channel estimation and traffic monitoring. If the monitored loss rate at certain hops exceeds the estimated normal loss rate, those nodes involved will be identified as attackers. Moreover, we carry out analytical studies to determine the optimal detection thresholds that minimize the summation of false alarm and missed detection probabilities. We also compare our CAD approach with some existing solutions, through extensive computer simulations, to demonstrate the efficiency of discriminating selective forwarding attacks from normal channel losses.

An Advanced Quiet-Period Management Scheme for Cognitive Radio Systems

In cognitive radio (CR) systems, the efficiency of the primary user detection scheme directly affects the system performance. In this paper, we consider CR systems adopting quiet period for primary user detection. Specifically, we propose an enhanced quiet-period management scheme that utilizes two types of sensing intervals and two detection thresholds for energy detection, and can be used with and without feature detection. By using Markov analysis, we derive some performance measures for the system using the proposed scheme, including the channel utilization of CR systems and the detection delay of a primary user. Numerical results show that the proposed scheme heightens the maximum channel utilization of CR users, sustaining the miss-detection probability to be below the predefined value.

Spectrum Handoff for Cognitive Radio Systems Based on Prediction Considering Cross-Layer Optimization

The spectrum handoff problem for cognitive radio systems is considered in this paper. The secondary users (SUs) can only opportunistically access the spectrum holes, i.e. the frequency channels unoccupied by the primary users (PUs). As long as a PU appears, SUs have to vacate the channel to avoid interference to PUs and switch to another available channel. In this paper, a prediction-based spectrum handoff scheme is proposed to reduce the negative effect (both the interference to PUs and the service block of SUs) during the switching time. In the proposed scheme, a hidden Markov model is used to predict the occupancy of a frequency channel. By estimating the state of the model in the next time instant, we can predict whether the frequency channel will be occupied by PUs or not. As a cross-layer design, the spectrum sensing performance parameters false alarm probability and missing detection probability are taken into account to enhance accuracy of the channel occupancy prediction. The proposed scheme will react on the spectrum sensing algorithm parameters while the spectrum handoff performance is significantly affected by them. The interference to the PUs could be reduced obviously by adapting the proposed spectrum handoff scheme, associated with a potential increase of switch delay of SUs. It will also be helpful for SUs to save broadband scan time and prefer an appropriate objective channel so as to avoid service block. Numerical results demonstrate the above performance improvement by using this prediction-based scheme.

A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the or-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.

Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios

This paper introduces a simple and computationally efficient spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) based primary user signal using its autocorrelation coefficient. Further, it is shown that the log likelihood ratio test (LLRT) statistic is the maximum likelihood estimate of the autocorrelation coefficient in the low signal-to-noise ratio (SNR) regime. Performance of the local detector is studied for the additive white Gaussian noise (AWGN) and multipath channels using theoretical analysis. Obtained results are verified in simulation. The performance of the local detector in the face of shadowing is studied by simulations. A sequential detection (SD) scheme where many secondary users cooperate to detect the same primary user is proposed. User cooperation provides diversity gains as well as facilitates using simpler local detectors. The sequential detection reduces the delay and the amount of data needed in identification of the underutilized spectrum. The decision statistics from individual detectors are combined at the fusion center (FC). The statistical properties of the decision statistics are established. The performance of the scheme is studied through theory and validated by simulations. A comparison of the SD scheme with the Neyman-Pearson fixed sample size (FSS) test for the same false alarm and missed detection probabilities is also carried out.

What is a Spectrum Hole and What Does it Take to Recognize One?

ldquoSpectrum holesrdquo represent the potential opportunities for noninterfering (safe) use of spectrum and can be considered as multidimensional regions within frequency, time, and space. The main challenge for secondary radio systems is to be able to robustly sense when they are within such a spectrum hole. To allow a unified discussion of the core issues in spectrum sensing, the ldquoweighted probability of area recoveredrdquo (WPAR) metric is introduced to measure the performance of a sensing strategy; and the ldquofear of harmful interferencerdquo <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HI</sub> metric is introduced to measure its safety. These metrics explicitly consider the impact of asymmetric uncertainties (and misaligned incentives) in the system model. Furthermore, they allow a meaningful comparison of diverse approaches to spectrum sensing unlike the traditional triad of sensitivity, probability of false-alarm <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</sub> , and probability of missed-detection <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MD</sub> . These new metrics are used to show that fading uncertainty forces the WPAR performance of single-radio sensing algorithms to be very low for small values of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HI</sub> , even for ideal detectors. Cooperative sensing algorithms enable a much higher WPAR, but only if users are guaranteed to experience independent fading. Lastly, in-the-field calibration for wide-band (but uncertain) environment variables (e.g., interference and shadowing) can robustly guarantee safety (low <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HI</sub> ) even in the face of potentially correlated users without sacrificing WPAR.

Training symbol based coarse timing synchronization in OFDM systems

In orthogonal frequency division multiplexing (OFDM) systems, coarse frame timing can be acquired from one or more training symbols preceding every OFDM burst. The existing literature studied the case where there was only one training symbol consisting of identical segments. We generalize the timing synchronization methods to take advantage of multiple training symbols and only require the segments to be highly correlated but not necessarily identical. We construct a series of component timing metrics, one for each pair of the highly correlated segments, and combine them linearly to minimize the false alarm probability while keeping the asymptotic missed detection probability to the same level as other techniques. The OFDM data symbols in a downlink burst can have different power levels to reach the users at different distances. We take that into account and yield more realistic results than those in existing literature which only considered the equal power case. The performance of the proposed method is analyzed in three scenarios generalized from the IEEE 802.11 and IEEE 802.16 standards. Numerical results are presented to confirm the robustness of the proposed method in various channel conditions.

Performance Evaluation of a Spectrum-Sensing Technique for Cognitive Radio Applications in B-VHF Communication Systems

This paper deals with a cognitive radio approach based on a noncooperative spectrum sensing technique for applications in a very-high-frequency (VHF) aeronautical communications system to detect idle VHF channels and allocate them to an additional overlay system or secondary users. In particular, the focus here is on a spectrum sensing technique belonging to the class of energy detectors based on the Welch windowed periodogram. The performance of the proposed detector is presented in terms of the missed detection probability of the primary user under different application conditions. The obtained results and resulting implementation complexity highlight better behavior of the proposed detector with respect to alternative schemes previously proposed in the literature on this subject.

Non-ambiguity of blind watermarking: a revisit with analytical resolution

Resistance to ambiguity attack is an important requirement for a secure digital rights management (DRM) system. In this paper, we revisit the non-ambiguity of a blind watermarking based on the computational indistinguishability between pseudo random sequence generator (PRSG) sequence ensemble and truly random sequence ensemble. Ambiguity attacker on a watermarking scheme, which uses a PRSG sequence as watermark, is viewed as an attacker who tries to attack a noisy PRSG sequence. We propose and prove the security theorem for binary noisy PRSG sequence and security theorem for general noisy PRSG sequence. It is shown that with the proper choice of the detection threshold Th = a√n (a is a normalized detection threshold; n is the length of a PRSG sequence) and n ⩾ 1.39 × m/a2 (m is the key length), the success probability of an ambiguity attack and the missed detection probability can both be made negligibly small thus non-ambiguity and robustness can be achieved simultaneously for both practical quantization-based and blind spread spectrum (SS) watermarking schemes. These analytical resolutions may be used in designing practical non-invertible watermarking schemes and measuring the non-ambiguity of the schemes.

Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks

The decision making in fault diagnosis methods generally relies on the analysis of fault signature vectors. The current paper presents a new approach of decision making for the signature vectors for various identical or similar faults. The main contribution of the paper consists in the fusion between the reliability and the evaluation of the residuals in order to increase the fault isolation efficiency. The decision making, formalized as a Bayesian network, is established with a priori knowledge on fault signatures, false alarm and missing detection probability, online component state estimation computed by a Bayesian fusion of the component reliability, and measurements. The effectiveness and performances of the method are illustrated on a heating water process corrupted by various faults.