170 research outputs found

    Analysis of energy detection of unknown signals under Beckmann fading channels

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    (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The Beckmann fading is a general multipath fading model which includes Rice, Hoyt and Rayleigh fading as particular cases. However, the generality of the Beckmann fading also implies a significant increased mathematical complexity. Thus, relatively few analytical results have been reported for this otherwise useful fading model. The performance of energy detection for multi-branch receivers operating under Beckmann fading is here studied, and the inherent analytical complexity is here circumvented by the derivation of a closed-form expression for the generalized moment generating function (MGF) of the received signal-to-noise ratio (SNR), which is a new and useful result, as it is key for evaluating the receiver operating characteristics. The impact of fading severity on the probability of missed detection is shown to be less critical as the SNR decreases. Monte Carlo simulations have been carried out in order to validate the obtained theoretical expressions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto MINECO-FEDER TEC2013-42711-R y TEC2013-44442-P. Junta de Andalucía P11-TIC-7109

    Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks

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    Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffc alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identifcation, mitigation, and localization techniques have been proposed. This research investigates NLOS identifcation for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identifcation. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identifcation is investigated. Considering narrowband multiple antenna wireless systems, two xvNLOS identifcation techniques are proposed. Here, the implementation of spatial correlation of channel coeffcients across antenna elements as a metric for NLOS identifcation is proposed. In order to obtain the spatial correlation, a new multi-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identifcation technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which use MIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identifcation in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identi?cation measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identifcation performance compared to those that only use space, time or frequency

    IRS-Assisted Active Device Detection

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    This paper studies intelligent reflecting surface (IRS) assisted active device detection. Since the locations of the devices are a priori unknown, optimal IRS beam alignment is not possible and a worst-case design for a given coverage area is developed. To this end, we propose a generalized likelihood ratio test (GLRT) detection scheme and an IRS phase-shift design that minimizes the worst-case probability of misdetection. In addition to the proposed optimization-based phase-shift design, we consider two alternative suboptimal designs based on closed-form expressions for the IRS phase shifts. Our performance analysis establishes the superiority of the optimization-based design, especially for large coverage areas. Furthermore, we investigate the impact of scatterers on the proposed line-of-sight based design using simulations

    Optimum Cooperative Spectrum Sensing Technique for Multiuser Ultraviolet Wireless Communications

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    In this paper, we present a novel optimum cooperative spectrum sensing technique to mitigate multiuser interference for multiuser ultraviolet wireless communications over Málaga distributed turbulence channel. We consider the distributed decision fusion for the cooperative sensing. Based on the derived Málaga distribution, a mathematically-tractable expression for the average probability of detection is presented. An optimal voting rule is derived to minimize the average error rate. To verify this optimum voting rule, we use the energy detection technique. It is found that the formulated voting rule produces one optimal value only, which indeed confirms its optimum performance

    Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms

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