1,721,122 research outputs found
On Oversampling-Based Signal Detection
No full textThe availability of inexpensive devices allows nowadays to implement cognitive radio functionalities in large-scale networks such as the internet-of-things and future mobile cellular systems. In this paper, we focus on wideband spectrum sensing in the presence of oversampling, i.e., the sampling frequency of a digital receiver is larger than the signal bandwidth, where signal detection must take into account the front-end impairments of low-cost devices. Based on the noise model of a software-defined radio dongle, we address the problem of robust signal detection in the presence of noise power uncertainty and non-flat noise power spectral density (PSD). In particular, we analyze the receiver operating characteristic of several detectors in the presence of such front-end impairments, to assess the performance attainable in a real-world scenario. We propose new frequency-domain detectors, some of which are proven to outperform previously proposed spectrum sensing techniques such as, e.g., eigenvalue-based tests. The study shows that the best performance is provided by a noise-uncertainty immune energy detector (ED) and, for the colored noise case, by tests that match the PSD of the receiver noise
Secure Key Throughput of Intermittent Trusted-Relay QKD Protocols
No full textQuantum key distribution (QKD) protocols are designed to distribute secure keys between two remote parties. Current QKD systems require the presence of a trusted relay to distribute the keys over intercontinental distances, due to technological limitations. When the trusted relay is intermittently available to the end parties, as in the case of low Earth orbit (LEO) satellites, the QKD system undergoes a severe reduction in the amount of exchangeable secret bits. This paper proposes a new way to analyze the performance of QKD systems under these premises. It is shown that the secret key rate is not the most important figure of merit in the design of QKD protocols with intermittent relays, despite its importance for standard QKD links
Syndrome-Based Encoding of Compressible Sources for M2M Communication
Full text linkData originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre- quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli- uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable
Anomaly Detection Using WiFi Signals of Opportunity
No full textDetection of changes in indoor areas and controlled environments is getting increasing interest in ambient intelligence and security. In this paper, we propose a radio-frequency (RF)- based anomaly detector that, observing the spectrum received from signals of opportunity (SoOp) and exploiting machine learning (ML) techniques, is capable of revealing changes in an indoor environment. Based on real waveforms emitted by a WiFi access point (AP) and collected by a RF sensor, we demonstrate that anomaly detection, e.g., represented by the presence of a person in the monitored area, is possible. The proposed methodology, tested in a typical office environment when the AP- sensor link is in non-line-of-sight (NLOS), achieves an accuracy greater than 95 % just by collecting few beacon packets, i.e., in dozens of milliseconds. Moreover, results demonstrate that the proposed approach outperforms a well-known received signal strength (RSS)-based solution in terms of accuracy, even using just a single sensor
Emerging Distributed Programming Paradigm for Cyber-Physical Systems Over LoRaWANs
Full text linkThe growing interest around the cyber-physical systems (CPS), populated with open systems counting myriads of devices, is calling for new technologies both in telecommunications and software engineering with full integration among them. One of the most promising wireless communication technologies for the CPS is LoRaWAN, which enables long range transmission with low power consumption. Typical application scenarios include smart-homes, smart-cities, precision agriculture, and intelligent transportation. On the software side, novel paradigms are emerging to dominate the complexity introduced by the CPS with a large number of spatially distributed devices. Among them, aggregate computing is gaining traction, for it enables expressing the behavior of aggregates of devices by considering their ensemble as a single computational entity, allowing expressive space-time computations. In this paper, we introduce a software architecture which allows aggregate programming software to execute on a network of LoRa-communicating devices. We also provide an open source prototype implementing such architecture, which we use to study the current limitations of existing aggregate programming interpreters in resource-constrained scenarios. We conclude by drawing recommendations for developing such interpreters in order to pave the way to a more power- and data-efficient design
Wideband spectrum sensing for cognitive radio: a model order selection approach
No full textWideband spectrum sensing (SS) allows cognitive radios (CRs) to reach, by monitoring large portions of spectrum, a better awareness of the surrounding radio environment. In this paper, we formulate wideband SS as a model order selection problem. This approach consists in the adoption of information theoretic criteria (ITC) to identify the occupied frequency components in a frequency domain representation of the observed signal. We provide a general formulation of the problem and then focus on the case in which discrete Fourier transform (DFT) is used as spectral representation. Finally, we propose consistent ITC for which we provide analytical expressions for the maximum probability of detection
Rate-Adaptive Information Transmission over MIMO ChannelsMIMO Systems, Theory and Applications
Full text linkIn the context of wireless communication, a Multiple-Input Multiple-Output (MIMO) system is a system that employs multiple antennas both at the transmitter and receiver. The first theoretical analysis of MIMO systems were developed by Winters (1987), Foschini (1996) and Telatar (1999), and since then there have been many research efforts on this subject. What mainly makes MIMO systems interesting is their potential ability to achieve an increase in system capacity or in link reliability without requiring additional transmission power or bandwidth (Goldsmith, 2005).
In this work, we focus on the utilization of MIMO systems for the lossy transmission of source information. In particular, we want to compare several different strategies for the transmission of a zero mean Gaussian source over Rayleigh-fading MIMO channels, assuming rate-adaptive source encoding. The MIMO transmission strategies are based on techniques such as Repetition coding (REP), Time Sharing (TS), the Alamouti scheme (ALM) and Spatial Multiplexing (SM) (Alamouti, 1998; Tse & Viswanath, 2006)
Quantum Discrimination of Noisy Photon-Added Coherent States
Full text linkQuantum state discrimination (QSD) is a key enabler in quantum sensing and networking, for which we envision the utility of non-coherent quantum states such as photon-added coherent states (PACSs). This paper addresses the problem of discriminating between two noisy PACSs. First, we provide representation of PACSs affected by thermal noise during state preparation in terms of Fock basis and quasi-probability distributions. Then, we demonstrate that the use of PACSs instead of coherent states can significantly reduce the error probability in QSD. Finally, we quantify the effects of phase diffusion and pho- ton loss on QSD performance. The findings of this paper reveal the utility of PACSs in several applications involving QSD
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