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This paper introduces the use of photon-added squeezed states (PASSs) for quantum pulse position modulation (QPPM). It is shown that the use of PASSs in QPPM communication systems can reduce the symbol error probability (SEP) with respect to the use of different classes of quantum states with the same energy, including squeezed states and photon-added coherent states. The impact of thermal noise and phase diffusion on the SEP is also evaluated. The findings of this paper show the utility of PASSs in quantum communication systems.

Quantum communication systems exploit the quantum features of light to enhance the reliability of a classically described communication system. This paper proposes the use of photon-added coherent states (PACSs), instead of coherent states, for quantum pulse position modulation (PPM) systems. PACSs are the most simple example of non-classical non-Gaussian states of light, as they can not be described by the classical laws of physics. We show that employing PACS in a PPM communication system improves the reliability of the communication with respect to using a coherent state with the same energy.

An important modulation technique for Internet of Things (IoT) is the one proposed by the LoRa allianceTM. In this paper we analyze the M-ary LoRa modulation in the time and frequency domains. First, we provide the signal description in the time domain, and show that LoRa is a memoryless continuous phase modulation. The cross-correlation between the transmitted waveforms is determined, proving that LoRa can be considered approximately an orthogonal modulation only for large M. Then, we investigate the spectral characteristics of the signal modulated by random data, obtaining a closed-form expression of the spectrum in terms of Fresnel functions. Quite surprisingly, we found that LoRa has both continuous and discrete spectra, with the discrete spectrum containing exactly a fraction 1/M of the total signal power., 8 pages, 5 figures, accepted for publication in IEEE Internet of Things Journal

This paper reviews in a tutorial way channel polarization exploited in successive cancellation decoding of polar codes, focusing on the binary erasure channel. Polarization phenomenon is tackled by decomposing a multi-input multi-output channel into virtual channels and by analyzing the mutual information evolution over each such channel. This evolution is shown to be closely related to the binary representation of the virtual channel index, which allows performing an exact mutual information analysis for each virtual channel via a transformation chart and to analyze eigenformations. A simple performance bound for polar codes, exploiting the above described analysis, is also reviewed.

Signal compression is essential for energy and bandwidth efficient communication and storage systems. In this paper, we provide two practical approaches for source compression of noisy sparse and non-strictly sparse (compressible) sources. The proposed schemes are based on channel coding theory to construct a source encoder that decreases the number of transmitted bits while preserving the fidelity of the reconstructed signal at the receiver by exploiting its sparsity. In addition, a model order selection scheme is proposed to detect the nonzero elements of sparse vectors embedded in noise, or to find a nonlinear sparse approximation of compressible signals. As illustrated by numerical results, our approach provides a lower distortion-rate function compared to previously known methods. For example, the proposed schemes achieve a lower distortion, about 2 orders of magnitude, compared to compressed sensing, for the same rate.

In this paper, the whole signal processing chain for an ultra-wideband (UWB) radar sensor network (RSN) is presented, starting from real measurements collected by sensor devices, and ending with the estimation of the target trajectory. The RSN is composed by one transmitter and six receivers, and monitors an indoor area of about 70 m2 performing tracking of a human target. The proposed processing chain consists of: clutter removal, a novel detection scheme, one-dimensional clustering, trilateration, two-dimensional clustering, particle-based probability hypothesis density filter tracking, and data association. Numerical results show the remarkable performance of the system, resulting in a root mean square localization error of 36 cm, a value smaller than the target size. The presented experimental study show that it is possible to accurately track human targets using a UWB RSN in a densely cluttered environment.

A common approach for estimating the number of wireless sources is to adopt model order selection based on information theoretic criteria (ITC). In this paper we study the generalized information criterion (GIC) and propose a design method for setting the penalty in practical situations, where the sample size is finite. The design is based on the maximum probability of correct model selection, that can be approximated using the statistic of the ratio between the largest eigenvalue and the trace of a white central Wishart matrix. For this metric we provide the exact distribution and a new approximation.

In this paper, a particle filtering algorithm is proposed for the challenging problem of multiple target tracking in ultra-wideband (UWB) radar sensor networks. The particle weights are derived analytically for the case of one transmitter and several receivers. The performance of the proposed particle algorithm is then evaluated through numerical results with comparison to the well-known Kalman filter. The scenarios account for the spatial configuration of the receivers, propagation effects, presence of clutter and noise.

Cognitive radio is regarded as the most promising technology for driving the future development of next-generation wireless networks, increasing spectrum efficiency by dynamic spectrum access. Allowing white spaces to be used in a deregulated manner exposes the radio spectrum to anomalous usage, which in turn may intentionally or unintentionally cause interference to primary users (PUs) as well as secondary users (SUs). In this paper we propose a subspace-based spectrum guarding detector (SSGD) to address the security of unauthorized spectrum usage. In particular, we consider the scenario where ongoing authorized transmissions by PUs have to be preserved from concurrent unauthorized emissions in the same frequency bands. The detector operates in two phases: a training phase where SSGD collects samples of the authorized transmissions, and a detection (on-line) phase where the spectrum is guarded against anomalous emissions. The performance of SSGD is analyzed in terms of detection and false alarm probabilities highlighting the effects of the propagation scenario, the PU characteristics, the signal-to-noise ratio (SNR) and the duration of both training and on-line phases. Numerical results show that in practical scenarios SSGD provides a remarkable SNR improvement of several dB over the conventional energy detector (ED).

In this paper we propose a wireless sensor network (WSN) designed for landslides monitoring and risk management. The WSN is self-organizing, has fault tolerance capabilities, and its behavior is driven by the events to be monitored, to guarantee fast deployment, robustness in harsh environments, and very long lifetime. Data collected by sensors are delivered through the network to a remote unit (RU) for on-line analysis and alerting. The WSN has been installed on a landslide located in Torgiovannetto (Italy) for an experimental campaign of several months where performance metrics, such as path statistics and battery levels, have been collected. These metrics demonstrate the effectiveness of the network protocols to manage self-organization, node failures, low link quality and unexpected battery depletion. With negligible human intervention during the pilot experiment the WSN revealed a very high level of robustness, which makes it suitable to monitor landslides in critical scenarios.