This work investigates a multibeam system for joint sensing and communication (JSC) based on multiple-input multiple-output (MIMO) 5G new radio (NR) waveforms. In particular, we consider a base station (BS) acting as a monostatic sensor that estimates the range, speed, and direction of arrival (DoA) of multiple targets via beam scanning using a fraction of the transmitted power. The target position is then obtained via range and DoA estimation. We derive the sensing performance in terms of probability of detection and root mean squared error (RMSE) of position and velocity estimation of a target under line-of-sight (LOS) conditions. Furthermore, we evaluate the system performance when multiple targets are present, using the optimal sub-pattern assignment (OSPA) metric. Finally, we provide an in-depth investigation of the dominant factors that affect performance, including the fraction of power reserved for sensing.

Precise localization and tracking of moving objects is of great interest for a variety of emerging applications including the Internet-of-Things (IoT). The localization and tracking tasks are challenging in harsh wireless environments, such as indoor ones, especially when objects are not equipped with dedicated tags (noncollaborative). The problem of detecting, localizing, and tracking noncollaborative objects within a limited area has often been undertaken by exploiting a network of radio sensors, scanning the zone of interest through wideband radio signals to create a radio image of the objects. This paper presents a sensor network for radio imaging (sensor radar) along with all of the signal processing steps necessary to achieve highaccuracy objects tracking in harsh propagation environments. The described sensor radar is based on the impulse radio (IR) ultrawideband (UWB) technology, entailing the transmission of very short duration pulses. Experimental results with actual UWB signals in indoor environments confirm the sensor radar’s potential in IoT applications.

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems, namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we introduce a novel linear minimum mean-squared-error (LMMSE) estimator. We also provide a rigorous theoretical characterization of side-lobe levels achieved by the proposed LMMSE estimator and two benchmark schemes, proving its superiority for any modulation scheme and SNR level. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate beams for sensing and communications not overlapping in time. Simulations demonstrate improved performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. Additionally, we present experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements

In this paper, we propose a wireless sensor network (WSN) designed for monitoring and risk management of landslides, where data collected by sensors are delivered through the network to a remote unit for online analysis and alerting. To ensure fast deployment, robustness in harsh environments, and very long lifetime, the sensor nodes and the communication protocol have been specifically conceived, so that the network is self-organizing, fault tolerant, and adaptive. The WSN has been installed on a landslide located in Torgiovannetto (Italy) for an experimental campaign of several months where performance metrics, such as radio link and path statistics as well as battery levels, have been collected. These metrics demonstrated 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.

This work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility.