Ambient backscatter is an intriguing wireless communication paradigm that allows small devices to compute and communicate by using only the power they harvest from far-field radio-frequency (RF) signals in the air. Ambient backscattering devices reflect RF signals emitted by existing or legacy communications systems, such as digital TV broadcasting, cellular, or Wi-Fi ones, which are designed for transporting information and are not intended for RF energy transfer. This paper deals with mathematical modeling and performance analysis of wireless broadband networks operating over fading channels with ambient backscatter devices. After introducing a detailed signal model of the relevant communication links, we study the influence of physical parameters on the capacity of both legacy and backscatter channels, by considering different receiver architectures. We analytically show that, under reasonable operative conditions, a legacy system-employing an orthogonal frequency-division multiplexing (OFDM) modulation scheme-can turn the RF interference arising from the backscatter process into a form of multipath diversity that can be exploited to increase its performance. Moreover, our analysis proves that a backscatter system-transmitting one symbol per OFDM symbol of the legacy system-can achieve satisfactory data rates over relatively short distances, especially when the intended recipient of the backscatter signal is co-located with the legacy transmitter, i.e., they are on the same device.

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user’s comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.

Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor networks (WSNs) for smart city applications in several ways. Among them, a UAV can be employed as a relay in a “store-carry and forward” fashion by uploading data from ground sensors and metering devices and, then, downloading it to a central unit. However, both the uploading and downloading phases can be prone to potential threats and attacks. As a legacy from traditional wireless networks, the jamming attack is still one of the major and serious threats to UAV-aided communications, especially when also the jammer is mobile, e.g., it is mounted on a UAV or inside a terrestrial vehicle. In this paper, we investigate anti-jamming communications for UAV-aided WSNs operating over doubly-selective channels in the downloading phase. In such a scenario, the signals transmitted by the UAV and the malicious mobile jammer undergo both time dispersion due to multipath propagation effects and frequency dispersion caused by their mobility. To suppress high-power jamming signals, we propose a blind physical-layer technique that jointly detects the UAV and jammer symbols through serial disturbance cancellation based on symbol-level post-sorting of the detector output. Amplitudes, phases, time delays, and Doppler shifts – required to implement the proposed detection strategy – are blindly estimated from data through the use of algorithms that exploit the almost-cyclostationarity properties of the received signal and the detailed structure of multicarrier modulation format. Simulation results corroborate the anti-jamming capabilities of the proposed method, for different mobility scenarios of the jammer.

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of powerconsuming active components or other signal conditioning units. In such a network, the performance of the backscatter system can be severely degraded by channel estimation errors and co-channel direct-link interference (DLI) from the primary system. To overcome these shortcomings, we consider a cloud radio access network (C-RAN) architecture, wherein both the primary and secondary edge nodes are connected to a cloud processor via high-speed links. In this centralized architecture, secondary edge nodes provide network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the problem of acquiring channel state information and suppressing the DLI is managed by the cloud processor. In particular, we assess the performance of the secondary backscatter sensor transmission in a realistic system setup, which takes into account training-based channel estimation, practical modulation constraints, and imperfect DLI suppression. Additionally, we formulate and solve an optimization problem aimed at maximizing the transmission rate of the secondary transmission, subject to limits on channel estimation error, average symbol error rate, power consumption, and energy storage capabilities of the backscatter sensor. The validity of our analysis and the performance of the secondary system based on the proposed designs are corroborated through Monte Carlo simulations.

In this paper, we consider a broadband cognitive radio wireless sensor network operating in a crowded spectrum scenario, where a sensor transmitter (i.e., a sensor node that needs to transmit information to another sensor node or to a sink) opportunistically uses the channel of a primary system adopting an underlay paradigm. Both the primary and sensor systems employ an orthogonal frequency-division multiplexing (OFDM) modulation scheme. The proposed approach is reminiscent of additive superposition coding: specifically, the sensor transmitter behaves as an amplify-and-forward relay for the primary system, by sending a linearly weighted combination of the received primary signal and its own block of symbols. Such a superposition is performed by the sensor transmitter on the fly, in a single OFDM symbol of the primary system. Moreover, the proposed scheme does not require spectrum sensing at the sensor nodes and does not demand any cooperation between primary and sensor systems, thereby accounting for the inherent resource constraints of sensor nodes. With reference to both primary and sensor communications, we address the problem of power allocation by considering different amounts of channel state information and develop a worst case analysis of the achievable information rates. The developed algorithms and the derived formulas, which are corroborated by numerical Monte Carlo simulations, allow one to recognize and discuss interesting tradeoffs between the main parameters of the cognitive radio sensor transmission.