In this paper, we study the problem of promptly detecting the presence of non-cooperative activity from one or more Reconfigurable Intelligent Surfaces (RISs) with unknown characteristics lying in the vicinity of a Multiple-Input Multiple-Output (MIMO) communication system using Orthogonal Frequency-Division Multiplexing (OFDM) transmissions. We first present a novel wideband channel model incorporating RISs as well as non-reconfigurable stationary surfaces, which captures both the effect of the RIS actuation time on the channel in the frequency domain as well as the difference between changing phase configurations during or among transmissions. Considering that RISs may operate under the coordination of a third-party system, and thus, may negatively impact the communication of the intended MIMO OFDM system, we present a novel RIS activity detection framework that is unaware of the distribution of the phase configuration of any of the non-cooperative RISs. In particular, capitalizing on the knowledge of the data distribution at the multi-antenna receiver, we design a novel online change point detection statistic that combines a deep support vector data description model with the scan$B$-test. The presented numerical investigations demonstrate the improved detection accuracy as well as decreased computational complexity of the proposed RIS detection approach over existing change point detection schemes.

In this paper, we present a spatially consistent A2G channel model based on probabilistic LOS/NLOS segmentation to parameterize the deterministic path loss and stochastic shadow fading model. Motivated by the limitations of existing Unmanned Aerial Vehicle (UAV) channel models that overlook spatial correlation, our approach reproduces LOS/NLOS transitions along ground user trajectories in urban environments. This model captures environment-specific obstructions by means of azimuth and elevation-dependent LOS probabilities without requiring a full detailed 3D representation of the surroundings. We validate our framework against a geometry-based simulator by evaluating it across various urban settings. The results demonstrate its accuracy and computational efficiency, enabling further realistic derivations of path loss and shadow fading models and thorough outage analysis.

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cramér Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations.

In this paper, we study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems to-wards the extension of the wireless cellular coverage in 3D, where multiple RISs, each equipped with an array of passive elements, are deployed to assist a base station (BS) to simultaneously serve multiple unmanned aerial vehicles (UAVs) in the same time-frequency resource of 5G wireless communications. With a focus on ensuring fairness among UAVs, our objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at UAVs by jointly optimizing the transmit beamforming parameters at the BS and phase shift parameters at RISs. We propose two novel algorithms to address this problem. The first algorithm aims to mitigate interference by calculating the BS beamforming matrix through matrix inverse operations once the phase shift parameters are determined. The second one is based on the principle that one RIS element only serves one UAV and the phase shift parameter of this RIS element is optimally designed to compensate the phase offset caused by the propagation and fading. To obtain the optimal parameters, we utilize one state-of-the-art reinforcement learning algorithm, deep deterministic policy gradient, to solve these two optimization problems. Simulation results are provided to illustrate the effectiveness of our proposed solution and some insightful remarks are observed.

Rural areas broadband access suffers from the limited investment of network operators, due to the forecasted return on investment. As a consequence, digital services such as eHealth, remote education, or smart agriculture cannot be offered to the rural population. In this context, research on Unmanned Aerial Vehicles (UAV) networks has emerged, which aims to solve the coverage problem by relying on small cells mounted on UAVs to provide coverage. From the network Quality of Service (QoS) point of view, i.e., the performance offered to users according to certain parameters such as delay, reliability, and throughput provisioning can be identified as one of the weak points. For the problem of maximizing throughput, the main solution is to group several UAVs in the same area. However, as the offered throughput increases, the power consumption will also increase. In this context, this paper proposes a genetic algorithm to solve the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications while minimizing the energy consumption of the swarm of UAVs. The algorithm is defined and evaluated in realistic scenarios, demonstrating its effectiveness on increasing the throughput while decreasing the number of UAVs that are required.

In the past decade, the number of amateur drones is increasing, and this trend is expected to continue in the future. The security issues brought by abuse and misconduct of drones become more and more severe and may incur a negative impact to the society. In this paper, we leverage existing cellular multiple-input multiple-output (MIMO) base station (BS) infrastructure, operating at millimeter wave (mmWave) frequency bands, for drone detection in a device-free manner with the aid of one reconfigurable intelligent surface (RIS), deployed in the proximity of the BS. We theoretically examine the feasibility of drone detection with the aid of the generalized likelihood ratio test (GLRT) and validate via simulations that, the optimized deployment of an RIS can bring added benefits compared to RIS-free systems. In addition, the effect of RIS training beams, training overhead, and radar cross section, is investigated in order to offer theoretical design guidance for the proposed cellular RIS-based passive drone detection system.

In recent years, mobile edge computing (MEC) has been proposed as a promising technique to alleviate the challenges faced by delay and computation-intensive applications. However, users in remote and mountainous areas continue to face difficulties obtaining reliable computation services. To overcome this obstacle, unmanned aerial vehicles (UAVs) equipped with MEC servers have emerged as a popular solution. In such a multi-UAV network, the coverage areas of the UAVs might overlap, which would result in resource wastage and interference. To address this issue, we investigate a collaborative UAV-assisted MEC system for both aerial users (AUs) and ground users (GUs) in this work. Specifically, each user is covered by multiple UAV servers, and the resources of UAVs are dynamic over time. The main objective of this work is to reduce the average delay and improve the service success rate by jointly designing the UAV server-user association, bandwidth, and computing resource allocation strategy. To address the non-convex optimization problem mentioned above, we formulate a multi-agent extension of Markov decision processes (MDPs) for the system and design a cooperative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach for each UAV server to make decisions using a centralized training approach with distributed execution. Simulation results validate that the proposed approach can achieve a superior success service rate with a lower delay compared with baselines.

Hybrid Reconfigurable Intelligent Surfaces (HRISs), which are capable of simultaneous programmable reflections and sensing, are expected to play a significant role in future wireless networks, enabling various Integrated Sensing and Communication (ISAC) applications. In this paper, we focus on HRIS-enabled Unmanned Aerial Vehicle (UAV) networks and design the HRIS parameters (phase profile, reception combining, and the power splitting between the two functionalities) for jointly estimating the individual UAV-HRIS and HRIS-base-station channels as well as the Angle of Arrival (AoA) of the Line-of-Sight (LoS) component of the UAV-HRIS channel. We derive the Cramér Rao lower bounds for the estimated channels and evaluate the performance of the proposed approach in terms of the channel estimation error and the LoS AoA estimation accuracy, verifying its effectiveness for HRIS-enabled ground-to-UAV wireless communication systems.

In this paper, the programmable signal propagation paradigm, enabled by Reconfigurable Intelligent Surfaces (RISs), is exploited for high accuracy 3-Dimensional (3D) user localization with a single multi-antenna base station. Capitalizing on the tunable reflection capability of passive RISs, we present a two-stage user localization method leveraging the multi-reflection wireless environment. In the first stage, we deploy an off-grid Compressive Sensing (CS) approach, which is based on the atomic norm minimization, for estimating the angles of arrival associated with each RIS, which is followed, in the second stage, by a maximum likelihood location estimation initialized with a least-squares line intersection technique. The presented numerical results showcase the high accuracy of the proposed 3D localization method, verifying our theoretical Cramér Rao lower bound analysis.

Abstract Recent research and development interests deal with metasurfaces for wireless systems beyond their consideration as intelligent tunable reflectors. Among the latest proposals is the simultaneously transmitting (a.k.a. refracting) and reflecting reconfigurable intelligent surface (STAR‐RIS) which intends to enable bidirectional indoor‐to‐outdoor, and vice versa communications thanks to its additional refraction capability. This double functionality provides increased flexibility in concurrently satisfying the quality‐of‐service requirements of users located at both sides of the metasurfaces, for example, the achievable data rate and localisation accuracy. The authors focus on STAR‐RIS‐empowered simultaneous indoor and outdoor three‐dimensional (3D) localisation, and study the fundamental performance limits via Fisher information analyses and Cramér Rao lower bounds (CRLBs). The authors also devise an efficient localisation algorithm based on an off‐grid compressive sensing (CS) technique relying on atomic norm minimisation (ANM). The impact of the training overhead, the power splitting at the STAR‐RIS, the power allocation between the users, the STAR‐RIS size, the imperfections of the STAR‐RIS‐to‐BS channel, as well as the role of the multi‐path components on the positioning performance are assessed via extensive computer simulations. It is theoretically demonstrated that high‐accuracy, up to centimetre level, 3D localisation can be simultaneously achieved for indoor and outdoor users, which is also accomplished via the proposed ANM‐based estimation algorithm.

HiPR+ is an approach for centimeter-accurate indoor localization. It combines distance estimation between ultra-wideband (UWB) transceivers and location estimation using an extended Kalman filter (EKF). The performance is tested with experiments on hardware platforms from Decawave. The distance estimation of HiPR+ achieves an order of magnitude better precision and a multiple improvement in accuracy compared to the company's native solution while it only takes only a fraction the time needed for range computation. We evaluate the 3D localization capabilities with two least-squares approaches and an EKF. A median accuracy below one centimeter can be attained using the proposed ranging error compensations in combination with the EKF-based~positioning.

Simultaneously transmitting (refracting) and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been recently identified to improve the spectrum/energy efficiency and extend the communication range. However, their potential for enhanced concurrent indoor and outdoor localization has not yet been explored. In this paper, we study the fundamental limits, i.e., the Cramér Rao lower bounds (CRLBs) via Fisher information analyses, on the three-dimensional (3D) localization performance with a STAR-RIS at millimeter wave frequencies. The effect of the power splitting between refraction and reflection at the STARRIS as well as the power allocation between the two mobile stations (MSs) are investigated. By maximizing the principal angle between the two subspaces corresponding to the STAR-RIS reflection and refraction matrices, we are able to find the optimal solutions for these simultaneous operations. We verify that high-accuracy 3D localization can be achieved for both indoor and outdoor MSs when the system parameters are well optimized.

Teleoperation of an unmanned aerial vehicle (UAV) is a challenging mobile application with real-time control from a first-person view. It poses stringent latency requirements for both video and control traffic. This paper studies the video quality and latencies for UAV teleoperation over LTE using ns3 simulations. A key ingredient is the latency budget model. We observe that the latency of the video is higher and more sensitive to mobility than that of the control traffic. The latency is influenced by the traffic variation caused by the variable bit rate of the streaming application. High mobility tends to increase latency and lead to more outliers, being problematic in real-time control.

Existing mobile devices such as smartphones rely on a multi-radio access technology (RAT) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is expected to provide the highest accuracy. In contrast, we propose to fuse timing range measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies. We take a first step in this direction and propose to fuse timing measurements of satellite navigation systems and WiFi networks. We introduce different novel methods such as a data fuser, an estimator of WiFi ToF distance and a geometrical-statistical approach to best fuse the set of ranges in presence of a rich set of measurements. Experimental results show that our solution allows the mobile device to efficiently position itself in diverse challenging scenarios.

Crowdsourced signal monitoring systems are gaining attention for capturing the wireless spectrum at large geographical scale. Yet, most of the current systems are still limited to simple power spectrum measurements reported by each sensor. Our objective is to enhance such systems with signal decoding capabilities performed in the backend while retaining the original vision of a low-cost and crowdsourced setup. We propose a distributed system architecture for collaborative radio signal monitoring and decoding that builds on $12 low-cost radio frequency (RF) frontends and embedded boards and that takes into consideration the limited network bandwidth from the sensors to the backend. We present a distributed time multiplexing mechanism to sample the spectrum in a coordinated fashion that exploits the similarity of the radio signal received by more than one RF frontend in the same radio coverage. We address the strict time synchronization required among sensors to reconstruct the signal from the samples they receive when in the same radio coverage. We study and implement techniques to identify and overcome errors in the timing information in the presence of noise sources and decode the data in the backend. We provide an evaluation based on simulations and on real signals transmitted by Long-Term Evolution (LTE) base stations. Our results show that we can reliably reconstruct and decode radio signals received by low-cost crowdsourced sensors.

Time-of-flight (ToF) echo techniques have been recently suggested for ranging mobile devices over WiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes our method particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median 2-D positioning error between 2 and 3.4 m.

Self-localization systems based on the Time-of-Flight (ToF) of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. In this work, we study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different self-positioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings.

Time-of-Flight (ToF) echo techniques have been proposed as a way to estimate the range between regular Wi-Fi stations. Recent works either did not address practical questions for deployability, or made evaluations in basic setups, or used advanced 802.11 hardware designs. We build an approach solely deployed using ToF measurements and relying on software access point (AP) upgrades of simple commercial off-the-shelf 802.11 chipsets. Our solution filters noisy measurements collected by WiFi chipsets of six dollars each, it has been tested across different and heterogeneous setups and testbeds, and has the potential to enable ToF ranging in every Wi-Fi chipsets.