arXiv subjects

Stefano Tomasin

Publications and source records attributed to Stefano Tomasin.

At least 55 records · Page 3Linked to original sources

arXiv2023

Secret-Key-Agreement Advantage Distillation With Quantization Correction

We propose a novel advantage distillation strategy for physical layer-based secret-key-agreement (SKA). We consider a scenario where Alice and Bob aim at extracting a common bit sequence, which should remain secret to Eve, by quantizing a random number obtained from measurements at their communication channel. We propose an asymmetric advantage distillation protocol with two novel features: i) Alice quantizes her measurement and sends partial information on it over an authenticated public side channel, and ii) Bob quantizes his measurement by exploiting the partial information. The partial information on the position of the measurement in the quantization interval and its sharing allows Bob to obtain a quantized value closer to that of Alice. Both strategies increase the lower bound of the secret key rate.

cs.CR↗

arXiv2023

On the Limits of Cross-Authentication Checks for GNSS Signals

Global navigation satellite systems (GNSSs) are implementing security mechanisms: examples are Galileo open service navigation message authentication (OS-NMA) and GPS chips-message robust authentication (CHIMERA). Each of these mechanisms operates in a single band. However, nowadays, even commercial GNSS receivers typically compute the position, velocity, and time (PVT) solution using multiple constellations and signals from multiple bands at once, significantly improving both accuracy and availability. Hence, cross-authentication checks have been proposed, based on the PVT obtained from the mixture of authenticated and non-authenticated signals. In this paper, first, we formalize the models for the cross-authentication checks. Next, we describe, for each check, a spoofing attack to generate a fake signal leading the victim to a target PVT without notice. We analytically relate the degrees of the freedom of the attacker in manipulating the victim's solution to both the employed security checks and the number of open signals that can be tampered with by the attacker. We test the performance of the considered attack strategies on an experimental dataset. Lastly, we show the limits of the PVT-based GNSS cross-authentication checks, where both authenticated and non-authenticated signals are used.

cs.CR↗

arXiv2023

On the Optimal Spoofing Attack and Countermeasure in Satellite Navigation Systems

The threat of signal spoofing attacks against GNSS has grown in recent years and has motivated the study of anti-spoofing techniques. However, defense methods have been designed only against specific attacks. This paper introduces a general model of the spoofing attack framework in GNSS, from which optimal attack and defense strategies are derived. We consider a scenario with a legitimate receiver (Bob) testing if the received signals come from multiple legitimate space vehicles (Alice) or from an attack device (Eve). We first derive the optimal attack strategy against a Gaussian transmission from Alice, by minimizing an outer bound on the achievable error probability region of the spoofing detection test. Then, framing the spoofing and its detection as an adversarial game, we show that the Gaussian transmission and the corresponding optimal attack constitute a Nash equilibrium. Lastly, we consider the case of practical modulation schemes for Alice and derive the generalized likelihood ratio test. Numerical results validate the analytical derivations and show that the bound on the achievable error region is representative of the actual performance.

eess.SP↗

arXiv2023

Downlink TDMA Scheduling for IRS-aided Communications with Block-Static Constraints

Intelligent reflecting surfaces (IRSs) are being studied as possible low-cost energy-efficient alternatives to active relays, with the goal of solving the coverage issues of millimeter wave (mmWave) and terahertz (THz) network deployments. In the literature, these surfaces are often studied by idealizing their characteristics. Notably, it is often assumed that IRSs can tune with arbitrary frequency the phase-shifts induced by their elements, thanks to a wire-like control channel to the next generation node base (gNB). Instead, in this work we investigate an IRS-aided time division multiple access (TDMA) cellular network, where the reconfiguration of the IRS may entail an energy or communication cost, and we aim at limiting the number of reconfigurations over time. We develop a clustering-based heuristic scheduling, which optimizes the system sum-rate subject to a given number of reconfigurations within the TDMA frame. To such end, we first cluster user equipments (UEs) with a similar optimal IRS configuration. Then, we compute an overall IRS cluster configuration, which can be thus kept constant while scheduling the whole UEs cluster. Numerical results show that our approach is effective in supporting IRSs-aided systems with practical constraints, achieving up to 85% of the throughput obtained by an ideal deployment, while providing a 50% reduction in the number of IRS reconfigurations.

eess.SP↗

arXiv2022

Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information

We propose a technique to authenticate received packets in underwater acoustic networks based on the physical layer features of the underwater acoustic channel (UWAC). Several sensors a) locally estimate features (e.g., the number of taps or the delay spread) of the UWAC over which the packet is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system is to make a binary decision as to whether a packet is authentic or not, we show the importance of having a rich set of compressed features, while still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.

eess.SP↗

arXiv2022

Unmanned Aerial Vehicles Meet Reflective Intelligent Surfaces to Improve Coverage and Secrecy

The high configurability and low cost of Reflective Intelligent Surfaces (RISs) made them a promising solution for enhancing the capabilities of Beyond Fifth-Generation (B5G) networks. Recent works proposed to mount RISs on Unmanned Aerial Vehicles (UAVs), combining the high network configurability provided by RIS with the mobility brought by UAVs. However, the RIS represents an additional weight that impacts the battery lifetime of the UAV. Furthermore, the practicality of the resulting link in terms of communication channel quality and security have not been assessed in detail. In this paper, we highlight all the essential features that need to be considered for the practical deployment of RIS-enabled UAVs. We are the first to show how the RIS size and its power consumption impact the UAV flight time. We then assess how the RIS size, carrier frequency, and UAV flying altitude affects the path loss. Lastly, we propose a novel particle swarm-based approach to maximize coverage and improve the confidentiality of transmissions in a cellular scenario with the support of RISs carried by UAVs.

cs.CR↗

arXiv2022

End-to-End Simulation of 5G Networks Assisted by IRS and AF Relays

The high propagation and penetration loss experienced at millimeter wave (mmWave) frequencies requires ultra-dense deployments of 5th generation (5G) base stations, which may be infeasible and costly for network operators. Integrated Access and Backhaul (IAB) has been proposed to partially address this issue, even though raising concerns in terms of power consumption and scalability. Recently, the research community has been investigating Intelligent Reflective Surfaces (IRSs) and Amplify-and-Forward (AF) relays as more energy-efficient alternatives to solve coverage issues in 5G scenarios. Along these lines, this paper relies on a new simulation framework, based on ns-3, to simulate IRS/AF systems with a full-stack, end-to-end perspective, with considerations on to the impact of the channel model and the protocol stack of 5G NR networks. Our goal is to demonstrate whether these technologies can be used to relay 5G traffic requests and, if so, how to dimension IRS/AF nodes as a function of the number of end users.

cs.NI↗

arXiv2021

Virtual Private Mobile Network with Multiple Gateways for B5G Location Privacy

In a beyond-5G (B5G) scenario, we consider a virtual private mobile network (VPMN), i.e., a set of user equipments (UEs) directly communicating in a device-to-device (D2D) fashion, and connected to the cellular network by multiple gateways. The purpose of the VPMN is to hide the position of the VPMN UEs to the mobile network operator (MNO). We investigate the design and performance of packet routing inside the VPMN. First, we note that the routing that maximizes the rate between the VPMN and the cellular network leads to an unbalanced use of the gateways by each UE. In turn, this reveals information on the location of the VPMN UEs. Therefore, we derive a routing algorithm that maximizes the VPMN rate, while imposing for each UE the same data rate at each gateway, thus hiding the location of the UE. We compare the performance of the resulting solution, assessing the location privacy achieved by the VPMN, and considering both the case of single hop and multihop in the transmissions from the UEs to the gateways.

cs.CR↗

arXiv2021

Maximum-Rate Optimization of Hybrid Intelligent Reflective Surface and Relay Systems

We consider a wireless communication system, where a transmitting source is assisted by both a reconfigurable intelligent reflecting surface (IRS) and a decode-and-forward half-duplex relay (hybrid IRS-relay scheme) to communicate with a destination receiver. All devices are equipped with multiple antennas, and transmissions occur in two stages. In stage 1, the source splits the transmit message into two sub-messages, transmitted to the destination and the relay, respectively, using block diagonalization to avoid interference. Both transmissions will benefit from the IRS. In stage 2, the relay re-encodes the received sub-message and forwards it (still through the IRS) to the destination. We optimize power allocations, beamformers, and configurations of the IRS in both stages, in order to maximize the achievable rate at the destination. We compare the proposed hybrid approach with other schemes (with/without relay and IRS), and confirm that high data rate is achieved for the hybrid scheme in case of optimal IRS configurations.

eess.SP↗

arXiv2021

Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning

We propose a coordinated random access scheme for industrial internet-of-things (IIoT) scenarios, with machine-type devices (MTDs) generating sporadic correlated traffic. This occurs, e.g., when external events trigger data generation at multiple MTDs simultaneously. Time is divided into frames, each split into slots and each MTD randomly selects one slot for (re)transmission, with probability density functions (PDFs) specific of both the MTD and the number of the current retransmission. PDFs are locally optimized to minimize the probability of packet collision. The optimization problem is modeled as a repeated Markov game with incomplete information, and the linear reward-inaction algorithm is used at each MTD, which provably converges to a deterministic (suboptimal) slot assignment. We compare our solution with both the slotted ALOHA and the min-max pairwise correlation random access schemes, showing that our approach achieves a higher network throughput with moderate traffic intensity.

cs.NI↗

arXiv2021

Interference Prediction for Low-Complexity Link Adaptation in Beyond 5G Ultra-Reliable Low-Latency Communications

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complxity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.

cs.ET↗

arXiv2021

Game-Theoretic Mode Scheduling for Dynamic TDD in 5G Systems

Dynamic time-division duplexing (TDD) enables independent uplink/downlink mode scheduling at each cell, based on the local traffic. However, this creates cross-interference among cells. Thus, the joint power allocation and scheduling problem becomes mixed-integer non-convex and turns out to be NP-hard. We propose a low-complexity and decentralized solution, where power allocation and scheduling are decoupled. First, power is allocated in a decentralized fashion, and then modes are scheduled by a non-cooperative game to achieve the mixed-strategy Nash equilibrium. We consider two possible approaches to compute the payoffs in the game, according to the cross-interference power model and the entailed communication overhead among cells. Simulation results are presented for an outdoor dense small-cell scenario, showing that our approaches outperform static TDD significantly.

eess.SP↗

arXiv2020

Localization Attack by Precoder Feedback Overhearing in 5G Networks and Countermeasures

In fifth-generation (5G) cellular networks, users feed back to the base station the index of the precoder (from a codebook) to be used for downlink transmission. The precoder is strongly related to the user channel and in turn to the user position within the cell. We propose a method by which an external attacker determines the user position by passively overhearing this unencrypted layer-2 feedback signal. The attacker first builds a map of fed back precoder indices in the cell. Then, by overhearing the precoder index fed back by the victim user, the attacker finds its position on the map. We focus on the type-I single-panel codebook, which today is the only mandatory solution in the 3GPP standard. We analyze the attack and assess the obtained localization accuracy against various parameters. We analyze the localization error of a simplified precoder feedback model and describe its asymptotic localization precision. We also propose a mitigation against our attack, wherein the user randomly selects the precoder among those providing the highest rate. Simulations confirm that the attack can achieve a high localization accuracy, which is significantly reduced when the mitigation solution is adopted, at the cost of a negligible rate degradation.

cs.IT↗

arXiv2020

Cascaded WLAN-FWA Networking and Computing Architecture for Pervasive In-Home Healthcare

Pervasive healthcare is a promising assisted-living solution for chronic patients. However, current cutting-edge communication technologies are not able to strictly meet the requirements of these applications, especially in the case of life-threatening events. To bridge this gap, this paper proposes a new architecture to support indoor healthcare monitoring, with a focus on epileptic patients. Several novel elements are introduced. The first element is the cascading of a WLAN and a cellular network, where IEEE 802.11ax is used for the wireless local area network to collect physiological and environmental data in-home and 5G-enabled Fixed Wireless Access links transfer them to a remote hospital. The second element is the extension of the network slicing concept to the WLAN, and the introduction of two new slice types to support both regular monitoring and emergency handling. Moreover, the inclusion of local computing capabilities at the WLAN router, together with a mobile edge computing resource, represents a further architectural enhancement. Local computation is required to trigger not only health-related alarms, but also the network slicing change in case of emergency: in fact, proper radio resource scheduling is necessary for the cascaded networks to handle healthcare traffic together with other promiscuous everyday communication services. Numerical results demonstrate the effectiveness of the proposed approach while highlighting the performance gain achieved with respect to baseline solutions.

cs.NI↗

arXiv2019

Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks

The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulty in pre-designing authentication model, providing continuous protections, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous and situation-aware device validation under unknown network conditions and unpredictable dynamics.

cs.CR↗

arXiv2019

Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading.

eess.SP↗

arXiv2019

Performance Requirements of Advanced Healthcare Services over Future Cellular Systems

The fifth generation (5G) of communication systems has ambitious targets of data rate, end-to-end latency, and connection availability, while the deployment of a new flexible network architecture will spur new applications. E-health and mobile health (m-health) solutions will meet the increasing demand of new, sustainable, and more accessible services beneficial to both practitioners and the rapidly aging population. This paper aims at defining the technical requirements of future cellular networks to support a variety of advanced healthcare services (e.g., smart hospital, telesurgery, connected ambulances, and monitoring). While 5G will be able to satisfy these requirements, it will also pave the way for future e- and m-health in the sixth-generation (6G) cellular networks.

eess.SP↗

arXiv2019

Location-Verification and Network Planning via Machine Learning Approaches

In-region location verification (IRLV) in wireless networks is the problem of deciding if user equipment (UE) is transmitting from inside or outside a specific physical region (e.g., a safe room). The decision process exploits the features of the channel between the UE and a set of network access points (APs). We propose a solution based on machine learning (ML) implemented by a neural network (NN) trained with the channel features (in particular, noisy attenuation values) collected by the APs for various positions both inside and outside the specific region. The output is a decision on the UE position (inside or outside the region). By seeing IRLV as an hypothesis testing problem, we address the optimal positioning of the APs for minimizing either the area under the curve (AUC) of the receiver operating characteristic (ROC) or the cross entropy (CE) between the NN output and ground truth (available during the training). In order to solve the minimization problem we propose a twostage particle swarm optimization (PSO) algorithm. We show that for a long training and a NN with enough neurons the proposed solution achieves the performance of the Neyman-Pearson (N-P) lemma.

eess.SP↗