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Analysis of Duplexing Patterns in Multi-Hop mmWave Integrated Access and Backhaul Systems
(IEEE, 2024-08-23) Tafintsev, Nikita; Moltchanov, Dmitri; Mao, Wei; Nikopour, Hosein; Yeh, Shu-Ping; Talwar, Shilpa; Valkama, Mikko; Andreev, Sergey
Integrated Access and Backhaul (IAB) technology promises to facilitate cost-effective deployments of 5G New Radio (NR) systems operating in both sub-6 GHz and millimeter-wave (mmWave) bands. As full-duplex wireless systems are in their infancy, initial deployments of IAB networks may need to rely on half-duplex operation to coordinate transmissions between access and backhaul links. However, the use of half-duplex operation not only makes the scheduling of links in the IAB networks interdependent, but also the number of their feasible combinations grows exponentially with the network size, thereby posing challenges to the optimal design of such systems. In this paper, by accounting for mmWave radio characteristics, we propose a joint resource allocation and link scheduling framework to enhance the user equipment (UE) throughput in multi-hop in-band IAB systems. We keep the problem in the form of linear programming type for the feasibility of the practical applications. We show that the increased number of uplink and downlink transmission time interval (TTI) configurations does not result in improved UE throughput as compared to two configurations. Further, we demonstrate that in-band IAB systems tend to be backhaul-limited, and the utilization of multi-beam functionality at the IAB-donor alleviates this limitation by doubling the average UE throughput. Finally, we show that the use of proportional-fair allocations allows the average UE throughput to be improved by around 10% as compared to the max-min allocations.
Power-Efficient Electronically Tunable Fractional-Order Filter
(MDPI, 2024-01-02) Tasneem, Sadaf; Kumar Ranjan, Rajeev; Paul, Sajal K.; Herencsár, Norbert
This article describes a low-voltage, low-power fractional-order low-pass filter (FO-LPF) of order 1 + alpha, which is implemented using a voltage differencing differential difference amplifier (VDDDA). The VDDDA structure is implemented using the bulk-driven metal oxide semiconductor transistor technique. The transistors operate in the subthreshold region to maintain low-supply voltage and low-power consumption. The FO-LPF structure implemented using this VDDDA structure is compact. It includes three VDDDAs and three grounded capacitors along with two active resistors implemented using MOS transistors. In addition, this filter structure provides electronic tuning of its order and cut-off frequency through the bias current of the active component used. The effects of tracking error and parasitics on the functionality of the proposed FO-LPF were investigated. The VDDDA and filter operate at +/- 300 mV and dissipate only 207 nW and 663 nW of power, respectively. Thus, the VDDDA structure and filter are suitable for low-voltage and low-power operation. Layouts of the proposed VDDDA as well as the FO-LPF were designed in the Cadence Virtuoso environment. Post-layout simulation results of the designed circuits imply that they are suitable for fabrication. Noise, total harmonic distortion, Monte-Carlo, and PVT analyses were also performed.
Identification of industrial devices based on payload
(Association for Computing Machinery, 2024-07-30) Pospíšil, Ondřej; Fujdiak, Radek
Identification of industrial devices based on their behavior in network communication is important from a cybersecurity perspective in two areas: attack prevention and digital forensics. In both areas, device identification falls under asset management or asset tracking. Due to the impact of active scanning on these networks, particularly in terms of latency, it is important to use passive scanning in industrial networks. For passive identification, statistical learning algorithms are nowadays the most appropriate. The aim of this paper is to demonstrate the potential for passive identification of PLC devices using statistical learning based on network communication, specifically the payload of the packet. Individual statistical parameters from 15 minutes of traffic based on payload entropy were used to create the features. Three scenarios were performed and the XGBoost algorithm was used for evaluation. In the best scenario, the model achieved an accuracy score of 83% to identify individual devices.
Secure and Privacy-Preserving Car-Sharing Systems
(ACM, 2024-07-30) Malina, Lukáš; Dzurenda, Petr; Lövinger, Norbert; Ekeh, Ijeoma Faustina; Matulevicius, Raimundas
With increasing smart transportation systems and services, potential security and privacy threats are growing. In this work, we analyze privacy and security threats in car-sharing systems, and discuss the problems with the transparency of services, users’ personal data collection, and how the legislation manages these issues. Based on analyzed requirements, we design a compact privacy-preserving solution for car-sharing systems. Our proposal combines digital signature schemes and group signature schemes, in order to protect user privacy against curious providers, increase security and non-repudiation, and be efficient even for systems with restricted devices. The evaluation of the proposed solution demonstrates its security and a practical usability for constrained devices deployed in vehicles and users’ smartphones.
Enhancing Service Continuity in Non-Terrestrial Networks via Multi-Connectivity Offloading
(IEEE, 2024-07-29) Sadovaya, Yekaterina; Vikhrova, Olga; Andreev, Sergey; Yanikomeroglu, Halim
Non-terrestrial networks (NTNs) have recently emerged as a promising paradigm for computation-intensive six-generation (6G) applications, which may range from augmented reality to disaster relief. Moreover, NTNs can cater to uninterrupted connectivity needs in both rural and urban areas. In urban settings, uncrewed aerial vehicles (UAVs) and high-altitude platform stations (HAPS) play crucial roles in supporting delay-sensitive computation applications for terrestrial UEs when terrestrial networks face limitations. Given the emerging interest in multi-connectivity for NTNs, this letter investigates UAV- and HAPS-assisted multi-connectivity computation offloading in urban areas. Specifically, we propose two novel multi-connectivity offloading strategies to improve the probability of timely task computation, along with a framework for optimizing the corresponding offloading probabilities onto HAPS and UAVs. Our results demonstrate that utilizing multi-connectivity in NTN-assisted offloading can achieve a 75% reduction in task computation delay as compared to scenarios with no offloading.

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