Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1905.05075

Intelligent Physical Layer Security Approach for V2X Communication

Haji M. Furqan·Muhammad Sohaib J. Solaija·Jehad M. Hamamreh·Huseyin Arslan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Intelligent transportation systems (ITS) with advanced sensing and computing technologies are expected to support a whole new set of services including pedestrian and vehicular safety, internet access for vehicles, and eventually, driverless cars. Wireless communication is a major driving factor behind ITS, enabling reliable communication between vehicles, infrastructure, pedestrians and network, generally referred to as vehicle to everything (V2X) communication. However, the broadcast nature of wireless communication renders it prone to jamming, eavesdropping and spoofing attacks which can adversely affect ITS. Keeping in view this issue, we suggest the use of an intelligent security framework for V2X communication security, referred to as intelligent V2X security (IV2XS), to provide a reliable and robust solution capable of adapting to different conditions, scenarios and user requirements. We also identify the conditions that impact the security and describe the open challenges in achieving a realistic IV2XS system.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haji M. Furqan, Muhammad Sohaib J. Solaija, Jehad M. Hamamreh, Huseyin Arslan. 2020-08-08. Intelligent Physical Layer Security Approach for V2X Communication. https://arxiv.org/abs/1905.05075

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

FOCAL: Fine-Grained Optimal-Transport-Driven Contrastive Alignment of Language and ECGs with Waveform Enhancement

Electrocardiograms (ECGs) are essential non-invasive tools for diagnosing cardiovascular diseases. While recent multimodal ECG-Report contrastive learning methods have shown promise for zero-shot ECG interpretation, they predominantly rely on global representations, failing to capture the fine-grained relationship between localized waveform patches and specific pathological tags. This limitation is further exacerbated by the fact that nearly 55% of standard clinical reports (e.g., in MIMIC-ECG) lack explicit waveform descriptions. In this paper, we propose FOCAL, a novel framework that achieves precise, fine-grained alignment between localized ECG segments and individual report tags via Optimal Transport. Furthermore, because fine-grained alignment at the tag level exacerbates the false negative problem among reports sharing common diagnoses, we introduce a semantic similarity matrix to guide the contrastive objective and correct misalignments. To address the scarcity of detailed waveform text, we introduce a coarse-to-fine enrichment pipeline that leverages Large Language Models (LLMs) to recover missing semantics, utilizing a coarse model verification step to rigorously filter out hallucinations. Extensive experiments across six datasets demonstrate that FOCAL establishes new state-of-the-art performance in zero-shot prediction and linear probing.

eess.SP↗
arXiv2026

The AECM Algorithm for Deterministic Maximum Likelihood Direction Finding in the Presence of Gaussian Mixture Noise

Gaussian mixture noise can model non-Gaussian noise and also be used when outliers are present in measurements. For deterministic maximum likelihood direction finding in Gaussian mixture noise, the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, an extension of the expectation-maximization algorithm, was applied and designed by Kozick and Sadler twenty odd years ago, which simultaneously updates DOA estimates at each iteration and cannot properly converge under unequal signal powers. In this paper, the Alternating Expectation-Conditional Maximization (AECM) algorithm, an extension of the SAGE algorithm, is applied and designed, which utilizes multiple less informative versions of the complete data and the golden section search method to update DOA estimates at each iteration sequentially (one by one). Theoretical analysis reveals that the AECM algorithm possesses nearly the same computational complexity of each iteration as the SAGE algorithm. However, simulation results prove that the AECM algorithm exhibits faster stable convergence and is more attractive in computation. Importantly, the AECM algorithm, unlike existing subspace based robust DOA estimation methods, can still be used when signals are coherent.

eess.SP↗
arXiv2026

Contact-Limited Throughput of a Buoyless Acoustic-to-LEO Gateway With Anticipatory Preparation

A buoyless acoustic-to-LEO gateway with predictable contacts is analyzed. Advanceable cross-medium preparation competes with acoustic collection, while residual RF acquisition consumes contact time. Exact fluid and packet service, a fluid-optimal fixed preparation lead, tandem-queue stability, and one-contact reliability are derived. An explicit service-bias identity shows why treating acquisition as advanceable can select an insufficient lead. For lognormal preparation, conditions are characterized under which variability improves mean service while degrading reliability. Monte Carlo and packet-queue simulations corroborate the analysis with synthetic parameters; in a diagnostic case, accounting for residual RF acquisition raises the actual sustainable rate from 8.776 to 9.079 kbit/s.

eess.SP↗