Search arXivSearch

arXiv · 2405.16777

Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks

Abstract

With the increasing of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliable and high-speed cellular vehicle-to-everything (C-V2X) communication has posed significant challenges due to the high mobility of vehicles. For improving the network performance and reliability, multi-connectivity technology has emerged as a crucial transmission mode for C-V2X in the 5G era. To this end, this paper proposes a framework for analyzing the performance of multi-connectivity in C-V2X downlink transmission, with a focus on the performance indicators of joint distance distribution and coverage probability. Specifically, we first derive the joint distance distribution of multi-connectivity. By leveraging the tools of stochastic geometry, we then obtain the analytical expressions of coverage probability based on the previous results for general multi-connectivity cases in C-V2X. Subsequently, we evaluate the effect of path loss exponent and downlink base station density on coverage probability based on the proposed analytical framework. Finally, extensive Monte Carlo simulations are conducted to validate the effectiveness of the proposed analytical framework and the simulation results reveal that multi-connectivity technology can significantly enhance the coverage probability in C-V2X.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luofang Jiao, Tianqi Zhang, Jiwei Zhao, Yunting Xu, Haibo Zhou. 2024-05-27. Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks. https://arxiv.org/abs/2405.16777

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

KEEP EXPLORING

Related papers

Towards optimal algorithms for the recovery of low-dimensional models with linear rates

We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this question has been at the center of many applications. Lately, with the success of models and methods relying on deep neural networks, there has been a multiplication of different algorithms and recovery results. Comparing the performance of recovery algorithms becomes a complex task without a unifying framework. In this article, as a first step for the study of general algorithms for low-dimensional recovery, we study a class of generalized projected gradient descent algorithms that can recover a given low-dimensional model with linear rates. The obtained rates decouple the impact of the quality of the measurements with respect to the model from the geometry of the properties of the chosen generalized projection: we can directly measure performance through a restricted Lipschitz constant of the projection with respect to the low dimensional model. By optimizing this constant, we define an optimal generalized projected gradient descent. Our general approach provides an optimality result in the case of sparse recovery. Moreover, our framework allows for a common interpretation of linear rates of recovery in the context of both sparse models and models induced by some ``plug-and-play'' imaging methods that rely on deep neural networks. These rates of recovery are observed in experiments on synthetic and real data.

eess.SP

Tracking Driving Stressors through Multimodal Physiological Monitoring

Understanding and mitigating driving stress is important for improving road safety and driver well-being. Reliable estimation, however, requires distinguishing biobehavioral responses to individual stressors from gradual physiological and contextual changes. We collected physiological data and vehicle telemetry from 31 participants across 44 simulated-driving sessions containing controlled stressor events. Under cross-validation, a multimodal classifier achieved an AUROC of 0.768 when distinguishing the stressor phase from an earlier baseline, reflecting both stressor effects and temporal drift. Controlling for drift retained an AUROC of 0.661, but revealed stronger responses to sustained than brief stressors, and shifted feature attribution toward phasic cardiac and electrodermal markers. We further quantified the interaction between model-estimated physiological stress and observable changes in vehicle control through simulation telemetry. Our findings show that stressor-aware modeling can identify physiologically grounded responses that correspond to meaningful changes in driving behavior.

eess.SP

Resolution-Aliasing Trade-off in Near-Field Localisation

Extremely Large-scale MIMO (XL-MIMO) systems operating in Near-Field (NF) introduce new degrees of freedom for accurate source localisation, but make dense arrays impractical. Sparse or distributed arrays can reduce hardware complexity while maintaining high resolution, yet sub-Nyquist spatial sampling introduces aliasing artefacts in the localisation ambiguity function. This paper presents a unified framework to jointly characterise resolution and aliasing in NF localisation and study the trade-off between the two. Leveraging the concept of local chirp spatial frequency, we derive analytical expressions linking array geometry and sampling density to the spatial bandwidth of the received field. We introduce two geometric tools--Critical Antenna Elements (CAEs) and the Non-Contributive Zone (NCZ)--to intuitively identify how individual antennas contribute to resolution and/or aliasing. Our analysis reveals that resolution and aliasing are not always strictly coupled, e.g., increasing the array aperture can improve resolution without necessarily aggravating aliasing. These results provide practical guidelines for designing NF arrays that optimally balance resolution and aliasing, supporting efficient XL-MIMO deployment.

eess.SP