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Resilient Vehicular Communications under Imperfect Channel State Information

Tingyu Shui, Walid Saad, Ye Hu, Mingzhe Chen

TL;DR

This work tackles QoS preservation in cellular V2X networks when channel state information is imperfect and highly dynamic. It introduces a two-phase resilience framework—absorption to estimate the CSI-error PDF and define an absorption strategy, followed by adaptation to recover QoS using the estimated PDF—and a hazard-rate metric to balance the phases. A deconvolution-based PDF estimation method, a bi-level optimization decoupled into absorption and adaptation subproblems, and a minimum-weight matching approach guide resource management. Theoretical bounds link the adaptation capability to estimation accuracy and absorption design, while simulations show substantial improvements in V2V delay and V2I throughput over model-based and data-driven baselines, without sacrificing absorption-phase QoS. Overall, the framework provides a practical route to robust C-V2X performance under unknown CSI imperfections in realistic vehicular environments.

Abstract

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting quality-of-service (QoS) requirements of vehicular communication links given limited network resources, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel two-phase framework is proposed to instill resilience into C-V2X networks under unknown imperfect CSI. The resilience of the C-V2X network is defined, quantified, and optimized the first time through two principal dimensions: absorption phase and adaptation phase. Specifically, the probability distribution function (PDF) of the imperfect CSI is estimated during the absorption phase through dedicated absorption power scheme and resource block (RB) assignment. The estimated PDF is further used to analyze the interplay and reveal the tradeoff between these two phases. Then, a novel metric named hazard rate (HR) is exploited to balance the C-V2X network's prioritization on absorption and adaptation. Finally, the estimated PDF is exploited in the adaptation phase to recover the network's QoS through a real-time power allocation optimization. Simulation results demonstrate the superior capability of the proposed framework in sustaining the QoS of the C-V2X network under imperfect CSI. Specifically, in the adaptation phase, the proposed design reduces the vehicle-tovehicle (V2V) delay that exceeds QoS requirement by 35% and 56%, and improves the average vehicle-to-infrastructure (V2I) throughput by 14% and 16% compared to the model-based and data-driven benchmarks, respectively, without compromising the network's QoS in the absorption phase.

Resilient Vehicular Communications under Imperfect Channel State Information

TL;DR

This work tackles QoS preservation in cellular V2X networks when channel state information is imperfect and highly dynamic. It introduces a two-phase resilience framework—absorption to estimate the CSI-error PDF and define an absorption strategy, followed by adaptation to recover QoS using the estimated PDF—and a hazard-rate metric to balance the phases. A deconvolution-based PDF estimation method, a bi-level optimization decoupled into absorption and adaptation subproblems, and a minimum-weight matching approach guide resource management. Theoretical bounds link the adaptation capability to estimation accuracy and absorption design, while simulations show substantial improvements in V2V delay and V2I throughput over model-based and data-driven baselines, without sacrificing absorption-phase QoS. Overall, the framework provides a practical route to robust C-V2X performance under unknown CSI imperfections in realistic vehicular environments.

Abstract

Cellular vehicle-to-everything (C-V2X) networks provide a promising solution to improve road safety and traffic efficiency. One key challenge in such systems lies in meeting quality-of-service (QoS) requirements of vehicular communication links given limited network resources, particularly under imperfect channel state information (CSI) conditions caused by the highly dynamic environment. In this paper, a novel two-phase framework is proposed to instill resilience into C-V2X networks under unknown imperfect CSI. The resilience of the C-V2X network is defined, quantified, and optimized the first time through two principal dimensions: absorption phase and adaptation phase. Specifically, the probability distribution function (PDF) of the imperfect CSI is estimated during the absorption phase through dedicated absorption power scheme and resource block (RB) assignment. The estimated PDF is further used to analyze the interplay and reveal the tradeoff between these two phases. Then, a novel metric named hazard rate (HR) is exploited to balance the C-V2X network's prioritization on absorption and adaptation. Finally, the estimated PDF is exploited in the adaptation phase to recover the network's QoS through a real-time power allocation optimization. Simulation results demonstrate the superior capability of the proposed framework in sustaining the QoS of the C-V2X network under imperfect CSI. Specifically, in the adaptation phase, the proposed design reduces the vehicle-tovehicle (V2V) delay that exceeds QoS requirement by 35% and 56%, and improves the average vehicle-to-infrastructure (V2I) throughput by 14% and 16% compared to the model-based and data-driven benchmarks, respectively, without compromising the network's QoS in the absorption phase.
Paper Structure (18 sections, 6 theorems, 45 equations, 9 figures, 1 table, 2 algorithms)

This paper contains 18 sections, 6 theorems, 45 equations, 9 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Prior Works
  3. Contributions
  4. System Model
  5. Vehicular QoS requirements
  6. Imperfect CSI Model
  7. Problem Formulation
  8. Absorption Phase
  9. Deconvolution based Estimation
  10. Adaptation Capability
  11. HR during absorption
  12. matching and Power Optimization during absorption
  13. Adaptation Phase
  14. Simulation Results and Analysis
  15. Conclusion
  16. ...and 3 more sections

Key Result

Theorem 1

An upper bound on the MSE of $\hat{f}_{E,m}$ is given by: where $\beta_m = K \pi (1-\delta_m^2)$ and $o_{nm} = \frac{p^{\textrm{V}}_{m,\textrm{a}} L^{\textrm{V}}_{m, \textrm{a}}}{p^{\textrm{I}}_{n,\textrm{a}} L^{\textrm{I}}_{nm,\textrm{a}}}$.

Figures (9)

  • Figure 1: System model of the considered C-V2X network. Orthogonal RB are shared by vehicular links with heterogeneous QoS requirements.
  • Figure 2: Illustration of how the various parameters change over time scales: a) matching $\boldsymbol{A}$ and transmit power scheme $\boldsymbol{p}_{\mathcal{M},t}$, $\boldsymbol{p}_{\mathcal{N},t}$, and b) large-scale fading $L_t$ and small-scale fading $g_t$.
  • Figure 3: The proposed two-phase resilient design for solving the bi-level problem \ref{['opt1']}.
  • Figure 4: The bipartite graph $\mathcal{G} = (\mathcal{M} \times \mathcal{N}, \mathcal{E}_{\mathcal{G}})$ used for solving \ref{['opt2']}.
  • Figure 5: The different cases of optimal absorption power scheme.
  • ...and 4 more figures

Theorems & Definitions (13)

  • Theorem 1
  • proof
  • Lemma 1
  • proof
  • Theorem 2
  • proof
  • Corollary 1
  • Proposition 1
  • proof
  • Corollary 2
  • ...and 3 more