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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

Bo Yang, Xueyao Zhang, Zhiwen Yu, Xuelin Cao, Chongwen Huang, George C. Alexandropoulos, Yan Zhang, Merouane Debbah, Chau Yuen

TL;DR

The paper tackles safety-critical MEC offloading for autonomous driving in networks where V2I offloading and V2V spectrum reuse cause interference. It introduces Reconfigurable Intelligent Computational Surfaces (RICS) that jointly provide reflection/refraction beamforming and on-surface analog computation to suppress interference and accelerate tasks. A joint optimization is formulated to maximize a driving-safety metric by jointly choosing offloading ratios, spectrum sharing, and RICS parameters, and is solved via an alternating iterative optimization framework (AIOA) that decomposes into FP, log-sum-exp/DC-SCA, and SDR-based subproblems. Numerical results show that the RICS-enabled approach improves safety performance and V2V interference suppression, with larger RICS element counts yielding greater gains and rapid convergence. This work demonstrates a practical integration of metamaterial-enabled computation and spectrum sharing for MEC-supported autonomous driving networks.

Abstract

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

TL;DR

The paper tackles safety-critical MEC offloading for autonomous driving in networks where V2I offloading and V2V spectrum reuse cause interference. It introduces Reconfigurable Intelligent Computational Surfaces (RICS) that jointly provide reflection/refraction beamforming and on-surface analog computation to suppress interference and accelerate tasks. A joint optimization is formulated to maximize a driving-safety metric by jointly choosing offloading ratios, spectrum sharing, and RICS parameters, and is solved via an alternating iterative optimization framework (AIOA) that decomposes into FP, log-sum-exp/DC-SCA, and SDR-based subproblems. Numerical results show that the RICS-enabled approach improves safety performance and V2V interference suppression, with larger RICS element counts yielding greater gains and rapid convergence. This work demonstrates a practical integration of metamaterial-enabled computation and spectrum sharing for MEC-supported autonomous driving networks.

Abstract

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.
Paper Structure (14 sections, 23 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 23 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. RICS-aided Autonomous Vehicular Networks
  3. RICS Modeling
  4. System and Channel Modeling
  5. Task and Partial Offloading Modeling
  6. Problem Formulation and Analysis
  7. Problem Formulation
  8. Outage Probability Analysis
  9. Proposed RISC-Aided Inference Solution
  10. Optimization of the Task Offloading Ratio
  11. Optimization of the Spectrum Sharing Strategy
  12. Reflection and Refraction Matrices Optimization
  13. Numerical Results
  14. Conclusions

Figures (2)

  • Figure 1: The proposed RICS-aided autonomous driving paradigm is shown in (a), where a V2V Rx suffers severe co-channel interference from neighboring CVs. An optimized RICS can mitigate the interference of the V2V link, while improving the V2I link performance. In (b), the RICS structure is configured as RR+AC mode, being capable to create an "interference-free zone" via properly configuring the relative permittivity and permeability of the metamaterial included in the intelligence computation layer.
  • Figure 2: Convergence of the proposed AIOA algorithm is shown in (a). Available transmission power with varying value of $P_{t}$ is shown in (b), where $N=5$. Sum safety coefficient with varying number of CVs is shown in (c).