Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Dynamic Cooperative MAC Optimization in RSU-Enhanced VANETs: A Distributed Approach

Zhou Zhang, Saman Atapattu, Yizhu Wang, Sumei Sun, Kandeepan Sithamparanathan

TL;DR

This work tackles throughput optimization in RSU-enabled VANETs by formulating a distributed cooperative MAC as a sequential planned decision problem. It introduces the RPCA strategy, yielding an optimal policy with a two-stage decision process that selectively probes the RSU and leverages V2V/V2R CSI to maximize long-term throughput, and provides a closed-form, low-complexity approximation for practical deployment. A location-aware, two-timescale MAC algorithm is developed, enabling online reconfiguration based on periodically updated vehicle-RSU distances and yielding substantial throughput gains over several baseline strategies in mobility-rich scenarios. The results demonstrate that RPCA and the accompanying distributed MAC framework offer robust, scalable improvements for RSU-assisted VANETs with realistic channel dynamics and vehicle mobility.

Abstract

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehicular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a ``large-scale'' phase accounting for gradual changes in vehicle locations and a ``small-scale'' phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.

Dynamic Cooperative MAC Optimization in RSU-Enhanced VANETs: A Distributed Approach

TL;DR

This work tackles throughput optimization in RSU-enabled VANETs by formulating a distributed cooperative MAC as a sequential planned decision problem. It introduces the RPCA strategy, yielding an optimal policy with a two-stage decision process that selectively probes the RSU and leverages V2V/V2R CSI to maximize long-term throughput, and provides a closed-form, low-complexity approximation for practical deployment. A location-aware, two-timescale MAC algorithm is developed, enabling online reconfiguration based on periodically updated vehicle-RSU distances and yielding substantial throughput gains over several baseline strategies in mobility-rich scenarios. The results demonstrate that RPCA and the accompanying distributed MAC framework offer robust, scalable improvements for RSU-assisted VANETs with realistic channel dynamics and vehicle mobility.

Abstract

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehicular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a ``large-scale'' phase accounting for gradual changes in vehicle locations and a ``small-scale'' phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.
Paper Structure (18 sections, 4 theorems, 5 equations, 3 figures, 3 algorithms)

This paper contains 18 sections, 4 theorems, 5 equations, 3 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Network Model
  4. Cooperative RSU-assisted V2V MAC Process
  5. Problem Formulation
  6. Sequential observation process
  7. Reward and objective function
  8. Statistical optimization
  9. Optimal RPCA Strategy and Algorithm
  10. General Formulation of the Optimal Strategy
  11. Closed-form Expression
  12. Pure Threshold Decision Strategy
  13. Two-timescale MAC Algorithm
  14. Location Information Update Procedure
  15. Proposed MAC Strategy Reconfiguration
  16. ...and 3 more sections

Key Result

Theorem 1

An optimal strategy $\mathcal{H}_s^*$ for maximizing $\sup_{\mathcal{H}_s}\{\mathbb{E}[Y_{\mathcal{H}_s}]/\mathbb{E}[T_{\mathcal{H}_s}]\}$ is as follows: Start with $k=1$, and then continue observing until the condition stop is met. Specially, after the $k$th successful channel contention, The maximal average network throughput $\lambda^*$ uniquely exists, and is determined by the solution of

Figures (3)

  • Figure 1: Topology of a VANET setup with a roadside unit (RSU)
  • Figure 2: Time framework in dual timescales.
  • Figure 3: Average network throughput comparison of alternative strategies.

Theorems & Definitions (8)

  • Definition 1
  • Theorem 1
  • Remark 1
  • Lemma 1
  • Theorem 2
  • Remark 2
  • Definition 2
  • Corollary 1