Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective

TL;DR

This paper proposes an oblivious game-theoretic approach for non-cooperative radio resource scheduling in vehicular networks, addressing high mobility and traffic variations with an online learning algorithm validated through simulations.

Contribution

It introduces an oblivious equilibrium framework for vehicle-to-vehicle resource allocation, transforming a complex stochastic game into a tractable finite-state game and providing an online learning method.

Findings

The OE approximation closely matches the Markov perfect equilibrium.

The online learning algorithm effectively adapts to network dynamics.

Numerical results demonstrate improved resource utilization and performance.

Abstract

This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed second-price auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm.