Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks

TL;DR

This paper proposes a dynamic scheduling framework using deep reinforcement learning to optimize unicast and multicast updates in vehicular networks, reducing age-of-information and power consumption.

Contribution

It introduces a novel DRL-based approach for real-time unicast-multicast scheduling to minimize AoI and power in vehicular networks, outperforming traditional methods.

Findings

DRL framework effectively balances AoI and power consumption.

Simulation shows near-optimal performance of the proposed algorithms.

Trade-offs between network parameters and performance metrics are analyzed.

Abstract

This paper investigates the problem of minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. Each vehicle is interested in maintaining the freshness of its information status about one or more physical processes. A framework is proposed to optimize the decisions to unicast, multicast, broadcast, or not transmit updates to vehicles as well as power allocations to minimize the AoI and the RSU's power consumption over a time horizon. The formulated problem is a mixed-integer nonlinear programming problem (MINLP), thus a global optimal solution is difficult to achieve. In this context, we first develop an ant colony optimization (ACO) solution which provides near-optimal performance and thus serves as an efficient benchmark. Then, for real-time implementation, we develop a deep reinforcement learning (DRL) framework that captures the vehicles' demands and channel conditions in the state space and assigns processes to vehicles through dynamic unicast-multicast scheduling actions. Complexity analysis of the proposed algorithms is presented. Simulation results depict interesting trade-offs between AoI and power consumption as a function of the network parameters.