Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks
This addresses timely information dissemination for vehicles in networks, but it is incremental as it applies existing optimization and learning methods to a specific domain.
The paper tackles the problem of minimizing age-of-information and power consumption in vehicular networks by optimizing unicast-multicast scheduling and power allocations, proposing an ant colony optimization solution for near-optimal performance and a deep reinforcement learning framework for real-time implementation.
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.