Multi-Objective Optimization of Energy-Efficient Base Station Placement for Hybrid Highway Networks Supporting Autonomous Vehicle Mobility

Theincreasing deployment of Autonomous Vehicles (AVs) on highways presents new challenges for the underlying communication infrastructure, which must ensure low latency, high reliability, and energy efficiency. This study proposes a novel approach for Base Satation (BS) placement in the hybrid fiber-wireless networks specifically designed for linear highway environments. By formulating the deployment as a multi-objective op timization problem, the model simultaneously minimizes total network energy consump tion and end-to-end latency while maximizing highway coverage. A dynamic traffic-aware sleep mode mechanism is also introduced to reduce power usage during low-density traffic conditions. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to explore Pareto-optimal configurations, and the simulation results demonstrate signifi cant trade-offs among the objectives. The proposed framework reduces the Base Satation (BS) energy consumption by up to 40% while maintaining a latency below 10 ms and achieving coverage above 95%. These findings offer an effective deployment strategy for next-generation vehicular communication networks.

5G and beyond; Autonomous vehicles; Energy efficiency; Hybrid network architecture; Multi-objective optimization