Quantum-Inspired Raute Optimizer for Terrain-Aware NextG Network Coverage

Abstract

This paper proposes a novel terrain-aware, two-layer, graphics processing unit (GPU)-accelerated framework for base station placement optimization. In the first layer, three-dimensional (3D) K-means clustering is employed for initial base station seeding using real geographical, population, and elevation data, while two-dimensional (2D) K-means is applied in scenarios without elevation information. The second layer utilizes the quantum-inspired Raute optimizer (QRO), an adaptive hybrid metaheuristic that refines the initial placements. QRO dynamically alternates between exploration and exploitation through quantum-governed role switching and incorporates a novel inter-algorithm “bartering” mechanism for solution exchange with other heuristics. To validate the proposed framework, a comprehensive benchmarking study is performed on both terrain-aware 3D and 2D configurations. Within each framework, QRO is rigorously compared against six established metaheuristics. The GPU-accelerated simulations adopt the 3GPP rural macro (RMa) model and assess population coverage, convergence, and runtime. Additional experiments confirm robustness to spectrum-sensing errors and linear scalability with massive user densities. Results show that QRO—especially with bartering—consistently achieves higher coverage with faster convergence than competing metaheuristics. Multi-objective evaluation further demonstrates tunable trade-offs between coverage maximization and interference mitigation for practical network planning.