Open Access

ARTICLE

Quantum-Inspired Optimization Algorithm for 3D Multi-Objective Base-Station Deployment in Next-Generation 5G/6G Wireless Network

Yao-Hsin Chou¹, Cheng-Yen Hua¹, Ru-Wei Tseng¹, Shu-Yu Kuo^2,*

1 Department of Computer Science and Information Engineering, National Chi Nan University, Puli, 54561, Taiwan
2 Department of Information Management, National Yunlin University of Science and Technology, Douliu, 640, Taiwan

* Corresponding Author: Shu-Yu Kuo. Email:

(This article belongs to the Special Issue: Heuristic Algorithms for Optimizing Network Technologies: Innovations and Applications)

Computers, Materials & Continua 2026, 87(2), 41 https://doi.org/10.32604/cmc.2025.075705

Received 06 November 2025; Accepted 15 December 2025; Issue published 12 March 2026

Abstract

The rapid growth of mobile and Internet of Things (IoT) applications in dense urban environments places stringent demands on future Beyond 5G (B5G) or Beyond 6G (B6G) networks, which must ensure high Quality of Service (QoS) while maintaining cost-efficiency and sustainable deployment. Traditional strategies struggle with complex 3D propagation, building penetration loss, and the balance between coverage and infrastructure cost. To address this challenge, this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate (GQTS-QNG) framework for 3D base-station deployment optimization. The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost. A binary-to-decimal encoding mechanism is designed to represent discrete placement coordinates and base station types, leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments. Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions. Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times. Additionally, our method generates well-distributed and structured Pareto fronts, offering diverse planning options that allow operators to flexibly balance cost and performance requirements. These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.

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Keywords

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