Multi-objective node placement optimization in multiplex 6G wireless networks using quantum-inspired evolutionary algorithms

Abstract

Abstract The planning of future sixth-generation (6G) wireless networks needs efficient node placement algorithms that can handle heterogeneous network deployment, high connectivity density, and competing performance requirements. Current node placement algorithms mostly use single-layer graph models and traditional optimization methods, which are inadequate to model the interdependent relationship of future 6G networks. In this paper, we present a quantum-inspired evolutionary learning-based multi-objective optimization framework for node placement in multiplex 6G wireless networks. The network is represented as a multiplex graph to capture the interactions between the capacity, latency, and interference layers. The solutions are probabilistically represented using quantum-inspired representations to facilitate global exploration and prevent premature convergence in the high-dimensional search space. A composite fitness expression is designed to address the optimization of network capacity contribution, incentive-aware participation, and multi-layer node centrality simultaneously. Extensive simulations validate that the proposed framework achieves a balanced fitness score of 3.0355 and a $$75.6\%$$ node cooperation rate. Compared to existing methods, the QIEA framework improves overall deployment fitness by $$61\%$$ over random selection and $$12\%$$ over greedy degree based placement. Furthermore, it converges $$23\%$$ faster than standard Genetic Algorithms (GA), demonstrating superior efficiency and scalability for largescale 6G network optimization.