Large Model‐Based Experiential Landscape Design in Wireless Networks

ABSTRACT To address the problems of dynamic user experience optimization, adaptive resource allocation, and personalized service provisioning in modern wireless communication networks, this letter proposes a large model‐based experiential landscape design method utilizing contrastive learning and pre‐trained transformer architectures. Considering the high complexity and heterogeneous nature of wireless environments, along with the insufficient performance of traditional optimization methods for experiential landscape design tasks, wireless signal sequences are first transformed into token‐like representations similar to those used in natural language processing. Subsequently, a pre‐trained transformer model is employed to convert shallow representations into universal wireless experience representations suitable for various downstream landscape design tasks. By transforming the experiential landscape optimization problem into a similarity analysis problem, a diversity‐sensitive transformer model architecture is designed based on contrastive learning principles, which enhances the model's sensitivity to experience differences through positive and negative sample pairs of wireless environments and proposes using information noise contrastive estimation as the loss function for fine‐tuning downstream landscape design tasks. Experimental results demonstrate that the proposed method outperforms mainstream approaches in terms of user satisfaction accuracy, quality of experience precision, service continuity recall, and F1 score metrics, achieving 89.73% accuracy in wireless experiential landscape classification.