Large Model-Based Multi-Community Virtual Interaction Scheme in Wireless Networks

The increasing ubiquity of 5G/6G wireless communication networks has created unprecedented opportunities for multi-community collaboration and virtual engagement. However, existing platforms often fail to support scalable, context-aware, and efficient interaction across decentralized social groups. In this paper, we propose LM-MCVIS (Large Model-Based Multi-Community Virtual Interaction Scheme), a novel framework designed to facilitate personalized content exchange and context-aware message routing in wireless community networks. LM-MCVIS integrates three key components: (1) an Edge-Aware Prompt Compression (EPC) module that semantically distills conversation inputs to reduce wireless transmission overhead; (2) a Community State Encoder (CSE) that models dynamic group structures and latent social contexts; and (3) a Federated Reinforcement Optimizer (FRO) that enables privacy-preserving, feedback-driven content routing. We evaluate LM-MCVIS on three public multi-community datasets using a high-fidelity 5G/6G emulator and benchmark its performance against five strong baselines. Results demonstrate significant gains in engagement depth, interaction diversity, response latency, and bandwidth savings. Ablation studies further validate the individual impact of each module. LM-MCVIS offers a scalable and modular paradigm for intelligent community interaction over wireless networks, with broad implications for collaborative learning, digital governance, and decentralized social ecosystems.