信号处理2025，Vol.41Issue(10)：1624-1635,12.DOI:10.12466/xhcl.2025.10.003
CEO神经网络:多观测语义通信中的一致性融合方法
CEO Neural Network:A Consistency-Aware Fusion Method for Semantic Multi-Observation Communication
摘要
Abstract
As the Chinese proverb says,"Three cobblers together can rival Zhuge Liang,"meaning that collective wis-dom often surpasses individual genius.This concept is equally applicable in the research of semantic communication.To address the challenges posed by multi-source observations involving multiple categories of coexisting information as well as diverse and easily disturbed semantic expressions in real-world scenarios,this paper focuses on the Semantic Multi-Observation Communication System(SMOCS)and proposes a neural network architecture based on a semantic consistency fusion mechanism,aiming to realize effective integration and reliable discrimination of semantic informa-tion from multiple observational perspectives.The system simulates different observers modeling the same semantic event through multiple structurally heterogeneous pre-trained neural networks,each independently generating semantic discrimination probabilities,thereby forming a rich and diverse set of semantic expressions.Accordingly,this paper in-troduces a Chief Executive Officer(CEO)fusion network as the semantic integration module,which focuses on mining latent distribution consistency and complementary structures from multi-source discrimination results.This network does not directly participate in semantic feature extraction;rather,through a deep fusion mechanism,it learns the intrinsic correlations and semantic aggregation methods from the outputs of multiple observation models,thus producing more stable and accurate final decisions.The entire system's structure reflects a semantic communication optimization strategy driven by"multi-observation,collaborative modeling,and unified decision-making."To verify the efficacy of the proposed method,the MNIST handwritten digit recognition task was selected as the experimental platform,where Multilayer Per-ceptron(MLP),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM)Network,and Residual Neural Network(ResNet)subnetworks were pre-trained with fixed weights,and only the CEO network was trained to ensure the purity of the information fusion phase.Experimental results demonstrate that,compared to single-neural-network classifiers,the proposed system achieves significant improvements in recogni-tion accuracy,robustness,and anti-interference capability,exhibiting stronger fault tolerance and expression stability when faced with semantic perturbations and information loss.To verify the generalizability of the proposed architecture,simulation experiments were conducted on a text semantic transmission task,demonstrating the applicability and supe-rior performance of the model across multimodal and diverse scenarios.关键词
多观测语义通信/CEO融合网络/语义一致性融合Key words
multi-observation semantic communication/CEO fusion network/semantic consistency-based fusion分类
信息技术与安全科学引用本文复制引用
万飞,曹琦,杜雅萌,白宝明..CEO神经网络:多观测语义通信中的一致性融合方法[J].信号处理,2025,41(10):1624-1635,12.基金项目
国家重点研发计划项目(2021YFA1000500) （2021YFA1000500）
国家自然科学基金(62301406,62461160306) （62301406,62461160306）
中央高校基本科研业务费专项资金(ZYTS25301) National Key R&D Program of China(2021YFA1000500) （ZYTS25301）
The National Natural Science Foundation of China(62301406,62461160306) （62301406,62461160306）
Fundamental Research Funds for the Central Universities(ZYTS25301) （ZYTS25301）
