Reliability Assessment of Communication Systems via Host–Guest Generative Cooperative Modeling in Antenna-Port Electromagnetic Environments

Abstract

The rapid evolution of information technology has led to a surge in radio frequency devices, exposing communication systems to multisource complex electromagnetic environments (EMEs) and making reliability assessment crucial. However, existing methods face two key limitations: system link modeling struggles to represent real dynamic interference, while analytical assessments based on “sampling–playback” of real EMEs waveforms require excessive testing resources and contain significant feature redundancy, failing to isolate the core factors causing device performance degradation. From the perspective of interference effect equivalence, this article proposes a host–guest generative cooperative reliability assessment framework based on a generative cooperative network (G-CoopNet). First, a baseline signal construction method via feature distillation is developed, where multiobjective genetic algorithms refine massive heterogeneous emitter waveforms into four high-coverage baseline signals, enabling effective dimensionality reduction and EMEs characterization. Second, by incorporating real test data, the actual bit-error-rate degradation inversely constrains environmental modeling to accurately characterize the host device’s response differences. Finally, G-CoopNet performs host–guest cooperative training to transform real EMEs into EME configuration graphs, which preserve the performance degradation effects of real interference on the host device while removing redundant environmental features. Experimental results show that the proposed method improves reliability assessment accuracy by 5.4%, providing a new intelligent paradigm for low-cost, high-efficiency laboratory equivalent retesting and system reliability assessment under multisource interference.