Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.
翻译:为了填补这一空白,我们首次在本条中提议了一个语义协议模型,通过将国家预防机制转换成以概率逻辑编程语言(ProbLog)写成的可解释的象征性图示(SPM)来构建一个语义协议模型(SPM),通过提取和合并共同的CMS及其连接,同时将国家预防机制作为CM生成器处理,这种转变是可行的。通过广泛的模拟,我们证实SPM在仅拥有0.02%的记忆时,紧紧接近其最初的国家预防机制。通过利用其可解释性和记忆效率,我们展示了几个由SPM(SPM)支持的应用程序,例如为避免碰撞而重组的SPM(SPM),以及将不同的SPMs(通过语义性昆虫计算和储存多个SPM)与非静止环境相比较。