标题： 基于随机策略梯度的语义通信无模型强化学习 显示英文标题

标题： Model-free Reinforcement Learning of Semantic Communication by Stochastic Policy Gradient

摘要： 随着机器学习工具在无线通信中的近期成功，Weaver于1949年提出的语义通信概念重新受到关注。 它通过旨在传输消息的含义（即语义）而不是其精确版本，打破了香农的经典设计范式，从而实现了信息速率的节省。 在本工作中，我们通过强化学习应用随机策略梯度（SPG）来设计一个语义通信系统，分离发送器和接收器，并不需要已知或可微的信道模型——这是实际部署中的关键一步。 此外，我们从接收变量和目标变量之间的互信息最大化中推导出SPG在经典和语义通信中的使用。 数值结果表明，我们的方法在性能上与基于重参数化技巧的模型感知方法相当，尽管收敛速度有所下降。 显示英文摘要

摘要： Following the recent success of Machine Learning tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning, i.e., semantics, of a message instead of its exact version, allowing for information rate savings. In this work, we apply the Stochastic Policy Gradient (SPG) to design a semantic communication system by reinforcement learning, separating transmitter and receiver, and not requiring a known or differentiable channel model -- a crucial step towards deployment in practice. Further, we derive the use of SPG for both classic and semantic communication from the maximization of the mutual information between received and target variables. Numerical results show that our approach achieves comparable performance to a model-aware approach based on the reparametrization trick, albeit with a decreased convergence rate.