Motivated by 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, not requiring a known or differentiable channel model - a crucial step towards deployment in practice. Further, we motivate 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.

翻译：受机器学习工具在无线通信领域近期成功的启发，韦弗于1949年提出的语义通信思想重新受到关注。该思想突破了香农经典设计范式，旨在传输消息的含义（即语义）而非其精确版本，从而节省信息速率。本文应用随机策略梯度（Stochastic Policy Gradient, SPG）通过强化学习设计语义通信系统，无需已知或可微的信道模型——这是向实际部署迈出的关键一步。此外，我们从接收变量与目标变量之间互信息最大化的角度，论证了SPG在经典通信和语义通信中的应用。数值结果表明，我们的方法在收敛速度略有下降的情况下，仍能达到基于重参数化技巧的模型感知方法相媲美的性能。