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

arXiv:2305.03571v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv attention communication communications cs.it cs.lg design eess.sp free gradient learning tools machine machine learning math.it meaning paradigm policy reinforcement reinforcement learning semantic semantics stat.ml stochastic success tools type wireless wireless communications