  Semantic communication (SemCom) has been deemed as a promising communication
paradigm to break through the bottleneck of traditional communications.
Nonetheless, most of the existing works focus more on point-to-point
communication scenarios and its extension to multi-user scenarios is not that
straightforward due to its cost-inefficiencies to directly scale the JSCC
framework to the multi-user communication system. Meanwhile, previous methods
optimize the system by differentiable bit-level supervision, easily leading to
a "semantic gap". Therefore, we delve into multi-user broadcast communication
(BC) based on the universal transformer (UT) and propose a reinforcement
learning (RL) based self-critical alternate learning (SCAL) algorithm, named
SemanticBC-SCAL, to capably adapt to the different BC channels from one
transmitter (TX) to multiple receivers (RXs) for sentence generation task. In
particular, to enable stable optimization via a nondifferentiable semantic
metric, we regard sentence similarity as a reward and formulate this learning
process as an RL problem. Considering the huge decision space, we adopt a
lightweight but efficient self-critical supervision to guide the learning
process. Meanwhile, an alternate learning mechanism is developed to provide
cost-effective learning, in which the encoder and decoders are updated
asynchronously with different iterations. Notably, the incorporation of RL
makes SemanticBC-SCAL compliant with any user-defined semantic similarity
metric and simultaneously addresses the channel non-differentiability issue by
alternate learning. Besides, the convergence of SemanticBC-SCAL is also
theoretically established. Extensive simulation results have been conducted to
verify the effectiveness and superiorness of our approach, especially in low
SNRs.
