  Semantic communication (SemCom) has received considerable attention for its
ability to reduce data transmission size while maintaining task performance.
However, existing works mainly focus on analog SemCom with simple channel
models, which may limit its practical application. To reduce this gap, we
propose an orthogonal frequency division multiplexing (OFDM)-based SemCom
system that is compatible with existing digital communication infrastructures.
In the considered system, the extracted semantics is quantized by scalar
quantizers, transformed into OFDM signal, and then transmitted over the
frequency-selective channel. Moreover, we propose a semantic importance
measurement method to build the relationship between target task and semantic
features. Based on semantic importance, we formulate a sub-carrier and bit
allocation problem to maximize communication performance. However, the
optimization objective function cannot be accurately characterized using a
mathematical expression due to the neural network-based semantic codec. Given
the complex nature of the problem, we first propose a low-complexity
sub-carrier allocation method that assigns sub-carriers with better channel
conditions to more critical semantics. Then, we propose a deep reinforcement
learning-based bit allocation algorithm with dynamic action space. Simulation
results demonstrate that the proposed system achieves 9.7% and 28.7%
performance gains compared to analog SemCom and conventional bit-based
communication systems, respectively.
