  Thanks to its capability of classifying complex phenomena without explicit
modeling, deep learning (DL) has been demonstrated to be a key enabler of
Wireless Signal Classification (WSC). Although DL can achieve a very high
accuracy under certain conditions, recent research has unveiled that the
wireless channel can disrupt the features learned by the DL model during
training, thus drastically reducing the classification performance in
real-world live settings. Since retraining classifiers is cumbersome after
deployment, existing work has leveraged the usage of carefully-tailored Finite
Impulse Response (FIR) filters that, when applied at the transmitter's side,
can restore the features that are lost because of the the channel actions,
i.e., waveform synthesis. However, these approaches compute FIRs using offline
optimization strategies, which limits their efficacy in highly-dynamic channel
settings. In this paper, we improve the state of the art by proposing Chares, a
Deep Reinforcement Learning (DRL)-based framework for channel-resilient
adaptive waveform synthesis. Chares adapts to new and unseen channel conditions
by optimally computing through DRL the FIRs in real-time. Chares is a DRL agent
whose architecture is-based upon the Twin Delayed Deep Deterministic Policy
Gradients (TD3), which requires minimal feedback from the receiver and explores
a continuous action space. Chares has been extensively evaluated on two
well-known datasets. We have also evaluated the real-time latency of Chares
with an implementation on field-programmable gate array (FPGA). Results show
that Chares increases the accuracy up to 4.1x when no waveform synthesis is
performed, by 1.9x with respect to existing work, and can compute new actions
within 41us.
