  Task-oriented communications, mostly using learning-based joint
source-channel coding (JSCC), aim to design a communication-efficient edge
inference system by transmitting task-relevant information to the receiver.
However, only transmitting task-relevant information without introducing any
redundancy may cause robustness issues in learning due to the channel
variations, and the JSCC which directly maps the source data into continuous
channel input symbols poses compatibility issues on existing digital
communication systems. In this paper, we address these two issues by first
investigating the inherent tradeoff between the informativeness of the encoded
representations and the robustness to information distortion in the received
representations, and then propose a task-oriented communication scheme with
digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the
transmitter encodes the features into a discrete representation and transmits
it to the receiver with the digital modulation scheme. In the DT-JSCC scheme,
we develop a robust encoding framework, named robust information bottleneck
(RIB), to improve the communication robustness to the channel variations, and
derive a tractable variational upper bound of the RIB objective function using
the variational approximation to overcome the computational intractability of
mutual information. The experimental results demonstrate that the proposed
DT-JSCC achieves better inference performance than the baseline methods with
low communication latency, and exhibits robustness to channel variations due to
the applied RIB framework.
