  This paper investigates task-oriented communication for edge inference, where
a low-end edge device transmits the extracted feature vector of a local data
sample to a powerful edge server for processing. It is critical to encode the
data into an informative and compact representation for low-latency inference
given the limited bandwidth. We propose a learning-based communication scheme
that jointly optimizes feature extraction, source coding, and channel coding in
a task-oriented manner, i.e., targeting the downstream inference task rather
than data reconstruction. Specifically, we leverage an information bottleneck
(IB) framework to formalize a rate-distortion tradeoff between the
informativeness of the encoded feature and the inference performance. As the IB
optimization is computationally prohibitive for the high-dimensional data, we
adopt a variational approximation, namely the variational information
bottleneck (VIB), to build a tractable upper bound. To reduce the communication
overhead, we leverage a sparsity-inducing distribution as the variational prior
for the VIB framework to sparsify the encoded feature vector. Furthermore,
considering dynamic channel conditions in practical communication systems, we
propose a variable-length feature encoding scheme based on dynamic neural
networks to adaptively adjust the activated dimensions of the encoded feature
to different channel conditions. Extensive experiments evidence that the
proposed task-oriented communication system achieves a better rate-distortion
tradeoff than baseline methods and significantly reduces the feature
transmission latency in dynamic channel conditions.

Signal Processing;Machine Learning