  Deep learning-based joint source-channel coding (DJSCC) is expected to be a
key technique for {the} next-generation wireless networks. However, the
existing DJSCC schemes still face the challenge of channel adaptability as they
are typically trained under specific channel conditions. In this paper, we
propose a generic framework for channel-adaptive DJSCC by utilizing
hypernetworks. To tailor the hypernetwork-based framework for communication
systems, we propose a memory-efficient hypernetwork parameterization and then
develop a channel-adaptive DJSCC network, named Hyper-AJSCC. Compared with
existing adaptive DJSCC based on the attention mechanism, Hyper-AJSCC
introduces much fewer parameters and can be seamlessly combined with various
existing DJSCC networks without any substantial modifications to their neural
network architecture. Extensive experiments demonstrate the better adaptability
to channel conditions and higher memory efficiency of Hyper-AJSCC compared with
state-of-the-art baselines.
