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.

翻译：基于深度学习的联合信源信道编码（DJSCC）有望成为下一代无线网络的关键技术。然而，现有DJSCC方案通常针对特定信道条件进行训练，仍面临信道适应性挑战。本文提出一种利用超网络实现信道自适应的通用DJSCC框架。为定制面向通信系统的超网络框架，我们提出内存高效的超网络参数化方法，进而开发名为Hyper-AJSCC的信道自适应DJSCC网络。与现有基于注意力机制的自适应DJSCC相比，Hyper-AJSCC引入的参数更少，且无需对现有DJSCC网络的神经网络架构进行实质性修改即可无缝结合。大量实验表明，与最先进的基线方法相比，Hyper-AJSCC具有更优越的信道条件适应性和更高的内存效率。