Deep learning (DL)-based channel state information (CSI) feedback has shown promising potential to improve spectrum efficiency in massive MIMO systems. However, practical DL approaches require a sizeable CSI dataset for each scenario, and require large storage or updating bandwidth for multiple learned models. To overcome this costly barrier, we develop a solution for efficient training and deployment enhancement of DL-based CSI feedback by exploiting a lightweight translation model to cope with new CSI environments and by proposing novel dataset augmentation based on domain knowledge. Specifically, we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which employs spherical normalization to address the challenge of path loss variation. We also introduce an integration of a trainable measurement matrix and residual CSI recovery blocks within SPTM2-ISTANet+ to improve efficiency and accuracy. Using SPTM2-ISTANet+ as the anchor feedback model, we propose an efficient scenario-adaptive CSI feedback architecture. This new CSI-TransNet exploits a plug-in module for CSI translation consisting of a sparsity aligning function and lightweight DL module to reuse pretrained models in unseen environments. To work with small datasets, we propose a lightweight and general augmentation strategy based on domain knowledge. Test results demonstrate the efficacy and efficiency of the proposed solution for accurate CSI feedback given limited measurements for unseen CSI environments.

翻译：基于深度学习的信道状态信息（CSI）反馈在大规模MIMO系统中展现出提升频谱效率的潜力。然而，实际部署的深度学习方法需要为每个场景积累大量CSI数据集，且需为多个学习模型预留大量存储或更新带宽。为突破这一高昂障碍，我们提出了一种高效训练与部署增强方案：一方面利用轻量级翻译模型适应新CSI环境，另一方面基于领域知识提出新颖的数据集扩充方法。具体而言，我们首先设计了基于深度展开的CSI反馈网络SPTM2-ISTANet+，该网络采用球面归一化处理路径损耗变化问题，并在其内部集成了可训练测量矩阵与残差CSI恢复模块以提升效率与精度。以SPTM2-ISTANet+作为基准反馈模型，我们进一步提出了一种场景自适应的高效CSI反馈架构——CSI-TransNet。该架构利用包含稀疏对齐函数与轻量级深度学习模块的插件式CSI翻译模块，实现预训练模型在未见过环境中的复用。针对小数据集场景，我们设计了一种基于领域知识的轻量级通用数据增强策略。测试结果表明，所提方案能在有限测量条件下对未知CSI环境实现精准反馈，兼具高效性与有效性。