Scenario-Aware Learning Approaches to Adaptive Channel Estimation
Scenario-Aware Learning Approaches to Adaptive Channel Estimation
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DOI：
10.1109/tcomm.2023.3330878
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发表时间：
2024-02
影响因子：
8.3
通讯作者：
Runhua Li;Jian Sun;Jiang Xue;C. Masouros
中科院分区：
文献类型：
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作者：
Runhua Li;Jian Sun;Jiang Xue;C. Masouros
随着下一代无线网络的频率带宽和应用的增长，将产生大量的无线传输场景、拓扑和信道结构。在这项工作中，我们超越了现有针对特定场景定制的基于学习的信道估计方法，开发了一种基于自适应学习的信道状态信息(CSI)估计方法。通过提取CSI的场景嵌入，并在每个场景中根据提取的信息自动调整信道估计方法，使学习方法具有自适应性。具体地，设计了基于学习的情景自适应信道估计算法(LACE)。LACE基于场景感知超网络(SAH-Net)，引入嵌入损失，使基于卷积神经网络(CNN)的编码者能够从CSI的时空二维特征中学习提取有效的场景嵌入。基于多层感知器(MLP)的调谐模块利用提取的嵌入来调谐信道估计方法的参数。我们的学习设计辅以分析，以验证LACE的理论性能严格优于传统的混合训练方法，该混合训练方法涉及使用所有场景的样本来训练基于深度网络的信道估计方法。结果表明，在有限场景下训练的LACE算法的性能与在每个场景下训练的基于深度网络的信道估计方法的性能相当，而复杂度更低。此外，在无限场景中训练的LASS在所有测试场景中的性能都优于混合训练方法。
The growth of frequency bandwidths and applications with the forthcoming generations of wireless networks will give rise to a multitude of wireless transmission scenarios, topologies and channel structures. In this work, we go beyond existing learning-based channel estimation methods tailored for specific scenarios, to develop an adaptive learning-based channel state information (CSI) estimation approach. We offer the adaptivity in the learning approach through extracting the scenario embeddings of CSI and adjusting the channel estimation method with the extracted information automatically in each scenario. Specifically, Learning-Based Scenario-Adaptive Channel Estimation Algorithm (LACE) is designed. LACE is based on a Scenario-Aware Hyper-Network (SAH-Net) that incorporates the embedding loss to make the Convolutional Neural Network (CNN) based encoder learn to extract the effective scenario embeddings from the time-space two dimensional features of the CSI. The extracted embeddings are utilized by a Multi-Layer Perceptron (MLP) based tuning module to tune the parameters of the channel estimation method. Our learning design is complemented with analysis to verify that the theoretical performance of LACE is strictly superior to that of the mix-training method, which involves conventionally training the deep network-based channel estimation method using samples from all scenarios. Our results show that the performance of LACE trained in finite scenarios is comparable to that of the deep network-based channel estimation method trained in each scenario, while having lower complexity. Further more, the performance of LACE trained in infinite scenarios is demonstrated to be superior to that of the mix-training method in all test scenarios.