Li, Runhua;
Sun, Jian;
Xue, Jiang;
Masouros, Christos;
(2024)
Scenario-Aware Learning Approaches to Adaptive Channel Estimation.
IEEE Transactions on Communications
, 72
(2)
874 -889.
10.1109/tcomm.2023.3330878.
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Abstract
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 <italic>Scenario-Aware Hyper-Network</italic> (SAH-Net) that incorporates the <italic>embedding loss</italic> 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.
| Type: | Article |
|---|---|
| Title: | Scenario-Aware Learning Approaches to Adaptive Channel Estimation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcomm.2023.3330878 |
| Publisher version: | https://doi.org/10.1109/TCOMM.2023.3330878 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Scenario-Adaptive Channel Estimation, Deep Learning, Neural Network, Scenario-Aware Hyper-Network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10185481 |
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