{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:35:01Z","timestamp":1768977301720,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFE0200103"],"award-info":[{"award-number":["2024YFE0200103"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RGC Postdoctoral Fellowship Scheme of Project","award":["PDFS2425-6S05"],"award-info":[{"award-number":["PDFS2425-6S05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Millimeter-wave (mmWave) massive multiple-input, multiple-output (MIMO) systems are a cornerstone technology for integrated sensing and communication (ISAC) in sixth-generation (6G) mobile networks. These systems provide high-capacity backhaul while simultaneously enabling high-resolution environmental sensing. However, accurate channel estimation remains highly challenging due to intrinsic noise sensitivity and clustered sparse multipath structures. These challenges are particularly severe under limited pilot resources and low signal-to-noise ratio (SNR) conditions. To address these difficulties, this paper proposes HASwinNet, a deep learning (DL) framework designed for mmWave channel denoising. The framework integrates a hierarchical Swin Transformer encoder for structured representation learning. It further incorporates two complementary branches. The first branch performs sparse token extraction guided by angular-domain significance. The second branch focuses on angular-domain refinement by applying discrete Fourier transform (DFT), squeeze-and-excitation (SE), and inverse DFT (IDFT) operations. This generates a mask that highlights angularly coherent features. A decoder combines the outputs of both branches with a residual projection from the input to yield refined channel estimates. Additionally, we introduce an angular-domain perceptual loss during training. This enforces spectral consistency and preserves clustered multipath structures. Simulation results based on the Saleh\u2013Valenzuela (S\u2013V) channel model demonstrate that HASwinNet achieves significant improvements in normalized mean squared error (NMSE) and bit error rate (BER). It consistently outperforms convolutional neural network (CNN), long short-term memory (LSTM), and U-Net baselines. Furthermore, experiments with reduced pilot symbols confirm that HASwinNet effectively exploits angular sparsity. The model retains a consistent advantage over baselines even under pilot-limited conditions. These findings validate the scalability of HASwinNet for practical 6G mmWave backhaul applications. They also highlight its potential in ISAC scenarios where accurate channel recovery supports both communication and sensing.<\/jats:p>","DOI":"10.3390\/e28010124","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T17:20:42Z","timestamp":1768929642000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HASwinNet: A Swin Transformer-Based Denoising Framework with Hybrid Attention for mmWave MIMO Systems"],"prefix":"10.3390","volume":"28","author":[{"given":"Xi","family":"Han","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}]},{"given":"Houya","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2102-6683","authenticated-orcid":false,"given":"Jiaxi","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong 999077, China"}]},{"given":"Junqiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}]},{"given":"Zhiqiang","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer, North China University of Technology, Beijing 100144, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/MCOM.001.1900107","article-title":"Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless communications","volume":"58","author":"Wu","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116753","DOI":"10.1109\/ACCESS.2019.2935192","article-title":"Wireless communications through reconfigurable intelligent surfaces","volume":"7","author":"Basar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/LWC.2019.2961357","article-title":"Intelligent reflecting surface-enhanced OFDM: Channel estimation and reflection optimization","volume":"9","author":"Zheng","year":"2020","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/LWC.2022.3145885","article-title":"Scalable channel estimation and reflection optimization for reconfigurable intelligent surface-enhanced OFDM systems","volume":"11","author":"An","year":"2022","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5766","DOI":"10.1109\/JIOT.2019.2905788","article-title":"Assistant Vehicle Localization Based on Three Collaborative Base Stations via SBL-Based Robust DOA Estimation","volume":"6","author":"Wang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_6","first-page":"8301","article-title":"Integrated sensing and communication with mmWave massive MIMO: A compressed sampling perspective","volume":"21","author":"Gao","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","article-title":"Power of deep learning for channel estimation and signal detection in OFDM systems","volume":"7","author":"Ye","year":"2018","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1109\/LCOMM.2021.3059922","article-title":"Channel estimation based on deep learning in vehicle-to-everything environments","volume":"25","author":"Pan","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/LWC.2018.2832128","article-title":"Deep learning-based channel estimation for beamspace mmWave massive MIMO systems","volume":"7","author":"He","year":"2018","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TWC.2021.3100148","article-title":"Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications","volume":"21","author":"Liu","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6490","DOI":"10.1109\/TWC.2025.3553851","article-title":"Diffusion-Driven Semantic Communication for Generative Models With Bandwidth Constraints","volume":"24","author":"Guo","year":"2025","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1109\/TWC.2025.3535714","article-title":"Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises","volume":"24","author":"Pei","year":"2025","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9223","DOI":"10.1109\/TVT.2020.3005402","article-title":"Deep denoising neural network assisted compressive channel estimation for mmWave intelligent reflecting surfaces","volume":"69","author":"Liu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6562","DOI":"10.1109\/TWC.2023.3244484","article-title":"Channelformer: Attention-based neural solution for wireless channel estimation and effective online training","volume":"22","author":"Luan","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1109\/TCOMM.2023.3343787","article-title":"Comm-Transformer: A robust deep learning-based receiver for OFDM system under TDL channel","volume":"72","author":"Xie","year":"2024","journal-title":"IEEE Trans. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3328","DOI":"10.1109\/TWC.2023.3307450","article-title":"Channel estimation by transmitting pilots from reconfigurable intelligent surface","volume":"23","author":"Zhu","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/COMST.2023.3340099","article-title":"A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks","volume":"26","author":"Zhou","year":"2024","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zayat, A., Hasabelnaby, M.A., Obeed, M., and Chaaban, A. (2024). Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities. IEEE Commun. Mag., 88\u201394.","DOI":"10.1109\/MCOM.002.2300257"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/JSTSP.2019.2925786","article-title":"Polarization Channel Estimation for Circular and Non-Circular Signals in Massive MIMO Systems","volume":"13","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tan, W., Nie, W., Wu, X., and Liu, T. (2022). Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture. Sensors, 22.","DOI":"10.3390\/s22103938"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1109\/TWC.2021.3073309","article-title":"Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems","volume":"20","author":"Mashhadi","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/1\/124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T17:22:22Z","timestamp":1768929742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/1\/124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["e28010124"],"URL":"https:\/\/doi.org\/10.3390\/e28010124","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,20]]}}}