{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T23:32:14Z","timestamp":1769643134853,"version":"3.49.0"},"reference-count":35,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":27,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Communications"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Ultra\u2010Wideband (UWB) is a wireless communication technology that uses Radio Frequency (RF) to transmit and receive signals between devices. Beamforming in UWB is a technique that uses multiple antennas simultaneously to focus on specific directions. In beamforming, Deep Learning (DL) techniques are applied to enhance signal processing and optimise beam pattern generation by utilising neural networks for efficient and accurate spatial filtering. However, existing DL techniques suffer from catastrophic forgetting, in which the testing data forgets previously learnt data due to the lack of knowledge distillation in other layers. Therefore, this research proposes a Regularised Hyperparameter Bilevel Optimisation with Continual Learning\u2010based Deep Neural Network (RHBO\u2010CLDNN) for beamforming in UWB systems. RHBO optimises hyperparameter efficiency at both the upper and lower levels, thereby enabling the DNN to accurately capture UWB channel characteristics, which improves channel estimation and enhances the Signal\u2010to\u2010Noise Ratio (SNR). CL is applied to dynamically adapt to changing environmental conditions without requiring complete retraining, making it suitable for real\u2010time applications. Elastic Weight Consolidation (EWC) regularisation is also applied, which mitigates catastrophic forgetting by preserving weights from learnt tasks and enables the model to adapt to channel conditions without losing previous knowledge. Experiments on the DeepMIMO dataset show that RHBO\u2010CLDNN enhances the sum\u2010rate by up to 18% and achieves an inference time of 0.025 s over Convolutional Neural Network (CNN), thereby demonstrating its suitability for real\u2010time beamforming.<\/jats:p>","DOI":"10.1049\/cmu2.70137","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:36:48Z","timestamp":1769603808000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Regularised Hyper Parameter Bi Level Optimisation With Continual Learning Based Deep Neural Network for Beamforming in Ultra\u2010Wide Band System"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3225-9562","authenticated-orcid":false,"given":"Pradeep Kumar","family":"Siddanna","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication, Nitte Meenakshi Institute of Technology Nitte (Deemed University)  Bengaluru India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3997-5070","authenticated-orcid":false,"given":"Bidare Divakarachari","family":"Parameshachari","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication, Nitte Meenakshi Institute of Technology Nitte (Deemed University)  Bengaluru India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0163-2455","authenticated-orcid":false,"given":"Dharmanna Shivappa","family":"Lamani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education  Manipal India"}]}],"member":"265","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2022.3210698"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3248168"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJAP.2024.3424209"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym14061171"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3139343"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3167020"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3155762"},{"key":"e_1_2_10_9_1","unstructured":"K. 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K.Dasari \u201cDeep Learning\u2010Based Channel Estimation and Beamforming Architecture for Massive MIMO Systems \u201dJournal of The Institution of Engineers (India): Series B(Early View) (2024) https:\/\/doi.org\/10.1007\/s40031\u2010024\u201001131\u2010x."},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1049\/mia2.12043"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2023.3245669"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3218892"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3158251"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3377539"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3151436"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJCOMS.2024.3437458"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3248961"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24196311"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2022.108495"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3261671"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3126056"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3476916"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24227153"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3139828"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAP.2024.3518064"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.4992"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prime.2024.100738"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2022.3157161"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3143372"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/s23052772"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2021.3126856"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3242716"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3283475"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3064073"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3305124"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2023.3294184"}],"container-title":["IET Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/cmu2.70137","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/cmu2.70137","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/cmu2.70137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:36:49Z","timestamp":1769603809000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/cmu2.70137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1049\/cmu2.70137"],"URL":"https:\/\/doi.org\/10.1049\/cmu2.70137","archive":["Portico"],"relation":{},"ISSN":["1751-8628","1751-8636"],"issn-type":[{"value":"1751-8628","type":"print"},{"value":"1751-8636","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70137"}}