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Abbasi, âNear Field-Aware UE Localization in RIS-Aided Wireless Networks Through ML-Regressor,â in Proc. 2026 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Scie
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The work examines how machine-learning localization models transfer from simulation to measurements collected from a physical 28 GHz RIS-assisted wireless system.",highlights:["Used a 20 à 20 RIS at 28 GHz with four angularly steered, 1-bit phase profiles.","KNN achieved 0.94° angle MAE and 5 cm range MAE on the simulation test set.","Experimental validation achieved 0.5° angle error and 5.3â8.3 cm range error without retraining."],experience:"Preparing and presenting the poster gave me experience in explaining the experimental setup, learning pipeline, and angular and range localization results to a conference audience, while discussing the practical challenges of measured near-field data.",nextSteps:"The next work will extend the measurement study across more locations, angles, and dynamic scenarios to assess the robustness of the localization pipeline under changing propagation conditions.",citation:"M. T. Hassan, D. Zelenchuk, G. Travers, M. A. B. Abbasi, and G. G. Machado, âExperimental Validation of Localisation in RIS-Assisted mmWave Networks,â in Proc. 19th IEEE United Conference on Millimetre Waves and Terahertz Technologies (UCMMT), Birmingham, U.K., 2026.",image:"/photos/News/ucmmt-poster.jpeg",imageAlt:"UCMMT 2026 poster on experimental validation of RIS-assisted mmWave localization"},{slug:"ris-size-beam-sweeping-published-eucap-2026",date:"18 August 2026",dateSort:"2026-08-18",title:"RIS size and ML-enabled beam sweeping paper published at EuCAP 2026",headline:"Published at EuCAP 2026: âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localization.â",summary:"The study evaluates how RIS aperture size affects machine-learning-enabled beam sweeping and user localization performance.",category:"Publication",overview:"The paper âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localizationâ was published at EuCAP 2026. It studies how the physical size of a reconfigurable intelligent surface changes the spatial information captured during beam sweeping and the resulting machine-learning localization performance.",highlights:["Compared three RIS apertures: 10 à 10, 20 à 20, and 30 à 30 elements.","The best approach produced mean errors below 1.5 dB for the 10 à 10 aperture.","Six probing beams improved performance for larger apertures by reducing prediction outliers."],experience:"The study developed my experience in comparing RIS aperture configurations, interpreting beam-sweeping data, and connecting physical design choices with the performance of a learning-based localization model.",nextSteps:"Future work will examine wider-band, multi-user, and hardware-aware scenarios to understand how RIS size and configuration should be selected for practical localization systems.",citation:"M. T. Hassan, D. Zelenchuk, M. A. B. Abbasi, and I. Munina, âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localization,â in Proc. 20th European Conference on Antennas and Propagation (EuCAP), Dublin, Ireland, 2026.",sourceUrl:"https://doi.org/10.23919/EuCAP68105.2026.11612071",sourceLabel:"View publication DOI",image:"/photos/News/Eucap.png",imageAlt:"EuCAP 2026 presentation on RIS size and machine-learning-enabled beam sweeping"},{slug:"ris-near-field-localization-accepted-globecom-2026",date:"31 July 2026",dateSort:"2026-07-31",title:"RIS near-field localization paper accepted at IEEE GLOBECOM 2026",headline:"Accepted at IEEE GLOBECOM 2026: âRIS-Aided Near-Field mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting.â",summary:"Accepted for the RIS and Smart Environments symposium at IEEE GLOBECOM 2026 in Macau.",category:"Publication",overview:"The paper âRIS-Aided Near-Field mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprintingâ was accepted at IEEE GLOBECOM 2026. It develops a beam-domain machine-learning fingerprinting approach for user localization while accounting for cross-link interference in RIS-assisted near-field mmWave networks.",highlights:["At 28 GHz with a 20 à 20 RIS, KNN achieved 0.37° angle MAE and 4 cm range MAE under clean conditions.","Under cross-link interference, KNN recorded 1.4° angle MAE and 7.6 cm range MAE.","The results show that interference affects angle estimation more strongly than range estimation."],experience:"This research expanded my experience in modelling interference-aware localization, constructing useful beam-domain fingerprints, and evaluating how interacting wireless links affect learning-based position estimation.",nextSteps:"The next step is experimental validation with measured channels and an investigation of joint communication and sensing strategies that remain reliable under varying interference levels.",citation:"M. T. Hassan, D. Zelenchuk, and M. A. B. Abbasi, âRIS-Aided Near-Field mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting,â in Proc. 2026 IEEE Global Communications Conference (GLOBECOM), Macau, China, 2026.",sourceUrl:"https://www.linkedin.com/feed/update/urn:li:activity:7488974087323648000/",sourceLabel:"View announcement on LinkedIn"},{slug:"angular-localization-published-ict-2026",date:"12 July 2026",dateSort:"2026-07-12",title:"Learning-based angular localization paper published at IEEE ICT 2026",headline:"Published at IEEE ICT 2026: âEfficient Angular Localization in RIS-Assisted mmWave Network via Learning-Based Estimations.â",summary:"Published work on efficie
1nt learning-based angular localization for RIS-assisted millimetre-wave wireless networks.",category:"Publication",overview:"The paper âEfficient Angular Localization in RIS-Assisted mmWave Network via Learning-Based Estimationsâ was published following its presentation at IEEE ICT 2026. It explores efficient learning-based angular estimation for user localization in a RIS-assisted millimetre-wave network.",highlights:["KNN achieved 0.79° mean absolute error and an R² score of 0.99 using six probing beams.","Reduced beam-probing overhead by more than 93% compared with exhaustive search.","The resulting 1-bit RIS radiation patterns remained within 1°â2° of the ground-truth direction."],experience:"The project strengthened my experience in building and evaluating a learning-based estimation pipeline, analysing angular localization behaviour, and presenting the contribution within the wider context of intelligent wireless environments.",nextSteps:"Future work will test the estimation approach with measured data and more complex propagation conditions to assess its reliability beyond the original evaluation setting.",citation:"M. T. Hassan, D. Zelenchuk, M. A. B. Abbasi, and A. Ullah, âEfficient Angular Localization in RIS-Assisted mmWave Network via Learning-Based Estimations,â in Proc. 2026 32nd International Conference on Telecommunications (ICT), Thessaloniki, Greece, 2026.",sourceUrl:"https://www.linkedin.com/feed/update/urn:li:activity:7483837406597660672/",sourceLabel:"View announcement on LinkedIn",image:"/photos/News/ict.png",imageAlt:"IEEE ICT 2026 publication update"},{slug:"experimental-localisation-accepted-ucmmt-2026",date:"18 May 2026",dateSort:"2026-05-18",title:"Experimental RIS localization paper accepted at UCMMT 2026",headline:"Accepted at UCMMT 2026: âExperimental Validation of Localisation in RIS-Assisted mmWave Networks.â",summary:"Accepted work on experimental validation of learning-based localization in a RIS-assisted 28 GHz wireless system.",category:"Publication",overview:"The paper âExperimental Validation of Localisation in RIS-Assisted mmWave Networksâ was accepted at UCMMT 2026. It evaluates learning-based localization with measurements from a physical 28 GHz RIS-assisted setup, focusing on how models transfer from simulation to real radio hardware.",highlights:["Used a 20 à 20 RIS at 28 GHz with four angularly steered, 1-bit phase profiles.","KNN achieved 0.94° angle MAE and 5 cm range MAE on the simulation test set.","Experimental validation achieved 0.5° angle error and 5.3â8.3 cm range error without retraining."],experience:"This work gave me practical experience in connecting simulation-based research with measured wireless data, organising an experimental localization pipeline, and assessing model behaviour under real propagation and hardware conditions.",nextSteps:"The next phase will broaden the measurement campaign across additional positions and hardware configurations and investigate methods that reduce the remaining simulation-to-measurement gap.",citation:"M. T. Hassan, D. Zelenchuk, G. Travers, M. A. B. Abbasi, and G. G. Machado, âExperimental Validation of Localisation in RIS-Assisted mmWave Networks,â in Proc. 19th IEEE United Conference on Millimetre Waves and Terahertz Technologies (UCMMT), Birmingham, U.K., 2026.",sourceUrl:"https://www.linkedin.com/feed/update/urn:li:activity:7462198912255430658/",sourceLabel:"View announcement on LinkedIn",image:"/photos/News/ucmmt.png",imageAlt:"UCMMT 2026 paper acceptance announcement"},{slug:"ris-size-localization-presented-eucap-2026",date:"23 April 2026",dateSort:"2026-04-23",title:"RIS size and user localization work presented at EuCAP 2026",headline:"Presented at EuCAP 2026: âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localization.â",summary:"Presented research on the relationship between RIS size, beam-sweeping measurements, and machine-learning user localization.",category:"Talk",overview:"I presented the paper âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localizationâ at EuCAP 2026 in Dublin. The presentation explained how changing the RIS aperture affects beam-sweeping observations and the performance of a machine-learning user-localization model.",highlights:["Compared three RIS apertures: 10 à 10, 20 à 20, and 30 à 30 elements.","The best approach produced mean errors below 1.5 dB for the 10 à 10 aperture.","Six probing beams improved performance for larger apertures by reducing prediction outliers."],experience:"Presenting at EuCAP gave me experience in communicating the relationship between RIS design and localization performance, responding to technical questions, and discussing practical system choices with the antennas and propagation community.",nextSteps:"The next activity is to expand the investigation across more RIS sizes and operating conditions and connect the findings with hardware measurements for practical validation.",citation:"M. T. Hassan, D. Zelenchuk, M. A. B. Abbasi, and I. Munina, âImpact of RIS Size on Machine Learning-Enabled Beam Sweeping for User Localization,â in Proc. 20th European Conference on Antennas and Propagation (EuCAP), Dublin, Ireland, 2026.",sourceUrl:"https://www.linkedin.com/feed/update/urn:li:activity:7453116780107165696/",sourceLabel:"View presentation update on LinkedIn",image:"/photos/News/Eucap.png",imageAlt:"EuCAP 2026 presentation update"}];function h(){let e=p.slice().sort((e,t)=>
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