Machine learning-based beamforming for integrated sensing and communication’
| dc.contributor.author | Nimnaka, H | |
| dc.contributor.author | Gayan, S | |
| dc.date.accessioned | 2026-05-06T07:32:22Z | |
| dc.description.abstract | Integrated Sensing and Communication (ISAC) is poised to be a transformative technology in future wireless networks, particularly at millimeter-wave (mmWave) and terahertz (THz) frequencies. By seamlessly integrating radar sensing with communication systems, ISAC enhances spectrum efficiency while minimizing hardware costs, size, weight, and computational demands. These advancements open new opportunities across various domains, including automotive technology, the Internet of Things (IoT), Extended Reality (XR), and robotics. | |
| dc.identifier.doi | https://doi.org/10.31705/BPRM.v5(2).2025.2 | |
| dc.identifier.issn | 2815-0082 | |
| dc.identifier.issue | 2 | |
| dc.identifier.journal | Bolgoda Plains Research Magazine | |
| dc.identifier.pgnos | pp. 11-13 | |
| dc.identifier.uri | https://dl.lib.uom.lk/handle/123/25175 | |
| dc.identifier.volume | 5 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Graduate Studies | |
| dc.title | Machine learning-based beamforming for integrated sensing and communication’ | |
| dc.type | Article-Full-text |
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