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Sunil, M.P.
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Adaptive Beamforming Techniques for Mmwave and Thz Communications In 6G Kundra, Danish; Vamalatha, Bodireddy; Jadhav, Yogesh; Leo, L. Megalan; Hota, Sarbeswar; Sunil, M.P.; Agarwal, Trapty
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1489

Abstract

The transition to sixth-generation (6G) wireless networks focuses on fulfilling extraordinary demands for ultra-high data rates, extensive connectivity, and ultra-low latency.  Achieving these goals requires extensive spectrum resources, particularly in the emerging millimeter-wave (mmWave) and terahertz (THz) frequency bands. Unfortunately, reliable communication at these frequencies is greatly hindered by high path loss, molecular absorption, blockage, and, even more so, the growing susceptibility to loss of line of sight. To combat these issues, path adaptive beamforming methods are critical in targeting narrow beams to improve link reliability. This work focuses on complete coverage of adaptive beamforming methods applied to 6G mmWave and THz communications, covering all forms of beamforming from fully analog to hybrid and digital architectures. This also includes recent machine learning advancements in beam alignment optimization, channel estimation, user tracking, and overhead minimization. Further, the paper details these systems' performance, complexity, and energy-efficient trade-off factors while putting forth open research opportunities towards developing intelligent, resilient, and adaptive beamforming techniques for next-generation wireless systems.
Beyond 5G: Exploring AI-Driven Network Optimisation for 6G Communications Meher, Kunal; Karthikeyan, S.; Ranjan Sahu, Bharat Jyoti; Sunil, M.P.; Mishra, Smita; Singh, Amanveer; Tejesh, Kukatla
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1305

Abstract

This research consists of various features of 5G networks; the vision for 6G networks promises significant advancements, including ultra-high data rates, sub-millisecond latency, highly intelligent network operations, and exceptional device interconnectivity, among others.  Artificial Intelligence (AI) meets these requirements, which act as a fundamental base in self-organising and proactive adaptive network management. In the scope of this paper, AI integration with core 6G network functions is considered, including AI techniques such as machine learning, deep learning, federated learning, and reinforcement learning. Focus is on the AI-driven optimisation of spectrum utilisation, user experience, traffic pattern prediction, dynamic network slicing, robust QoS, and responsive QoS retention. Advancing edge computing, reconfigurable intelligent surfaces (RIS), and digital twins are also discussed. The study also discusses the lack of AI governance in 6G infrastructure, which includes data privacy, transparency of the algorithms, energy expenses, and global standardisation. This research focus reveals the highlights of the primary gaps in design and governance rationale that emerge through the lack of AI-integrated structural frameworks, resigns through the absence of a designed fabric needed to supplant the transcending potential of 6G enabled autonomous communication systems AI will irrevocably purge and define the naivety behind detonating the boundless potential AI entrenched paradigms will deliver.
Co-Authors Agarwal, Trapty Hota, Sarbeswar Jadhav, Yogesh Karthikeyan, S. Kundra, Danish Leo, L. Megalan Meher, Kunal Mishra, Smita Ranjan Sahu, Bharat Jyoti Singh, Amanveer Tejesh, Kukatla Vamalatha, Bodireddy