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Rinkesh Mittal,
Mohit Srivastava,
Pooja sahni,
Nidhi Chahal,
Preeti Bansal,
- Professor and Head, Department of electronics and communication engineering,Chandigarh Engineering,College-CGC, Punjab, India
- Dean of the Department, Department of electronics and communication engineering,Chandigarh Engineering College-CGC, Punjab, India
- Professor, Department of electronics and communication engineering,Chandigarh Engineering College-CGC, Punjab, India
- Faculty, Department of electronics and communication engineering,Chandigarh Engineering College-CGC, Punjab, India
- Assistant Professor, Department of electronics and communication engineering,Chandigarh Engineering College-CGC, Punjab, India
Abstract
The next generation of wireless communications, 6G, will be built on the convergence of artificial intelligence (AI) and advanced antenna systems. AI-driven antennas are poised to address the unprecedented requirements for data rate, reliability, adaptability, and ubiquity in future networks. An overview of current advancements in AI-enabled antenna systems for 6G networks is provided in this study. From traditional base station deployments to distributed, cell-free, and user-centric frameworks, it examines the architectural progress. Advanced beamforming methods that improve spectrum efficiency and interference mitigation—such as hybrid, dynamic, and AI-assisted beam management strategies—are given special attention. Massive MIMO and cell-free MIMO systems are also discussed, emphasizing how machine learning methods enhance mobility management, resource allocation, and channel estimation in extremely crowded and diverse situations. By integrating learning and decision-making skills straight into the physical layer, AI-powered antenna technologies bring about a paradigm shift. These intelligent systems continuously evaluate channel conditions, user mobility patterns, interference characteristics, and ambient dynamics to maximize performance in real time, as opposed to depending just on preset signal processing algorithms. Emerging 6G applications, such as immersive extended reality (XR), holographic communications, autonomous transportation systems, smart cities, remote healthcare, digital twins, and large-scale industrial automation, depend on this kind of flexibility. AI-driven antennas greatly improve spectral efficiency and network resilience by enabling adaptive waveform design, intelligent interference suppression, and predictive beam steering. This paper presents a synthesized account of recent developments, focusing on architectural evolution, beam forming techniques, massive and cell-free MIMO, integrated AI-empowered management and comparison of Privacy preserving methods.
Keywords: Artificial intelligence (AI), 6G wireless communications, AI-powered antennas, massive and cell-free MIMO, and privacy-preserving methods.
Rinkesh Mittal, Mohit Srivastava, Pooja sahni, Nidhi Chahal, Preeti Bansal. Overview AI-Driven Antenna Technologies and Privacy- Preserving Methods for Next-Generation 6G Wireless Systems. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(01):-.
Rinkesh Mittal, Mohit Srivastava, Pooja sahni, Nidhi Chahal, Preeti Bansal. Overview AI-Driven Antenna Technologies and Privacy- Preserving Methods for Next-Generation 6G Wireless Systems. Journal of Telecommunication, Switching Systems and Networks. 2026; 13(01):-. Available from: https://journals.stmjournals.com/jotssn/article=2026/view=238506
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 13 |
| 01 | |
| Received | 25/02/2026 |
| Accepted | 27/02/2026 |
| Published | 06/03/2026 |
| Publication Time | 9 Days |
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