telecom equipment manufacturers

Abstract: Millimeter-wave (mmWave) communication relies on directional beam sweeping for initial access, which incurs substantial latency and signaling overhead as antenna arrays grow large and deployments become denser. Machine-learning-based beam prediction can reduce this overhead, but its effectiveness depends on training data that accurately reflects the deployment environment, a requirement that is difficult to meet in practice. This paper presents a latency-aware beam management architecture that integrates digital-twin-assisted prediction, regionbased localization, weighted ensemble learning, and explainability-driven beam pruning into a unified framework. A digital twin (DT) generates site-specific ray-traced channel knowledge offline, enabling environment-aware beam prediction without exhaustive real-world measurements and supporting both initial access and beam tracking. The proposed ensemble learning scheme combines a base classifier and a specialist classifier through reliability-weighted fusion to improve prediction accuracy and robustness, while explainable artificial intelligence (XAI)-based attribution pruning reduces the beam codebook to a small subset of the most informative beams for beam allocation. To capture the distributional mismatch between the DT and real-world environments: the network was trained on imperfect digital-twin data but evaluated on real-world surrobase data. Results show that the proposed method achieves 96.80% top-1 and 98.36% top-3 beam prediction accuracy, while reducing over-the-air sounding overhead by nearly an order of magnitude relative to exhaustive sweeping, outperforming convolutional and deep neural network baselines from prior work.

Abstract: As mobile communications cross the threshold from 5G to 6G, the 3GPP has entered a decisive phase. Following the Technical Specification Group (TSG) plenary meetings of June 2026 and the associated 6G workshop in Singapore, the Release-20 study phase is now well underway across all three TSGs: Radio Access Network (RAN), Service and System Aspects (SA), and Core Network and Terminals (CT). Concurrently, the timeline for the first normative 6G specifications in Release 21 has been established. Rather than offering another technology vision, or a chronological account of committee progress, this article asks what the first body of agreed 6G material tells us. We review service requirements, system architecture, the emerging radio interface, native AI, ISAC, non-terrestrial networks (NTN), and core-network protocols, tracing how each moves from study item toward specification. Read across the three TSGs, this material also supports seven cross-cutting lessons: AI has moved from a feature to a primitive spanning every stage, non-terrestrial access has crossed from overlay to substrate, and connectivity is now one objective among three alongside computing and sensing; the industry favors deployability over redesign, with spectrum access and site reuse, rather than waveform innovation, appearing to be the tighter constraint; and standardization targets AI workflows rather than AI models, with testability across vendors recurring as a decisive criterion for which techniques survive. Because the material spans very different stages of maturity, every item cited is tagged with its status at a single snapshot date (approved, draft, individually proposed, or vendor-demonstrated), and the lessons are offered as the authors' synthesis rather than as 3GPP positions. We close with the road toward trials and commercial launch around 2030, and with the open questions these lessons help prioritize.