AI/ML in 5G Beam Management 2026: Adaptive Beamforming, Rel-18 & Rel-19 Innovations
AI/ML in 5G Beam Management 2026: Adaptive Beamforming, Rel-18 & Rel-19 Innovations
At mmWave frequencies (FR2), the gNB and UE must form highly directional transmission links to secure adequate link budget — this precise alignment of transmitter and receiver beams is what beam management (BM) does. The challenge is that traditional exhaustive beam sweeping over predefined codebooks incurs high overhead, particularly with large antenna arrays. With 64–256 antenna elements in a typical massive MIMO gNB, scanning all beam pairs sequentially is slow, expensive in reference signal overhead, and power-hungry for the UE.
The legacy 3GPP procedures (Rel-15–17)
The baseline beam management framework was introduced in Rel-15 and uses three procedures defined in TS 38.331 and TS 38.213: P1 (cell/beam sweeping using SSB or CSI-RS for initial access), P2 (beam refinement within a selected beam pair), and P3 (UE receive beam adjustment). Rel-16 added L1-SINR-based beam measurement (improving on L1-RSRP alone) for better interference-aware beam selection, while Rel-17 specified a unified TCI (Transmission Configuration Indicator) framework for both downlink and uplink beam indication, reducing signaling overhead.
Enter Rel-18: AI/ML takes over
Rel-18 was the first release in which AI and ML was studied in RAN1, and the study focused on three representative use cases: CSI feedback, beam management, and positioning. 3GPP For beam management specifically, the primary objective was to apply ML-driven schemes to reduce measurement overhead — providing more flexibility for beam measurements and greater room for UE power saving — while maintaining comparable end-to-end performance to legacy beam management.
3GPP defined two concrete ML use cases under TR 38.843:
BM-Case 1 (Spatial-domain beam prediction / SBP): The ML model predicts the best DL TX beam or TX/RX beam pair at different spatial locations — useful for static or slowly moving UEs.
BM-Case 2 (Time-domain beam prediction / TBP): The model predicts the best DL TX beam or TX/RX beam pairs for future time instances — directly tackling beam tracking for mobile UEs.
In both cases, the ML model can be deployed on either side: the training and inference processes can be executed either at the gNB end or on the UE side, offering flexible deployment options. A gNB-sided model receives UE measurement reports and infers beam recommendations centrally; a UE-sided model runs inference locally and reports the predicted beam index — reducing air-interface latency.
What the ML models actually look like
Typical architectures combine a CNN input layer, LSTM layer, fully connected layer, and regression output — taking an RSRP matrix of candidate beams as input and outputting the optimal beam ID for both the transmitting and receiving ends. ResearchGate RNN-based beam-pair prediction algorithms using both beam measurements and sensor data (e.g., accelerometer, LiDAR) have also been proposed to further enhance prediction accuracy.
A key practical finding: UE speed is a crucial factor affecting the optimal time window for collecting input data — there is an inverse correlation between UE speed and window size ResearchGate, meaning a fast-moving UE needs a shorter, more recent measurement history while a pedestrian UE can average over a longer window.
Model lifecycle management (LCM) — the hard part
3GPP investigated three approaches to ensure reliable ML performance across varied conditions: model generalization (one model that works across sites), model switching (a set of scenario-specific models selected dynamically), and model update/fine-tuning (adapting model parameters when the environment changes). IEEE Communications Society This LCM framework is now actively being specified in RAN2 — the NR_AI/ML_air work item at RAN2#128 received over 210 proposals from 25+ contributors including Ericsson, Nokia, Google, and OPPO, covering activation/deactivation procedures, performance monitoring, and data collection signaling.
What's coming in Rel-19
Rel-19 is extending beam prediction to FR1 (sub-6 GHz) — not just mmWave — and to multi-TRP scenarios and efficient handover in mobility. Future prediction reports may also feed into power control and link adaptation decisions. Reinforcement learning support is also under consideration as part of the Rel-19 work item scope.
The bottom line for deployment
AI/ML beam management reduces SSB/CSI-RS sweep overhead, lowers beam failure rate, and cuts UE battery consumption in mmWave scenarios — all without changing the fundamental P1/P2/P3 procedure structure. The normative signaling hooks (reporting configuration, measurement RS resource sets) largely reuse existing Rel-15/16 CSI frameworks, making it a relatively clean upgrade path for operators already running 5G mmWave.
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