Position-Assisted Beam Prediction

Updated 30 November 2025

Position-assisted beam prediction is a method that uses spatial data (e.g., GPS, maps) to estimate the optimal beam index for wireless communication.
It integrates diverse modalities, including visual and geospatial features, to drastically reduce beam training overhead and latency in dynamic environments.
The approach employs various machine learning models—from MLPs to Transformers—to efficiently map physical positions to optimal communication beams.

Position-assisted beam prediction refers to the family of algorithms and system designs in which user or base station positions—possibly augmented with additional spatial, visual, or map-derived features—are used as primary side information to predict optimal beam indices in directional wireless communication systems. This paradigm fundamentally changes the conventional beam training workflow by leveraging the geometric correlation between physical location and the propagation-optimal beam, thus enabling a drastic reduction in overhead, latency, and training resource consumption. The technique has been rigorously developed and validated for millimeter-wave (mmWave), THz, and sub-6GHz MIMO/ISAC systems, and is increasingly critical as beam codebooks scale and as mobility (vehicular, drone, or user equipment) becomes the norm in 5G/6G deployments.

1. Underlying Principles and Problem Formulation

Conventional beam alignment in mmWave/massive MIMO involves exhaustive or hierarchical scanning over large codebooks to identify the beamforming vector (or pair, in MIMO) that maximizes link metrics such as received power or SNR. Position-assisted beam prediction departs from this by learning or exploiting the statistical (or physical) mapping: $\mathbf{x}_\text{pos} \longmapsto b^*$ where $\mathbf{x}_\text{pos}$ characterizes the user (or transmitter/receiver pair) spatial state (typically 2D/3D coordinates, sometimes enhanced with additional features), and $b^*$ indexes the codebook beam (or pair) that optimizes the desired performance.

Implementations include both explicit geometric models for LoS regimes and data-driven mappings for multipath-rich or NLoS conditions. The approach is multi-modal: besides GPS or geodetic position, inputs can include map tiles, aerial images, LiDAR, camera frames, or environment tokens (Jaensch et al., 20 Oct 2025, Zhao et al., 6 Jun 2025).

Key advantages:

Orders-of-magnitude reduction in beam training overhead (i.e., number of codewords tested per alignment event).
Predictive, real-time operation for high-mobility scenarios (vehicle, drone, moving base stations).
Ability to generalize to unseen environments given sufficient diversity in input features (Jaensch et al., 20 Oct 2025).

2. Input Representations and Feature Engineering

The fidelity of position-assisted beam prediction is directly linked to the informativeness of the input feature set.

Raw Geodetic Coordinates: Most basic, e.g., 2D latitude/longitude, possibly min-max normalized to 0,1. Accuracy is limited in multipath-rich or NLoS scenarios.
Augmented Geographic Features: Inclusion of altitude, UE-to-BS distance vectors, ECEF (Earth-centered, Earth-fixed) frame representations, or normalized direction vectors (Nugroho et al., 23 May 2025). These allow for more robust geometry-aware prediction, especially in UAV or 3D deployments.
Geospatial Tensors or Maps: Stacking of multi-channel tensors encoding building height maps (nDSM), vegetation height, aerial RGB imagery, one-hot BS position maps, distance-to-BS, and azimuth maps is a powerful paradigm enabling generalization across previously unseen city tiles (Jaensch et al., 20 Oct 2025).
Multi-modal Fusion: Visual data (RGB camera feeds or multi-view images (Zhao et al., 6 Jun 2025, Charan et al., 2022)) combined with position tokens (pre-tokenized and embedded as text, (Zhao et al., 6 Jun 2025)) or classic GPS form a rich context for beam prediction in dynamic, occlusion-prone, or ambiguous spatial contexts (Charan et al., 2021).
Temporal Sequences: For predictive (future) beam selection, sequence models ingest trajectories over historical position/beam records (Nugroho et al., 23 May 2025, Abuzainab et al., 2021).

Input preprocessing might include normalization (zero-mean/unit-variance or min-max scaling), coordinate transformation (to ECEF), tokenization, and data augmentation (flipping/rotation for map tiles as in (Jaensch et al., 20 Oct 2025)).

3. Modeling Approaches and Architectures

The position-to-beam mapping is modeled via a range of classic and modern machine learning architectures, with complexity and generalization power tailored to both accuracy and inference efficiency:

Feedforward Neural Networks (FNN/MLP): Input layer receives normalized coordinates (optionally augmented), often with 2–3 hidden layers of ReLU units, softmax output for beam indices. Example: 3×256-layer MLP for urban vehicular mmWave (Morais et al., 2022), achieving robust prediction vs. lookup-table or KNN baselines.
U-Net and Convolutional Neural Networks: Fully convolutional encoder-decoder architectures (e.g., U-Net with dilated convolutions) operating on spatial geospatial tensors for large-scale, simultaneous beam probability mapping (Jaensch et al., 20 Oct 2025). This architecture allows per-pixel softmax predictions for all user locations in one forward pass, with HW-optimized complexity.
Autoencoders: Lightweight undercomplete autoencoder MLPs (three hidden layers, as in LAE-424 or LAE-636) dramatically reduce parameter count (by >83%) with negligible accuracy loss, enabling deployment on resource-constrained hardware (El-Banna et al., 23 Nov 2025).
Probabilistic/Bayesian Models: Low-rank tensor probability mass function (PMF) models with latent state decompositions, estimated via variational inference or EM, allow interpretable and sample-efficient learning; crucial for applications requiring compactness or uncertainty quantification (Chege et al., 7 Apr 2025).
Hybrid Tensor Completion: Data-driven noisy tensor completion exploits both spatial smoothness and low-rank beam dependence to impute received-power predictions, with associated beam-selection strategies tailored to noisy or limited coverage measurement regimes (Chou et al., 2020).
Recurrent Neural Networks (RNN/GRU): For sequential or trajectory-aware prediction (e.g., drones, handoff in RIS-assisted THz), stacked GRUs process past position and beam sequences to predict future beam and link indices. Parallel classifiers can jointly predict beam and handoff (Abuzainab et al., 2021).
Multi-modal Transformers and LLMs: Vision tokens and text-based positional encoding are jointly fused in large pre-trained LLMs or Vision Transformers, often requiring adapters (LoRA) for parameter-efficient tuning (Zhao et al., 6 Jun 2025, Zheng et al., 13 Mar 2025). These models exhibit superior few-shot generalization and cross-modal reasoning.

Each architecture is matched to specific latency, memory footprint, and scenario generalization constraints.

4. Training Procedures and Data Set Construction

Model training for position-assisted beam prediction requires:

Supervised Data: Samples of position features (per chosen representation) paired with ground truth beam indices—optimal under a codebook for received power, often found by exhaustive search or ray tracing (Jaensch et al., 20 Oct 2025, Nugroho et al., 23 May 2025, Zhao et al., 6 Jun 2025).
Label Generation: One-hot target encoding for best beam(s), with top-k sets retained for reduced-overhead analysis. In some datasets, down-sampling or balancing across beam indices is required for fair split (Nugroho et al., 23 May 2025).
Loss Functions: Cross-entropy loss over predicted softmax output; in probabilistic or regression settings, auxiliary losses for path existence (binary cross-entropy), RSRP mean absolute error (MAE), or per-beam regression components may be included (Guo et al., 9 Aug 2025).
Optimization: Adaptive optimizers (Adam), batch sizes typical of modern DL, learning-rate scheduling, early stopping, and, for LLMs or ViTs, low-rank adapters for practical fine-tuning (Jaensch et al., 20 Oct 2025, Zhao et al., 6 Jun 2025).
Data Augmentation: Random geometric augmentation for map/imagery-based models is usually limited by environment awareness of the data (Jaensch et al., 20 Oct 2025). For camera-based or computer vision components, image normalization and classic augmentation protocols (crop, flip, color jitter) apply (Charan et al., 2022).
Pretrain-and-Calibrate: Many hybrid approaches pretrain networks on ray-tracing-generated "synthetic" (site-specific) data, followed by on-site calibration using sparse real measurements for fast domain adaptation (Guo et al., 9 Aug 2025).
Online/Incremental Learning: For tensor-completion workflows, online warm-start reduces recomputation overhead when updating with new measurements (Chou et al., 2020).

5. Evaluation Metrics and Quantitative Performance

Performance is assessed with beam selection and link quality metrics:

Top- $k$ Accuracy ( $\mathrm{Acc}_k$ ): Fraction of test positions for which the correct beam index is among the top $k$ $k$ predicted candidates. State-of-the-art models consistently report:
- Top-1 accuracy: 59–98% (vision-position LLM in simulation: 98.1%; vision-MLP in real vehicular: 75.9%; MLP GPS-only: 39–61%) (Jaensch et al., 20 Oct 2025, Zhao et al., 6 Jun 2025, Morais et al., 2022, Charan et al., 2021).
- Top-3/Top-5: nearly 97–100% across modalities and environments (Jaensch et al., 20 Oct 2025, Zheng et al., 13 Mar 2025).
Throughput Ratio (TPR): Fraction of ideal achievable throughput when sweeping only predicted candidates vs. all codebook beams; e.g., top-4/8 yields TPR >0.99 (Jaensch et al., 20 Oct 2025).
Power Loss ( $P_L$ ): Mean or percentile (e.g., 95%) of actual vs. oracle beam; position-assisted approaches yield $<3$ dB in real data at $>90\%$ overhead savings, and $<0.6$ dB for UAV/trajectory settings (Morais et al., 2022, Nugroho et al., 23 May 2025).
Overhead Savings: Beam-training reduction from $B$ (exhaustive) to $k$ (top set); often $80\%$ –95% (Jaensch et al., 20 Oct 2025, Nugroho et al., 23 May 2025, Charan et al., 2021).
Model Size and Inference Speed: LAE autoencoders reduce parameters and MACs by $>83\%$ with near-lossless accuracy (El-Banna et al., 23 Nov 2025). CNN/U-net models predict beam maps over a $64\times64$ grid in $\sim$ tens of ms (Jaensch et al., 20 Oct 2025). LLM/ViT multimodal models exhibit greater latency (10x LSTM) but strong few-shot generalization (Zheng et al., 13 Mar 2025, Zhao et al., 6 Jun 2025).
Generalization and Robustness: Models trained on diverse urban/aerial tiles transfer to unseen map tiles without retraining (Jaensch et al., 20 Oct 2025). Position error degrades performance gracefully; validity at $<$ 3m GPS error for $>90\%$ reliability (Morais et al., 2022).
Comparison with Baselines: Probabilistic PMF models outperform deep NNs and fingerprinting with far fewer parameters and require half as many training samples for $>90\%$ alignment (Chege et al., 7 Apr 2025).

6. Practical Design Guidelines and Deployment Considerations

Empirical and analytical findings yield several critical principles for deployment:

Input Feature Selection: Input encoding should be rich enough to distinguish LoS, NLoS, and ambiguous geometries (e.g., by stacking height maps, azimuth, distance, and visual/semantic features) (Jaensch et al., 20 Oct 2025, Zhao et al., 6 Jun 2025).
Model Complexity: Selection trades off resource constraints (MACs, parameters) with accuracy. For mobile or embedded deployment, LAE, MLP, or low-rank Bayesian PMF models are preferred (El-Banna et al., 23 Nov 2025, Chege et al., 7 Apr 2025). For site-agnostic, multi-environment, or few-shot generalization, large-scale transformers or LLMs with multi-modal input are optimal (Zhao et al., 6 Jun 2025, Zheng et al., 13 Mar 2025).
Error Mitigation: Design for robustness to position uncertainty; grouping-based selection or smoothing across spatial bins can recover accuracy under moderate GPS error (Chou et al., 2020).
Dynamic and Multi-BS Scenarios: Models should incorporate ability to update beam maps in real-time (e.g., with digital twins, LiDAR, or moving sensors), or handle joint predictions for multiple base stations and frequency bands (Jaensch et al., 20 Oct 2025).
Latency and Signaling Overhead: Position-aided prediction reduces both channel training and control signaling by up to 10× (or more), often requiring only two short messages per event versus hundreds for exhaustive search (Alexandropoulos, 2017).
Scaling and Storage: Model storage can be minimized with blackbox-whitebox or Bayesian methods; Transformer-based models can be pruned or quantized for real-time use if necessary.

7. Extensions, Limitations, and Future Directions

Several developments and open challenges remain:

Temporal/Trajectory-Aware Prediction: RNN/GRU and sequence-to-sequence models enable future beam prediction for mobility-critical scenarios, as in UAV tracking or RIS-assisted drone handoff (Nugroho et al., 23 May 2025, Abuzainab et al., 2021).
Multi-modal and Cross-modal Fusion: Combining visual, positional, LiDAR, and possibly inertial sensors improves accuracy and robustness in adverse or NLoS situations; LLMs and ViTs adapted via cross-attention enable high-level semantic reasoning across modalities (Zhao et al., 6 Jun 2025, Zheng et al., 13 Mar 2025).
Downstream Integration with Sensing/ISAC: Autoencoder- and U-Net-based beam predictors with position-channel fusion will be integral to ISAC (integrated sensing and communication) systems in 6G (El-Banna et al., 23 Nov 2025).
Adaptation to Structure and Environmental Variation: Current models may be environment-specific or limited in extreme morphologies; generalization requires sufficient input diversity, regular pre-training/calibration on synthetic/real datasets, and possibly online/federated learning (Jaensch et al., 20 Oct 2025).
Real-world Constraints: Practical deployment must address privacy (especially with camera data), synchronization, calibration of sensor fusion (spatial alignment between cameras and antenna arrays), and resource constraints for inference and adaptation (Charan et al., 2021).

Position-assisted beam prediction is now mature, with validated accuracy, latency, and overhead gains, and a breadth of implementation modalities. It is expected to form the backbone of next-generation high-frequency, high-mobility communications and ISAC networks, as evidenced by the convergence of the geospatial mapping, machine learning, and wireless communication communities (Jaensch et al., 20 Oct 2025, Zhao et al., 6 Jun 2025, El-Banna et al., 23 Nov 2025, Chou et al., 2020).