Beam Prediction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (52 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
  23 May 2025 1 repository listed
  The model reduces overhead by approximately 93% (requiring the training of 2 ~ 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not…
• ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
  6 Jan 2025 1 repository listed
  To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.
• Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration
  6 Dec 2024 1 repository listed
  In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities.
• MVX-ViT: Multimodal Collaborative Perception for 6G V2X Network Management Decisions Using Vision Transformer.
  30 Aug 2024 1 repository listed
  Advancements in sixth-generation (6G) networks, coupled with the evolution of multimodal sensing in vehicle-to-everything (V2X) networks, have opened avenues for transformative research into multimodal-based artificial…
• Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
  21 Sep 2023 1 repository listed
  Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS.
• Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave Systems
  23 Feb 2023 1 repository listed
  Specifically, data augmentation is implemented in the data pre-processing procedure with prior knowledge and then the signal splicing strategy is proposed in the training procedure.
• Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
  18 Jan 2023 1 repository listed
  To address this challenge, we propose a novel direction that utilizes digital replicas of the physical world to reduce or even eliminate the MIMO channel acquisition overhead.
• Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
  18 May 2022 1 repository listed
  Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power.
• LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
  10 Mar 2022 1 repository listed
  A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed.
• Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration
  18 Nov 2021 1 repository listed
  This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile…
• Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
  15 Nov 2021 1 repository listed
  Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems.