https://github.com/acyiobs/digital_twin
공식 코드Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
Papers with Code 아카이브
Beam Prediction
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The beam prediction task involves determining the optimal beam or set of beams to use for signal transmission between a base station and a user device. Beam prediction is crucial in millimeter-wave (mmWave) and massive MIMO systems, where highly directional beams are used to overcome signal attenuation and ensure high data rates. The goal is to predict the most suitable beam(s) based on environmental factors, user location, and historical signal data, without exhaustive search over all possible beams, which can be computationally intensive.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/acyiobs/lidar_beam_tracking
공식 코드LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
https://github.com/ardiantovn/gpsbeam
공식 코드GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
https://github.com/gourangc/Vision-Position-Beam-Prediction
공식 코드Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
https://github.com/itu-ai-ml-in-5g-challenge/deepsense6g_tii
공식 코드Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
https://github.com/jmoraispk/position-beam-prediction
공식 코드Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
https://github.com/omarmshaal/protobeam
공식 코드ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
https://github.com/umut-demirhan/Radar-aided-beam-prediction
공식 코드Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration
https://github.com/ghazigh/MVX
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.