Exploiting the vacant spectrum resource at mmWave bands provides the potential for fulfilling the requirements of broadband services. Nevertheless, the sensitivity of mmWave to blockages together with its directionality bring new technical challenges in the vehicular context. In particular, traditional beam training is inadequate in satisfying low communication overhead and small latency, and the influence of the same blockage on a fixed position varies according to vehicle motion. To facilitate fast beam alignment, fingerprint-based method stands out as an efficient solution, where the utilities of selecting different beam pairs are recorded in the fingerprint database for reference at a given position. In order to better orchestrate fingerprint-based method with vehicular scenarios, we propose the idea of trajectory-aware fingerprint, which extracts the combined effect of mobility, propagation environment, and blockages, so as to faithfully reflect the transmitting condition in mobile scenarios. Then, a light-weight machine learning framework is established for intelligent adaptation among multiple fingerprints to find a near-optimal BS association and beam alignment solution. Finally, the simulation result verifies the performance improvements.
Light-weight Machine Learning for mmWave Vehicular Communications
2020-11-01
1359633 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
mmWave massive MIMO vehicular communications
TIBKAT | 2023
|Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks
BASE | 2019
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