MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach
By Yuyang Wang, Murali Narasimha, Robert W. Heath. 2018
95 citations recorded by the source corpus.
Millimeter-wave communication is a challenge in the highly mobile vehicular context. Traditional beam training is inadequate in satisfying low overheads and latency. In this paper, we propose to combine machine learning tools and situational awareness to learn the beam information (power, optimal beam index, etc) from past observations. We consider forms of situational awareness that are specific to the vehicular setting including the locations of the receiver and the surrounding vehicles. We leverage regression models to predict the received power with different beam power quantizations. The result shows that situational awareness can largely improve the prediction accuracy and the model can achieve throughput with little performance loss with almost zero overhead.
Subfields: Electrical and Electronic Engineering
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In Electrical and Electronic Engineering
- Medhi Bennis
- Merouane Debbah
- Sumudu Samarakoon
- Jihong Park
- Chen-Feng Liu
- Mohammed S. Elbamby
- Cristina Perfecto
- Ahmed Alkhateeb
Citations in the corpus
- Wireless Network Intelligence at the Edge (2018, methodology): “To overcome mmWave’s link sensitivity to blockage [23], a GAN-aided long-term channel estimation [24] and a reinforcement learning (RL)-based beam alignment technique [25] were proposed.”
- Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “Edge Machine Learning : Inference (or prediction) capabilities with low latency is one of the main reason for ML to be popular in MEC as well as several other communication applications such as coding, beamforming, resource optimization, caching, scheduling, routing, and security [43]–[46].”
- DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications (2019, background): “Examples include predicting the beamforming and channel matrices based on the user RF signature [11], [12], or based on the user location [14], [22], [23] and predicting the future blockage based on the sequence of previously selected beams [15].”