Location-Aided Beamforming in Mobile Millimeter-Wave Networks
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Abstract (arXiv)
Due to the large bandwidth available, millimeter-Wave (mmWave) bands are considered a viable opportunity to significantly increase the data rate in cellular and wireless networks. Nevertheless, the need for beamforming and directional communication between the transmitter and the receiver increases the complexity of the channel estimation and link establishment phase. Location-aided beamforming approaches have the potential to enable fast link establishment in mmWave networks. However, these are often very sensitive to location errors. In this work, we propose a beamforming algorithm based on tracking spatial correlation of the available strong paths between the transmitter and the receiver. We show that our method is robust to uncertainty in location information, i.e., location error and can provide a reliable connection to a moving user along a trajectory. The numerical results show that our approach outperforms benchmarks on various levels of error in the location information accuracy. The gain is more prominent in high location error scenarios.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2022.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learningcited by 3
- 2025Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learningcited by 3
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamicscited by 2
- 2025Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning8 Apr 2025
- 2024Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics24 Aug 2024
- 2024Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning2 Feb 2024