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FML: Fast Machine Learning for 5G mmWave Vehicular Communications., , , , and . INFOCOM, page 1961-1969. IEEE, (2018)SCAROS: A Scalable and Robust Self-Backhauling Solution for Highly Dynamic Millimeter-Wave Networks., , , , and . IEEE J. Sel. Areas Commun., 37 (12): 2685-2698 (2019)BEAMWAVE: Cross-Layer Beamforming and Scheduling for Superimposed Transmissions in Industrial IoT mmWave Networks., , and . WiOpt, page 145-152. IFIP, (2021)Opportunistic Finite Horizon Multicasting of Erasure-Coded Data., , and . IEEE Trans. Mob. Comput., 15 (3): 705-718 (2016)mm-Wave on wheels: Practical 60 GHz vehicular communication without beam training., , , , and . COMSNETS, page 1-8. IEEE, (2017)Millimeter-wave blind spots: Mitigating deafness collisions using frame aggregation., and . INFOCOM Workshops, page 158-163. IEEE, (2016)6G Massive Radio Access Networks: Key Issues, Technologies, and Future Challenges., , , and . CoRR, (2019)HydraWave: Multi-Group Multicast Hybrid Precoding and Low-Latency Scheduling for Ubiquitous Industry 4.0 mmWave Communication., , and . CoRR, (2020)Proportional Fair Decentralized Scheduling for mmWave D2D Communications., and . WOWMOM, page 14-15. IEEE Computer Society, (2018)HydraWave: Multi-group Multicast Hybrid Precoding and Low-Latency Scheduling for Ubiquitous Industry 4.0 mmWave Communications., , and . WoWMoM, page 98-107. IEEE, (2020)