Improved User Tracking in 5G Millimeter Wave Mobile Networks via Refinement Operations

The millimeter wave (mmWave) frequencies offer the availability of huge bandwidths to provide unprecedented data rates to next-generation cellular mobile terminals. However, directional mmWave links are highly susceptible to rapid channel variations and suffer from severe isotropic pathloss. To face these impairments, this paper addresses the issue of tracking the channel quality of a moving user, an essential procedure for rate prediction, efficient handover and periodic monitoring and adaptation of the user's transmission configuration. The performance of an innovative tracking scheme, in which periodic refinements of the optimal steering direction are alternated to sparser refresh events, are analyzed in terms of both achievable data rate and energy consumption, and compared to those of a state-of-the-art approach. We aim at understanding in which circumstances the proposed scheme is a valid option to provide a robust and efficient mobility management solution. We show that our procedure is particularly well suited to highly variant and unstable mmWave environments.Comment: Accepted for publication to the 16th IEEE Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), Jun. 201

Smart Beam Management for Vehicular Networks Using ML

[EN] The mmWave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmWave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this paper, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using ML algorithms, can improve both network performance and communication stability in high mobility vehicular communications.This work was supported by the Spanish Comision Interministerial de Ciencia y Tecnologia (CICYT) under projects TEC2016-78028-C3-1-P and MDM2016-O6OO, Catalan Research Group 2017 SGR 21, and Industrial Doctorate programme (2018-DI-084) of Generalitat de Catalunya.Bharath-Reddy, G.; Montero, L.; Perez-Romero, J.; Molins-Benlliure, J.; Ferrando Bataller, M.; Molina, J.; Romeu, J.... (2021). Smart Beam Management for Vehicular Networks Using ML. Íñigo Cuiñas Gómez. 1-4. http://hdl.handle.net/10251/1910661

Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification

The mmwave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmwave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this thesis, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using machine learning algorithms, can improve both network performance and communication stability in high mobility vehicular communications

A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies

The millimeter wave (mmWave) frequencies offer the availability of huge bandwidths to provide unprecedented data rates to next-generation cellular mobile terminals. However, mmWave links are highly susceptible to rapid channel variations and suffer from severe free-space pathloss and atmospheric absorption. To address these challenges, the base stations and the mobile terminals will use highly directional antennas to achieve sufficient link budget in wide area networks. The consequence is the need for precise alignment of the transmitter and the receiver beams, an operation which may increase the latency of establishing a link, and has important implications for control layer procedures, such as initial access, handover and beam tracking. This tutorial provides an overview of recently proposed measurement techniques for beam and mobility management in mmWave cellular networks, and gives insights into the design of accurate, reactive and robust control schemes suitable for a 3GPP NR cellular network. We will illustrate that the best strategy depends on the specific environment in which the nodes are deployed, and give guidelines to inform the optimal choice as a function of the system parameters.Comment: 22 pages, 19 figures, 10 tables, published in IEEE Communications Surveys and Tutorials. Please cite it as M. Giordani, M. Polese, A. Roy, D. Castor and M. Zorzi, "A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 173-196, First quarter 201