Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information

By Anum Ali, Nuria González-Prelcic, Robert W. Heath. IEEE Transactions on Wireless Communications, 2017

294 citations recorded by the source corpus.

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c1ae602a79e669144b04ea6c55d24ff526466786

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In Electrical and Electronic Engineering

Citations in the corpus

MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach (2018, background): “In [13], it was argued that there exists congruency between the channel at mmWave and sub6 GHz bands, which can be leveraged to do beam selection and channel estimation.”
Toward 6G Networks: Use Cases and Technologies (2020, background): “For example, out-of-band estimation (e.g., for the angular direction of arrival of the signal) can improve the reactiveness of beam management by mapping the omnidirectional propagation of sub-6 GHz signals to the channel estimation for mmWave frequencies [14].”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, background): “been studied for NLoS scenarios [127]–[129].”
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels (2019, methodology): “First, the solutions in [10], [17], [18] generally rely on the approach of estimating some spatial parameters, such as the angular characteristics and path gains, at the sub-6 GHz band and then leverage them at mmWave.”
Towards 6G Networks: Use Cases and Technologies (2019, background): “, for the angular direction of arrival of the signal) has been proposed to improve the reactiveness of beam management schemes, by exploiting the omnidirectional propagation of sub6 GHz signals and mapping the channel estimation to mmWave frequencies [13].”