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Dataset for: Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication

Dataset for: Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication
Dataset for: Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication
Data supports the paper K. Satyanarayana, M. El-Hajjar, Alain Mourad and L. Hanzo Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication, in Transactions on Vehicular Technology, 2019.Harnessing the substantial bandwidth available at millimeter wave (mmWave) carrier frequencies has proved to be beneficial to accommodate a large number of users with increased data rates. However, owing to the high propagation losses observed at mmWave frequencies, directional transmission has to be employed. This necessitates efficient beam-alignment for a successful transmission. Achieving perfect beam-alignment is however challenging, especially in the scenarios when there is a rapid movement of vehicles associated with ever-changing traffic density, which is governed by the topology of roads as well as the time of the day. Therefore, in this paper, we take the approach of fingerprint based beam-alignment, where a set of beam pairs constitute the fingerprint of a given location. Furthermore, given the time-varying traffic density, we propose a multi-fingerprint based database for a given location, where the base station (BS) intelligently adapts the fingerprints with the aid of learning. Additionally, we propose multi-functional beam transmission as an application of our proposed design, where the beam-pairs that satisfy the required received signal strength (RSS) participate in increasing the spectral efficiency or improving the end-to-end performance in some other way. Explicitly, the BS leverages the plurality of beam-pairs to attain both multiplexing and diversity gains. Furthermore, if the plurality of beam-pairs is higher than the number of RF chains, the BS may also employ beam-index modulation to further improve the spectral efficiency. We demonstrate that having multiple fingerprint-based beam-alignment provides superior performance than that of the single fingerprint based beam-alignment. Furthermore, we show that our learning-aided multiple fingerprint design provides a better fidelity compared to that of the benchmark scheme also employing multiple fingerprint but dispensing with learning. Additionally, our reduced-search based learning-aided beam-alignment design performs similarly to beam-sweeping based beam-alignment, even though an exhaustive beam-search is carried out by the latter. More explicitly, our design is capable of maintaining the target performance in dense vehicular environments, while both single fingerprint and line-of-sight (LOS) based beam-alignment suffer from blockages.
University of Southampton
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Katla, Satyanarayana
f3436daa-e5da-4b3c-ab4b-ad07a0cef99a
El-Hajjar, Mohammed
3a829028-a427-4123-b885-2bab81a44b6f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Katla, Satyanarayana, El-Hajjar, Mohammed and Hanzo, Lajos (2019) Dataset for: Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication. University of Southampton doi:10.5258/SOTON/D1067 [Dataset]

Record type: Dataset

Abstract

Data supports the paper K. Satyanarayana, M. El-Hajjar, Alain Mourad and L. Hanzo Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication, in Transactions on Vehicular Technology, 2019.Harnessing the substantial bandwidth available at millimeter wave (mmWave) carrier frequencies has proved to be beneficial to accommodate a large number of users with increased data rates. However, owing to the high propagation losses observed at mmWave frequencies, directional transmission has to be employed. This necessitates efficient beam-alignment for a successful transmission. Achieving perfect beam-alignment is however challenging, especially in the scenarios when there is a rapid movement of vehicles associated with ever-changing traffic density, which is governed by the topology of roads as well as the time of the day. Therefore, in this paper, we take the approach of fingerprint based beam-alignment, where a set of beam pairs constitute the fingerprint of a given location. Furthermore, given the time-varying traffic density, we propose a multi-fingerprint based database for a given location, where the base station (BS) intelligently adapts the fingerprints with the aid of learning. Additionally, we propose multi-functional beam transmission as an application of our proposed design, where the beam-pairs that satisfy the required received signal strength (RSS) participate in increasing the spectral efficiency or improving the end-to-end performance in some other way. Explicitly, the BS leverages the plurality of beam-pairs to attain both multiplexing and diversity gains. Furthermore, if the plurality of beam-pairs is higher than the number of RF chains, the BS may also employ beam-index modulation to further improve the spectral efficiency. We demonstrate that having multiple fingerprint-based beam-alignment provides superior performance than that of the single fingerprint based beam-alignment. Furthermore, we show that our learning-aided multiple fingerprint design provides a better fidelity compared to that of the benchmark scheme also employing multiple fingerprint but dispensing with learning. Additionally, our reduced-search based learning-aided beam-alignment design performs similarly to beam-sweeping based beam-alignment, even though an exhaustive beam-search is carried out by the latter. More explicitly, our design is capable of maintaining the target performance in dense vehicular environments, while both single fingerprint and line-of-sight (LOS) based beam-alignment suffer from blockages.

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Published date: 6 September 2019

Identifiers

Local EPrints ID: 434015
URI: http://eprints.soton.ac.uk/id/eprint/434015
PURE UUID: 82928c71-bdb3-4fac-afe6-18f519910a3d
ORCID for Satyanarayana Katla: ORCID iD orcid.org/0000-0002-5411-3962
ORCID for Mohammed El-Hajjar: ORCID iD orcid.org/0000-0002-7987-1401
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 10 Sep 2019 16:30
Last modified: 11 Sep 2019 00:39

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