
READ ME File For Data for Deep Learning Aided Fingerprint Based Beam Alignment for mmWave Vehicular Communication



ReadMe Author: Satyanarayana Katla, University of Southampton ORCID ID https://orcid.org/0000-0002-5411-3962




This is the dataset of the accepted paper (Sept, 2019):  K. Satyanarayana, M. El-Hajjar,  Alain Mourad  and L. Hanzo, "Multi-User Hybrid Beamforming Relying on Learning-Aided Link-Adaptation for mmWave Systems"
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Paper Abstract:
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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Project: 
The fiscal support of InterDigital as well as that of the EPSRC projects EP/Noo4558/1, EP/PO34284/1, of the Royal Society�s GRFC Grant and of the European Research Council�s Advanced Fellow Grant QuantCom.

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This DOI contains the datasets of Figures  11, 12, 13, 14 of the aforementioned paper. 
Each folder is named according to its content, where the curves of each figure are stored in text files.  
To regenerate the results, please use the Graphics Layout Engine (GLE), using the command "gle Figure.gle"

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The  embedded folders are as follows:
  
- Figure-11: Contains the dataset of Figure 11. Received signal strength (RSS) of the different schemes against traffic density.
   
  
- Figure-12: Contains the dataset of Figure 12. Average RSS values over all the beam-pairs in the fingerprint observed at the user for v<-0.7
  
  
- Figure-13: Contains the dataset of Figure 13. Probability distribution function of fingerprints dispensing with learning.

  
- Figure-14: Contains the dataset of Figure 14. Average RSS values observed at the receiver in the specifically selected beam-pairs which meet the target RSS.


 






Information about geographic location of data collection: University of Southampton, U.K.

Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/


Date that the file was created: February, 2019

