READ ME File For Data for Multi-User Hybrid Beamforming Relying on Learning-Aided Link-Adaptation for mmWave Systems

Dataset DOI: 10.5258/SOTON/D0812

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




This is the dataset of the accepted paper (Feb, 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:
Hybrid beamforming (HBF) relying on a large antenna array is conceived for millimeter wave (mmWave) systems, where the beamforming (BF) gain compensates for the propagation loss experienced. The BF gain required for a successful transmission depends on the user’s distance from the base station (BS). For the geographically separated users of a multi-user mmWave system, the BF gain requirements of different users tend to be different. On the other hand, the BF gain is directly related to the number of antenna elements (AEs) of the array. Therefore, in this paper, we propose a HBF design for the downlink of multi-user mmWave systems, where the number of AEs employed at the BS for attaining BF gains per user is dependent on the user’s distance. We then propose grouping of the RF chains at the BS, where each group of RF chains serves a specific group of users depending on the nature of the channel. Furthermore, to support the escalating data rate demands, the exploitation of link-adaptation techniques constitutes a promising solution, since the rate can be maximized for each link while maintaining a specific target bit error rate (BER). However, given the time-varying nature of the wireless channel and the non-linearities of the amplifiers, especially at mmWave frequencies, the performance of conventional link adaptation relying on pre-defined threshold values degrades significantly. Therefore, we additionally propose a two-stage link adaptation scheme. Specifically, in the first stage we switch on or off both the digital precoder and the combiner depending on the nature of the channel, while in the second stage a machine-learning assisted link-adaptation is proposed, where the receiver predicts whether to request spatial multiplexing- or diversity-aided transmission from the BS for every new channel realization. We demonstrate by simulation that having
both a digital precoder and a combiner in a single dominant path scenario is redundant. Furthermore, our simulations show that the learning assisted adaptation provides significantly higher data rates than that of the conventional link-adaptation, where the reconfiguration decision is simply based on pre-defined threshold values.

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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  6, 10, 11, and 12 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-6: Contains the dataset of Figure 6. BER and achievable rate with HBF (analog and digital) and using only analog BF, when the channel has only one dominant path. 
   
  
- Figure-7: Contains the dataset of Figure 7. Average BER versus average SNR of spatial diversity and spatial multiplexing for different transmission rates. In this configuration, two spatial streams are transmitted using a 64 × 32 element MIMO with N_t^RF and N_r^RF = 2, while channel is NLOS in nature.
  
  
- Figure-10: Contains the dataset of Figure 10. Probability density functions for different classes as a function of the average SNR. Simulation parameters used are listed in Table I. (a) Learning assisted link-adaptation. (b) Conventional link-adaptation. In this setting, the channel is NLOS in nature.

  
- Figure-11: Contains the dataset of Figure 11. Capacity of the proposed design and of the conventional adaptation as a function of the average SNR — (a)NLOS (b) Only one dominant path. Simulation parameters
used are listed in Table 1.

  
- Figure-12: Contains the dataset of Figure 12. BER performance of the proposed design and of the conventional design as a function of the average SNR —(a) NLOS (b) Only one dominant path. Simulation parameters used are listed in Table 1.

 






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