READ ME File For 'Dataset'

Dataset DOI: 10.5258/SOTON/D2250

ReadMe Author: Haochen LIU, University of Southampton

This dataset supports the publication: IEEE Transactions on Vehicular Technology
AUTHORS: Haochen Liu, Yaoyuan Zhang, Xiaoyu Zhang, Mohammed El-Hajjar, Lie-Liang Yang
TITLE: Deep Learning Assisted Adaptive Index Modulation for mmWave Communications with Channel Estimation
JOURNAL: IEEE Transactions on Vehicular Technology


This dataset contains: Figure 6, 7, 8, 9, 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 mat files.  To regenerate the results, please use the Matlab.


The figures are as follows:

   
  - Figure-6: Contains the dataset of Figure 6. BER performance comparison of OFDM mmWave systems with perfect CSI, the conventional SBL and multi-layer SBL algorithms assisted channel estimation.
  
  - Figure-7: Contains the dataset of Figure 7. MSE comparison of the conventional single-layer and the multi-layer SBL algorithms with different resolutions $N$, benchmarked against their corresponding BCRBs.

  - Figure-8: Contains the dataset of Figure 8. MSE performance comparison of OFDM mmWave systems with different channel estimation techniques. The multi-layer SBL has the resolution of $\bm{N}=[8,16]$, while for the rest cases of $N=16$.

  - Figure-9: Contains the dataset of Figure 9. The numbers of multiplications of the conventional SBL and multi-layer SBL algorithms with different resolution.

  - Figure-10: Contains the dataset of Figure 10. Probabilities for the adaptive system employing MODE$_1$, MODE$_2$ and MODE$_3$.
 
  - Figure-11: Contains the dataset of Figure 11. Throughput comparison of the conventional, \knn-based and DNN-based adaptive modulation.
 
  - Figure-12: Contains the dataset of Figure 12. BER versus $E_\mathrm{s}/N_0$ performance of the conventional, \knn-based and DNN-based adaptive modulations, as well as of the three individual MODEs.


Date of data collection: 06, 2021 ~ 06, 2022

Information about geographic location of data collection: University of Southampton

Dataset available under a CC BY 4.0 licence

Related projects:
The financial support of the Engineering and Physical Sciences Research Council (EPSRC) and the Royal Academy of Engineering  EP/P034284/1.


Date that the file was created: 06, 2022