READ ME File For 'Dataset in support of the University of Southampton Doctoral Thesis ‘Multiple Layer Channel Estimation for mmWave Communications’.

Dataset DOI: 10.5258/SOTON/D2880

ReadMe Author: Yaoyuan Zhang, University of Southampton ORCID ID:0000-0002-8126-108X

This dataset supports the thesis entitled 
AWARDED BY: Univeristy of Southampton
DATE OF AWARD: [2024]

DESCRIPTION OF THE DATA
The data is collected by using Matlab to simulate the mmWave communication systems.The data in Chapter 2 is some simulation results for mmWave propagation characteristics. The data in Chapter 3 is the simulation results for the proposed adaptive codebook channel estimation. The data in Chapter 4 is for the proposed multi-layer SBL channel estimation. The data in Chapter 5 is for the proposed multi-layer channel estimation in RIS assisted system. Each folder is named according to its chapter, where the curves of each figure are stored in fig files. To regenerate the results, please use the Matlab.

This dataset contains:
  
  - Figure-2.1: Contains the dataset of Figure. 2.1. Free space loss for different distances at mmWave frequencies.
  
  - Figure-2.2: Contains the dataset of Figure. 2.2. Free-space loss at different mmWave frequencies.

  - Figure-2.5: Contains the dataset of Figure. 2.5. The foliage attenuation variation with frequency with different foliage depths.

  - Figure-3.5: Contains the dataset of Figure. 3.5. MSE performance of channel estimation with different K and N values.
  
  - Figure-3.6: Contains the dataset of Figure. 3.6. Comparison of MSE performance of the codebook-based and SBL-based channel estimation, when Nt = 8 and Nr = 8.

  - Figure-3.7: Contains the dataset of Figure. 3.7. Achievable rates of the OFDM systems with respectively the proposed channel estimation and the conventional codebook-based channel estimation.
	
  - Figure-4.2: Contains the dataset of Figure. 4.2. The numbers of multiplications required by the single-layer SBL, multilayer SBL for different methods.
  
  - Figure-4.3: Contains the dataset of Figure. 4.3. The numbers of multiplications with different resolutions.

  - Figure-4.4: Contains the dataset of Figure. 4.4. Comparison of the angle coverage rate of the SBL algorithm, adaptive codebook, and random selection.

  - Figure-4.5: Contains the dataset of Figure. 4.5. MSE performance of different channel estimation algorithms with resolution N = 10 and N = 20 for single-layer SBL and OMP, N1 = 10, N2 = 20 for multi-layer SBL, N = 9 and N = 25 for adaptive codebook channel estimation.
  
  - Figure-4.6: Contains the dataset of Figure. 4.6. MSE performance of multi layer SBL channel estimation with different resolutions and number of layers.

  - Figure-4.7: Contains the dataset of Figure. 4.7. MSE performance of the adaptive codebook channel estimation and the AC-SBL channel estimation with a resolution of N = 16.

  - Figure-4.8: Contains the dataset of Figure. 4.8. MSE performance of channel estimation with different precoding and combining methods.

  - Figure-5.7: Contains the dataset of Figure. 5.7. Beam Patterns in Base Station for Different Sub-carriers.

  - Figure-5.8: Contains the dataset of Figure. 5.8. Beam Patterns in RIS for Different Selected Angles.

  - Figure-5.10: Contains the dataset of Figure. 5.10. Number of multiplications used in the channel estimation algorithm versus SNR when NBS = 32, N = 8, T = 10 and Nc = 5.

  - Figure-5.11: Contains the dataset of Figure. 5.11. Number of multiplications for different SBL-based algorithm versus number of Sub-carriers when NBS = 32, N = 8, SNR= 20dB and T = 10.
  
  - Figure-5.12: Contains the dataset of Figure. 5.12. Number of Selected Angles for different SNR when NBS = 32, N = 8, Nc = 5 and T = 10.

  - Figure-5.13: Contains the dataset of Figure. 5.13. Number of Selected Angles for different number of Sub-carriers when NBS = 32, N = 8, SNR= 20dB and T = 10.

  - Figure-5.14: Contains the dataset of Figure. 5.14. Angle Estimation Error for different SNR when NBS = 32, N = 8, Nc = 5 and T = 10.

  - Figure-5.15: Contains the dataset of Figure. 5.15. Angle Estimation Error for different Number of Sub-carriers when NBS = 32, N = 8, SNR= 20dB and T = 10.
  
  - Figure-5.16: Contains the dataset of Figure. 5.16. NMSE performance of different channel estimation algorithms at different SNR when NBS = 32, N = 8, Nc = 5 and T = 10.

  - Figure-5.17: Contains the dataset of Figure. 5.17. NMSE performance of different channel estimation algorithms with different Number of Sub-carriers when NBS = 32, N = 8, SNR= 20dB and T = 10.

  - Figure-5.18: Contains the dataset of Figure. 5.18. NMSE performance of different channel estimation algorithms versus the pilot overhead T when NBS = 32, N = 8, SNR= 20dB and Nc = 5.
	
Date of data collection: [2019.09-2023.09]

Information about geographic location of data collection: 

Licence:
CC-BY

Related projects/Funders:
No

Related publication:
Adaptive Codebook-Based Channel Estimation in OFDM-Aided Hybrid Beamforming mmWave Systems 
Multi-layer Sparse Bayesian Learning Channel Estimation in OFDM-MIMO mmWave Systems 
Multi-Layer Beam Training aided SBL Channel Estimation for RIS-assisted Multi-User mmWave OFDM Systems 
Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation 

Date that the file was created: 12.2023