Projects

• Learning to Demap
  • Table of Contents
  • Import Libraries
    • Import Python Libraries
    • Import 5G Toolkit Modules
  • Learning to Demap the Symbols
    • Input Output Mapping for M = 4
    • Input Output Mapping for M = 6
    • Input Output Mapping for M = 8
  • Throughput and BER Performance of LLRnet
    • Import Libraries
    • Simulation Parameters
    • PDSCH Parameters
    • LLRnet Parameters
      • Training Framework
      • Deployment Framework
  • Simulation Section
  • Performance Evaluation
    • Throughput vs SNR (dB) for 16-QAM, 64-QAM and, 256-QAM.
      • Throughput vs SNR (dB) for 16-QAM.
      • Throughput vs SNR (dB) for 64-QAM.
      • Throughput vs SNR (dB) for 256-QAM.
    • Bit Error rate (BER) vs SNR (dB) for 16-QAM, 64-QAM and, 256-QAM.
      • BER vs SNR for 64-QAM
      • BER vs SNR for 256-QAM
    • Block Error Rate (BLER) vs SNR (dB) for 16-QAM, 64-QAM and, 256-QAM.
      • BLER vs SNR for 16-QAM
      • BLER vs SNR for 64-QAM
      • BLER vs SNR for 256-QAM
    • Complexity Analysis
  • Conclusion
    • Positives of the LLRnet:
    • Limitations of the LLRnet:
  • References:
• Imitation Learning for Position Estimation
• Channel Interpolation based on SRCNN and DnCNN
• Comparative Study of Reed Muller codes, Polar Codes and LDPC codes
• Channel Quality Estimation in 5G and Beyond Networks
• Hybrid Automatic repeat Request in 5G and Beyond
• Constellation Learning in an AWGN Channel
  • PHY layer as AutoEncoder
  • Steps
    • Importing Libraries
    • Parameters of AutoEncoder
    • Training Data
    • Testing Data
    • Normalization Functions
    • Defining AutoEncoder Model
    • Training AutoEncoder
    • Defining Tx, Channel and Rx from Trained AutoEncoder
    • Block Error Rate (BLER) performance
    • Hamming Codes
  • Importing necessary libraries
  • Transmitter
  • BLER plot : comparison of AutoEncoder BLER with base line (n,k) Hamming Code BLER
    • Constellation Learning
  • learned constellation plot
    • References
• Downlink Synchronization using SSB in 5G systems
• Uplink Synchronization using PRACH in 5G systems
• Downlink TDoA Based Positioning for Millimeter Wave 5G Networks Deployed in Indoor Factory
  • Import Libraries
    • Python Libraries
    • 5G Toolkit Libraries
  • Simulation Parameters
  • Channel Generation
    • Channel Parameters:
  • Position Reference Signal
  • OFDM Transmitter: Create Transmission Grid
    • Display Transmission Grid
  • Transmit Beamforming
  • Pass the Beamformed Grid Through Wireless Channel
  • Add Noise
    • Display Received Resource Grid
  • Extracting the Resource Grid
  • Channel Estimation + Interpolation
    • Display the quality of Channel Estimates
  • ToA Estimation
    • Visualization: Time of Arrival locus Circles
  • Position Estimation + K-Best Measurement Selection (Genie Aided)
    • Measurement Selection:
    • Visualization of Positioning
  • Performance Analysis of Positioning Error for ToA based method
  • Further Study