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Autonomous Self-Trained Channel State Prediction Method for mmWave Vehicular Communications

Abidemi Orimogunje, Vukan Ninkovic, Evariste Twahirwa, Gaspard Gashema, Dejan Vukobratovic

TL;DR

This paper develops a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model.

Abstract

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.

Autonomous Self-Trained Channel State Prediction Method for mmWave Vehicular Communications

TL;DR

This paper develops a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model.

Abstract

Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.
Paper Structure (8 sections, 2 equations, 3 figures, 2 tables)

This paper contains 8 sections, 2 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. CSI Prediction Module for 5G NR mmWave Vehicular Users
  4. Performance Evaluation
  5. Simulation Setup for 5G NR mmWave Environment
  6. CSI-PM Model Training Procedure
  7. Numerical Results
  8. Conclusion

Figures (3)

  • Figure 1: System model for vehicular users in 5G NR mmWave environment
  • Figure 2: Comparison of true CSI values and predicted CSI values (single instance of 16 complex values) for dataset 250 using various input features
  • Figure 3: Comparison of true CSI values and predicted CSI values (single instance of 16 complex values) for dataset 500 using various input features