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AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR

Alejandro Villena-Rodríguez, Gerardo Gómez, Mari Carmen Aguayo-Torres, Francisco J. Martín-Vega, José Outes-Carnero, F. Yak Ng-Molina, Juan Ramiro-Moreno

TL;DR

An intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions is proposed and achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput.

Abstract

The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM), which is appealing for cell-edge users using high-frequency bands, since it shows a smaller peak-to-average power ratio, and allows a higher transmit power. Nevertheless, DFT-S-OFDM exhibits a higher block error rate (BLER) which complicates an optimal waveform selection. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL). In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users' service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. Results show that our proposed scheme greatly outperforms both metrics compared to classical approaches.

AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR

TL;DR

An intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions is proposed and achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput.

Abstract

The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM), which is appealing for cell-edge users using high-frequency bands, since it shows a smaller peak-to-average power ratio, and allows a higher transmit power. Nevertheless, DFT-S-OFDM exhibits a higher block error rate (BLER) which complicates an optimal waveform selection. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL). In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users' service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. Results show that our proposed scheme greatly outperforms both metrics compared to classical approaches.
Paper Structure (10 sections, 6 equations, 3 figures, 4 tables, 1 algorithm)

This paper contains 10 sections, 6 equations, 3 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Dynamic Port and Waveform Switching
  4. Proposed Deep Reinforcement Learning Dynamic Switching
  5. States, actions, and reward
  6. States
  7. Actions
  8. Reward
  9. Simulation results
  10. Conclusions

Figures (3)

  • Figure 1: Digital base-band transmitter structure of CP-OFDM.
  • Figure 2: Digital base-band transmitter structure of DFT-S-OFDM.
  • Figure 3: Achieved throughput with both fixed waveforms and with AI-assisted switching for: (a) low percentiles; and (b) high percentiles.