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

TL;DR

This paper introduces a deep reinforcement learning-based mechanism for dynamic waveform switching in 5G uplink, improving coverage and performance for cell-edge users by intelligently selecting between CP-OFDM and DFT-S-OFDM.

Contribution

It presents a novel DRL-based approach for real-time waveform selection in 5G uplink, optimizing user coverage and throughput without requiring extensive network modifications.

Findings

Significant performance improvement over classical methods.

Enhanced cell-edge user service quality.

Effective use of real network measurements for decision-making.

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.