用于增强5G NR上行覆盖的AI辅助动态端口与波形切换
AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR
AI总结：
针对5G NR上行波形选择难题，提出基于深度强化学习的智能波形切换机制，利用真实网络的SNR、TA测量值优化加权吞吐量百分位数，在保障小区平均性能的同时提升边缘用户服务，性能显著优于传统方法。
摘要：
5G网络的上行链路允许在循环前缀正交频分复用（CP-OFDM）和离散傅里叶变换扩展OFDM（DFT-S-OFDM）之间选择发射波形，这对使用高频段的小区边缘用户颇具吸引力，因为后者峰均功率比更低，可实现更高发射功率。然而，DFT-S-OFDM的块差错率（BLER）更高，使得最优波形选择变得复杂。本文提出一种基于深度强化学习（DRL）的智能波形切换机制，该方案中，学习智能体旨在最大化利用真实网络中可用吞吐量百分位数构建的函数，对这些百分位数加权以在不大幅降低小区平均性能的前提下改善小区边缘用户的服务。过程中使用了真实网络中可获取的信噪比（SNR）和定时提前量（TA）的聚合测量值。结果表明，所提方案在两项指标上均远优于传统方法。
英文摘要：
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.
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