While 5G New Radio (NR) networks offer significant uplink throughput improvements, these gains are primarily realized when User Equipment (UE) connects to high-frequency millimeter wave (mmWave) bands. The growing demand for uplink-intensive applications, such as real-time UHD 4K/8K video streaming and Virtual Reality (VR)/Augmented Reality (AR) content, highlights the need for accurate uplink throughput prediction to optimize user Quality of Experience (QoE). In this paper, we introduce UplinkNet, a compact neural network designed to predict future uplink throughput using past throughput and RF parameters available through the Android API. With a model size limited to approximately 4,000 parameters, UplinkNet is suitable for IoT and low-power devices. The network was trained on real-world drive test data from commercial 5G Standalone (SA) networks in Tokyo, Japan, and Bangkok, Thailand, across various mobility conditions. To ensure practical implementation, the model uses only Android API data and was evaluated on unseen data against other models. Results show that UplinkNet achieves an average prediction accuracy of 98.9% and an RMSE of 5.22 Mbps, outperforming all other models while maintaining a compact size and low computational cost.

翻译：尽管5G新空口（NR）网络显著提升了上行链路吞吐量，但这些增益主要在用户设备（UE）连接到高频毫米波（mmWave）频段时实现。随着实时超高清4K/8K视频流、虚拟现实（VR）/增强现实（AR）内容等上行密集型应用需求的增长，准确预测上行链路吞吐量以优化用户体验质量（QoE）变得至关重要。本文提出UplinkNet，一种紧凑型神经网络，旨在利用Android API提供的过往吞吐量和射频参数预测未来上行链路吞吐量。该模型参数量限制在约4,000个，适用于物联网和低功耗设备。网络基于日本东京和泰国曼谷商用5G独立组网（SA）的实际路测数据进行训练，覆盖多种移动场景。为确保实际部署可行性，模型仅使用Android API数据，并在未见数据上与其他模型进行对比评估。结果表明，UplinkNet平均预测准确率达98.9%，均方根误差为5.22 Mbps，在保持紧凑结构和低计算成本的同时，性能优于所有对比模型。