UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction

TL;DR

UplinkNet is a small, efficient neural network that accurately predicts 5G uplink throughput using Android API data, aiding in optimizing user experience for uplink-intensive applications.

Contribution

The paper introduces UplinkNet, a compact neural network model for real-time uplink throughput prediction in 5G networks using only Android API data, suitable for IoT devices.

Findings

Achieves 98.9% prediction accuracy

Maintains a model size of approximately 4,000 parameters

Outperforms existing models in real-world tests

Abstract

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.