Enhancing Wi-Fi 7: Traffic Flow Intelligence and Multi-Link Operation for Optimal Efficiency - Neha Dawar; Khuong N. Nguyen; Abhishek Sehgal; Yuming Zhu; Boon Loong Ng; Junsu Choi (IEEE Access)
As the proliferation of Wi-Fi-enabled devices continues to accelerate, there is a pressing need for efficient wireless connectivity solutions. To address this challenge, the introduction of Multi-Link Operation (MLO) in Wi-Fi 7 (IEEE 802.11be) enables clients to establish connections with access points across multiple frequency bands, thereby enhancing throughput and reducing latency at the expense of increased power consumption. To balance the benefits of MLO with its potential impact on smartphone battery life and user experience, we propose an innovative approach that integrates machine learning-based network service detection for Traffic Flow Intelligence with intelligent link adaptation for MLO. Our solution utilizes machine learning to detect latency-sensitive applications with up to 99% accuracy and optimizes link settings based on the Quality of Experience (QoE) of the user relative to the application, detected by our QoE Monitoring Classifier with 99.7% recall for bad QoE and 88% accuracy overall. Notably, our approach enables users to enjoy up to 40 minutes more of high-quality video calls compared to an always-on MLO configuration on the Samsung Galaxy S24 Ultra.