A Machine Learning-Based Link Quality Assistance at Transport Layer for High-Frequency Networks

Operating in high-frequency bands such as mmWave and Terahertz poses challenges due to frequent variations in channel quality. These fluctuations impact the radio protocol stack, increasing latency and reducing throughput. Existing transport layer protocols need help to adapt to the high variability of link quality and network capacity, leading to the under-utilization of resources. The absence of radio link information further hinders the transport layer's ability to handle dynamic channel conditions. This paper presents Machine Learning-based Cross Layer Improvement (ML-CLI) of the transport layer, a novel solution designed to address the challenges posed by dynamic link variations in high-frequency bands. ML-CLI leverages real-time wireless network quality estimation to optimize the transport layer for an enhanced quality of service (QoS). Various ML and deep learning models for link quality prediction are evaluated, with the Artificial Neural Network (ANN) model emerging as the top-performing model, achieving an accuracy of 98.1% and an F1-score of 0.98. The integration of ML-CLI into the ns3 simulator enables the assessment of its impact on the transport layer. The results demonstrate substantial goodput and packet loss ratio improvements, with ML-CLI providing faster recovery and improved congestion control. Notably, ML-CLI achieves a 45.22% improvement in goodput and up to a 38.52% reduction in packet loss ratio compared to the traditional TCP Cubic variant.