Intelligent Scheduling with Volatile Wireless Path State Prediction for 5G+ Multi-Access Networks

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Modern 5G+ network terminals are equipped with multiple access interfaces (e.g. cellular and WiFi), enabling the implementation of multipath protocols, providing means for better network resource utilisation and better Quality of Service (QoS). This paper addresses a critical challenge in multipath network environments: the degradation of application performance and increase in out-of-order packet delivery due to the volatility of wireless paths. The paper presents a new scheduling algorithm, Best Path First (BPF), which leverages a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model. By analysing historical path data, the model predicts the path state, enabling selection of the optimal path in a multipath framework. BPF uses the path time-throughput characteristics (rolling standard variation, path bottleneck queue, path throughput) in real-time to prioritise the path with the highest expected quality, predicting with a 2% Mean Absolute Error (MAE). A Mininet testbed with real mobility traces is used to demonstrate the solution and evaluate its performance. BPF can reduce UDP end-to-end delays and jitter by 20% and 21% respectively when compared to the standard Cheapest-path-first (CPF) multipath scheduler. Tests on TCP traffic demonstrate that BPF improves by 7% and 6% TCP end-to-end delay and jitter and reduces FTP download times by 7% compared to CPF and up to 30% compared to Peekaboo.