congestion control
The advancement and ubiquity of digital networks have fundamentally transformed numerous spheres of human activity. At the heart of this phenomenon lies the Transmission Control Protocol (TCP) model, whose influence is particularly notable in the exponential growth of the Internet due to its potential ability to transmit flexibly through an advanced Congestion Control (CC). Seeking an even more efficient CC mechanism, this work proposes the construction of Deep Learning Neural Networks (MLP, LSTM, and CNN) for classifying network congestion levels.
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Existing end-to-end congestion control algorithms, in Transmission Control Protocol (TCP), use packet loss and queueing delay for congestion detection, and use static control laws to adjust the sending rate and to control the congestion. This approach presupposes that the network, and its interaction with the congestion control mechanism, is static or quasi-static. In practice, the state of the network continuously changes over time, resulting in suboptimal performance of existing algorithms.
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the measurement data simulated data of Hd-TCP and its comparisons' performance on the real high-speed railways scenario
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