Cross-Layer Congestion Control with Deep Neural Network in Cellular Network

Abstract: A significant fraction of data traffic is transmitted via cellular networks. When introducing fifth-generation (5G) radio access technology, the maximum bitrate of the radio link increases significantly, and the delay is lowered. Network congestion occurs when the sender attempts to send data at a higher rate than the network link or nodes can handle. In order to improve the performance of the mobile networks, many congestion control techniques and approaches have been developed over the years. Varying radio conditions in mobile networks make it challenging to indicate the occurrence of the congestion using packet loss as congestion indicator. This master thesis develops a congestion control algorithm based on Artificial Intelligence (AI) technologies, evaluates and compares it with existing state-of-the-art congestion control algorithms that are used with TCP today.In this study, we use the abundant readable physical layer information exchanged between the base stations and the user equipment to predict the available bandwidth. Two neural network models, Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM), are introduced as congestion control algorithms based on cross-layer information in order to improve user throughput and utilize the available capacity as much as possible.Evaluation in a Long-Term Evolution (LTE) network system simulator confirms that the estimation of LSTM model is able to track the varying link capacity, while MLP is less accurate and induces higher delay. The sender uses the estimated link capacity to adjust its packet sending behavior. Our evaluation reveals that for large flows, the LSTM model can attain higher throughput than state-of-the-art congestion control algorithms, which are the Google Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm and the Data Center TCP (DCTCP) algorithm. However, it has higher latency than that of these two algorithms. The MLP based model provides unstable performance compared to LSTM; its prediction is not accurate enough and has the highest latency among the algorithms.In conclusion, the LSTM does not underperform the state-of-the-art congestion control algorithms. However, it does not provide additional performance gains in current settings. The MLP model underperforms BBR and DCTCP with L4S and it is not stable enough to be used as a congestion control algorithms.