Differentiating Losses in Wireless Networks: A Learning Approach

Yuhao Chen, Jinyao Yan, Yuan Zhang, Karin Anna Hummel

Abstract: This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.