Machine-Learning-Based Collision Detection for Retransmissions

Abstract

Described herein are devices, systems, methods, and processes for improving retransmissions in wireless communication networks by distinguishing between temporal interference and longer-term radio frequency (RF) condition issues. The fact that the access point (AP) does not usually move may be leveraged, and a machine learning process can be utilized to learn and adapt to the RF conditions in the cell. The AP records various parameters for each frame received from client devices and uses this data to build a pairwise temporal matrix. Machine learning models are trained using these parameters, enabling the AP to compute the likely efficient set of modulation and coding schemes (MCSs) at each static position and along moving positions. The AP can then adapt its MCS accordingly for the downlink traffic and provide the client device with recommended MCSs for upcoming uplink transmissions. Accordingly, the retry count at the client devices can be reduced.

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