Improving BLE Coexistence: Automatic Gain Control Index Prediction Using Bagged Tree

The reliability of low-power wireless communications is compromised by the constant radio spectrum usage increase needed to support new applications. To allow reliable signal quality, more flexible countermeasures are needed to allow communication protocols despite crowded radio spectrum. This paper proposes to integrate Machine Learning (ML) in the internal processing of the Bluetooth Low Energy (BLE) radio. The objective is to predict the optimal gain of the Automatic Gain Control (AGC) index according to internal radio processing metrics produced during the previous received packets. The integration of a Bootstrap Aggregation Tree (BAgged Tree) in a simulated BLE radio shows reduction of 11% of the mean Packet Error Rate (PER) compared to the original performance.