Prediction of Throughput Degradation from Trouble Frequencies, given Environmental Unknowns

Emergence of carrier aggregation technology to augment user throughput in LTE and 5G technologies also results in passive intermodulation (PIM) artifacts in frequency-division duplexing (FDD)-based radio transceivers. While it is imperative to suppress PIM distortions, in real time, the problem is more arduous. In practical scenarios, the transmission frequencies are unknown across telecom operators due to security concerns and dynamically changing set of frequencies. PIM detection and mitigation in the face of such environmental unknowns becomes a challenge. In this paper, we address this challenge and propose an automated solution to mitigate PIM in real time. We propose a binary search-based solution that is amenable to real-time implementation. We show through simulations that this search in tandem with a reinforcement learning based solution can dynamically mitigate and cancel PIM. Results show that the number of steps to converge to identify and mitigate the PIM in uplink frequency is reduced by a factor of ~200 (i.e., from 2500 ms to 12 ms) for around 200 combinations of DL PRB combinations.