Abstract: A device may receive network data identifying an SINR, an RSSI, congestion, and throughput associated with a RAN, device data identifying latency and packet loss associated with the RAN, and an issue inference confidence score associated with a machine learning model. The device may process the network data, the device data, and the issue inference confidence score, with a model, to determine one or more issues. The device may adjust TTI bundling to generate adjusted TTI bundling, may lower a QCI or a QFI and adjust associated parameters to generate QCI/QFI adjusted parameters, may adjust slice parameters to generate adjusted slice parameters, or may adjust DRX parameters to generate adjusted DRX parameters based on the one or more issues with the RAN. The device may cause the adjusted TTI bundling, the QCI/QFI adjusted parameters, the adjusted slice parameters, or the adjusted DRX parameters to be implemented by the RAN.

Abstract: A device may receive network data identifying at least one of a signal-to-interference-plus-noise ratio (SINR), a bit error rate (BER), a packet loss, or a frame loss associated with a radio access network (RAN), and may receive model data associated with a plurality of machine learning models. The device may receive inference confidence scores associated with the plurality of machine learning models, and may process the network data, the model data, and the inference confidence scores, with a model, to select a machine learning model from the plurality of machine learning models. The device may cause the selected machine learning model to be implemented in connection with processing images.

Abstract: A device may receive network data identifying at least one of a signal-to-interference-plus-noise ratio (SINR), a bit error rate (BER), a packet loss, or a frame loss associated with a radio access network (RAN), and may receive model data associated with a plurality of machine learning models. The device may receive inference confidence scores associated with the plurality of machine learning models, and may process the network data, the model data, and the inference confidence scores, with a model, to select a machine learning model from the plurality of machine learning models. The device may cause the selected machine learning model to be implemented in connection with processing images.

Abstract: A device may receive network data identifying an SINR, an RSSI, congestion, and throughput associated with a RAN, device data identifying latency and packet loss associated with the RAN, and an issue inference confidence score associated with a machine learning model. The device may process the network data, the device data, and the issue inference confidence score, with a model, to determine one or more issues. The device may adjust TTI bundling to generate adjusted TTI bundling, may lower a QCI or a QFI and adjust associated parameters to generate QCI/QFI adjusted parameters, may adjust slice parameters to generate adjusted slice parameters, or may adjust DRX parameters to generate adjusted DRX parameters based on the one or more issues with the RAN. The device may cause the adjusted TTI bundling, the QCI/QFI adjusted parameters, the adjusted slice parameters, or the adjusted DRX parameters to be implemented by the RAN.