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A mobile device - or user equipment (UE) - using 5G can be configured to operate with large bandwidths and a large number of MIMO layers to enable high-throughput applications. This, however, comes at the cost of high power consumption. A variety of applications run at the UE with widely varying throughput requirements. For applications with low throughput requirement, over-configuration of the UE results in unnecessary power consumption. We propose to classify the traffic at the UE, and then configure the UE accordingly to meet the requirement of the current application without unnecessary power consumption. Specifically, we (i) use a machine learning (ML) based traffic classifier at the UE to detect the traffic class, (ii) find the RF parameters that are suitable for the determined traffic class in the current channel conditions, and (iii) use the UE assistance information (UAI) framework of 3GPP Release 16 to share the proposed RF parameters with the NW for UE power saving. We evaluate our proposed method using data collected in commercial networks with commercial UEs. The evaluation shows that the UE power consumption can be reduced by up to 60% in the cell-center and 50% in the cell-edge without degrading the quality-of-service (QoS).

UE Power Saving with Traffic Classification and UE Assistance