Intelligent and Adaptive Machine Learning-based Algorithm for Power Saving in Mobile Hotspot

In current Wi-Fi technology trend, Mobile Hotspot (MHS) or Soft Access Point (S-AP) is an integral part of our day-to-day life. At any time, MHS could be enabled as Wi-Fi Hotspot in mobility devices (smart phone, tablet) with cellular backhaul network (3G/4G/5G) and provides Internet access to client devices such as laptop, TV etc. Unlike Wi-Fi Access Point, which is typically a powered device, MHS is enabled as battery-operated device. In addition, MHS consumes higher power and reported as one of the primary Voice of Customer (VoC) issue. Due to high power consumption, many customers are skeptical about MHS feature and its continuous usage. Apart from few literatures, there is no specific IEEE 802.11 standard for MHS and its power management. In this paper, we have proposed a Machine Learning (ML) based Intelligent MHS Power Save (I-MHSPS) algorithm using Wi-Fi parameters such as RSSI, SNR, TX power and channel condition. In addition, we have used other contextual parameters such as client behavior, battery level, application usage and internet backhaul to improve the accuracy of our algorithm. In I-MHSPS, we have proposed Intelligent Transmit Power Control (I-TPC): MHS TX power regulation based on client vicinity, Intelligent Ultra Power Save (I-UPS): Applying different system power level for MHS operation and Intelligent Low Power Encryption (I-LPE): Enabling low power encryption for short range MHS. In our first experiment with I-TPC idea has reduced power consumption by 10-16% approximately when compared to existing methodologies. In second experiment for IUPS, we have applied different system power levels for MHS operation and achieved power saving around 22% without any performance degradation. Further, in third experiment, using ILPE method, we have observed the power required for encryption of data packets reduced by 20%.