Intelligent adaptive network switching system

Abstract

An intelligent, adaptive network switching system consisting of a monitoring module for capturing radio metrics, application context, and battery status in real time; a decision engine for selecting between heterogeneous mobile networks based on multi-criteria guidelines refined by machine learning; and a transition module for seamless handover to a selected target network while maintaining the user session.

Description

Application area of ​​the invention

[0001] The invention relates to mobile communication systems for heterogeneous networks, in particular device-side network selection and seamless vertical handover, optimized with respect to energy consumption, bandwidth and user experience using machine learning and cloud analytics. Background of the invention

[0002] Modern devices utilize various wireless access technologies such as LTE, 5G, and Wi-Fi. Vertical handover between heterogeneous networks requires a balanced approach to throughput, latency, cost, and energy consumption. Studies show that multi-criteria decision-making and machine learning / reinforcement learning can improve handover performance. Simultaneously, battery- and QoS-based selection reduces unnecessary handovers and conserves energy. Seamless handover mechanisms aim for uninterrupted transitions to avoid perceptible disruptions. However, many implementations lack integrated, user-definable policies that consider task context, data usage, and battery efficiency in combination with cloud-based learning.There is a need for a unified, configurable system that monitors in real time, makes decisions using ML-based guidelines, performs seamless transitions, and uses cloud analytics to reduce energy consumption and improve usability. Summary of the invention

[0003] The invention provides an intelligent, adaptive network switching system that includes a real-time monitoring module for radio metrics, data traffic, task context, and battery level; a network decision engine that uses machine learning to select the appropriate network generation based on energy and performance guidelines; a seamless transition mechanism that enables uninterrupted connectivity during handover; and user-defined settings that prioritize cost, performance, or battery life. Cloud-based analytics aggregate anonymized telemetry data to improve decision models and provide users with valuable insights.

[0004] In embodiments, the monitoring module captures parameters such as received signal strength, bandwidth, latency, jitter, packet error rate, application class, data usage, and battery status; the decision engine uses machine learning models to evaluate multi-criteria guidelines to select power-saving networks for minimal tasks and to switch to faster networks for bandwidth-intensive applications; and the transition module performs "make-before-break" or "soft handover" procedures to maintain sessions without perceptible interruption. Detailed description

[0005] A monitoring agent installed on the device collects radio metrics from available interfaces (e.g., LTE, 5G, WLAN), gathers application context information such as foreground processes and traffic classes, and reads battery level and thermal reserve. It aggregates time-limited features and calculates a utility vector that considers power consumption per bit, estimated throughput, latency requirements, and cost constraints. The agent provides a policy API for user preferences and enterprise profiles.

[0006] The network decision engine implements a multi-criteria optimizer that weights RSSI, SINR, available bandwidth, latency, jitter, historical stability, network costs, and battery consumption. Using machine learning or reinforcement learning, the engine predicts the short-term performance and energy consumption of each candidate network. It selects the target network and schedules network switching to minimize ping-pong events, taking hysteresis, dwell times, and confidence thresholds into account.

[0007] A seamless transition mechanism, if the hardware allows, supports the establishment of a secondary connection before the current connection is terminated. Otherwise, pre-authentication, buffered transmission and multipath propagation, or connection migration techniques are used to bridge temporary interruptions. Session continuity is maintained, so ongoing application flows continue without any user-perceived interruption, thus meeting the definitions of seamless handoffs.

[0008] The system prioritizes power-saving networks (e.g., LTE with reduced bandwidth or Wi-Fi in power-saving mode) for background synchronization and messaging, and switches to faster networks (e.g., 5G NR) for bandwidth-intensive tasks such as streaming or large uploads, taking battery level and thermal limits into account. When the battery is low, energy efficiency and fewer radio activations are prioritized; in performance mode, throughput and latency are the focus.

[0009] Cloud-based analytics collect anonymized device telemetry data, train population-wide models to predict network quality and energy consumption in context, and provide users with insights such as data usage trends and achieved savings. Model updates are delivered to the devices, and privacy features ensure consent, data minimization, and aggregation.

[0010] Custom settings allow for app-specific profiles, cost limits, data-saving modes, roaming rules, and schedules. Administrators can enforce profiles on company devices. The user interface provides explanations of switching decisions and their expected impact on battery and data usage.

[0011] The system integrates security and reliability features, including monitoring mechanisms for failed handover attempts, rollback to the last functioning network, and logging for diagnostic purposes. A / B testing frameworks reliably evaluate policy variations with safeguards to prevent service disruption.

[0012] Hardware and operating system integration includes access to APIs for radio metrics, selection of eSIM profiles (where applicable), and support for multipath transmission. The architecture supports plug-ins for new radio access technologies (RATs) and network slices.

[0013] During operation, the device monitors the context, evaluates potential networks, and seamlessly switches according to the guidelines. For background email synchronization when the battery is low, it remains on a power-saving connection; for a video call, it establishes a 5G connection beforehand and reroutes the data traffic without interruption, then returns to a power-saving network after the call ends.

Claims

[1] An intelligent, adaptive network switching system consisting of a monitoring module for capturing radio metrics, application context and battery status in real time; a decision engine for selecting between heterogeneous mobile networks based on multi-criteria guidelines refined by machine learning; and a transition module for seamless handover to a selected target network while maintaining the user session. [2] System according to claim 1, wherein the decision engine prioritizes power-saving networks for background processes or tasks with minimal bandwidth and faster networks for bandwidth-intensive applications, subject to user-configurable preferences and battery limitations. [3] System according to claim 1, wherein the transition module implements a make-before-break or soft handover with pre-authentication and buffered transfer and applies hysteresis and dwell time timers to reduce ping-pong effects. [4] System according to claim 1, further comprising a cloud analytics service configured to collect anonymized telemetry data, train predictive models for network performance and energy consumption, and distribute model updates and user insights to devices.