SIGNAL STRENGTH-BASED BATTERY LIFE OPTIMIZATION SYSTEM

Abstract

The invention relates to a system that uses a multi-layered, adaptive, and learning approach to optimize the battery life of mobile devices based on signal strength conditions. The system continuously analyzes real-time signal strength measurements, network connectivity status, user activity profiles, and environmental factors to optimize the power consumption parameters of user equipment. This minimizes energy waste caused by unnecessary network search operations, high power transmission levels, and failed connection attempts in areas with weak signals, while also preventing unnecessary energy consumption in areas with strong signals.

Description

1 TARIFF SIGNAL STRENGTH-BASED BATTERY LIFE OPTIMIZATION SYSTEM Technical Area 5 The invention relates to the battery of user equipment (UE) in mobile communication networks. to be used in order to optimize its lifespan and increase energy efficiency It is related to the system. The invention is particularly relevant to LTE (Long Term Evolution), 5G NR (New Radio), and next-generation mobile technologies. In network technologies, device power consumption is determined based on signal strength and quality metrics. A system developed for the dynamic and intelligent management of modes. It offers. State of the Art Fixed Power Management Strategies Power saving modes on current mobile devices are usually manually activated by the user. It is enabled as such or works with simple algorithms based on fixed threshold values. This 20 The approach incorporates dynamic signaling conditions, variable network topology, and real-time usage. They don't take these scenarios into account enough. Users often don't use power saving mode. You don't know when to enable or disable it, which can be unnecessary. This leads to increased energy consumption or performance loss. Lack of Signal Strength Awareness Traditional power management systems measure signal strength and quality metrics as power consumption. They do not use it as a central factor in their decisions. However, in areas with weak signals, devices It maximizes transmission power to maintain network connectivity, more frequently. It performs a cell search and conducts repeated connection attempts. These processes take 30 minutes. This results in 200-400% more energy consumption compared to normal signal conditions. Lack of Contextual Adaptation Current systems detect the user's current activity status (active data transfer, idle) (waiting, phone call, background synchronization), application requirements and 35 2 It does not adequately consider network usage patterns in power management decisions. For example, Implementing aggressive power saving measures during an active video call improves service quality. It lowers it significantly. Predictive Optimization Deficiency 5 Historical signal strength data, location-based signal maps, and user mobility. These patterns are not used to predict future power consumption needs. The absence of proactive power management strategies results in reactive responses to sudden signal changes. This leads to the release of new gas and a decrease in energy efficiency during these transition periods. Ignoring Network Signaling Load In areas with weak signals, devices often use RRC (Radio Resource Control) connectors. It enters setup / termination loops, performs track area update operations, and processes cells. It carries out the re-election procedures. This signaling traffic both devices It drains the battery and unnecessarily occupies network resources. Current 15 intelligent mechanisms to minimize this signaling load in systems It is not available. Lack of Optimization in Multiple RAT (Radio Access Technology) Scenarios Modern devices support multiple RATs (2G / 3G / 4G / 5G, Wi-Fi, Bluetooth). However, this 20 Transitions between technologies are optimized in terms of signal strength and energy efficiency. This has not been done. For example, it automatically switches to a strong Wi-Fi network when there is a weak LTE signal. This approach can significantly extend battery life, but this type of smart RAT selection is important. These mechanisms are not common. Static Structure of Transmission Power Control Uplink transmission power control mechanisms are generally controlled by the network and The device's battery status, user preferences, or application priorities are not taken into account. For a device with a critical battery level, lower transmission power profiles are used. Service continuity can be improved, but such adaptive strategies are not currently available. 30 The current patent, numbered US9615333B2, only covers battery percentage and processor load. or reactively intervenes in screen brightness according to application usage habits. It offers top-level optimizations such as background restrictions. But the reality is... time-based signal metrics, location-based heat map, or machine learning prediction 35 3 It does not take its algorithms into account. Therefore, the device experiences drops in signal quality. We are unable to proactively predict before entering a weak coverage area and the radio layer It is unable to dynamically optimize its parameters using a context-sensitive approach. In conclusion, due to the negative aspects described above and the current solutions, topic 5 Due to its shortcomings, an improvement is needed in the relevant technical field. It has been observed. Purpose of the Invention The system described in the invention provides real-time signal strength measurements (RSRP - Reference Signal). Received Power, RSRQ - Reference Signal Received Quality, RSSI - Received Signal Strength Indicator (GRI) measures network connection status, user activity profile, and environmental factors. By continuously analyzing, it automatically adjusts the power consumption parameters of the user equipment. It optimizes the system. This allows for fewer unnecessary network search operations in weak signal areas. energy caused by high power transmission levels and failed connection attempts While minimizing waste, unnecessary energy consumption is also prevented in areas with strong signals. The invention relates to smartphones, tablet computers, IoT (Internet of Things) devices, and M2M. Machine-to-machine communication modules, wearable technology products, and mobile modems 20 It can be used in a wide range of devices, especially where battery capacity is critical. in the scenarios it carries out (long-term field work, emergency communication, remote monitoring These systems (low-power IoT applications) offer significant benefits. Signal-Aware Dynamic Power Management 25 The system continuously monitors real-time signal strength metrics (RSRP, RSRQ, SINR). By analyzing and applying signals, it implements optimized power consumption profiles for each signal condition. Aggressive energy saving strategies are implemented in weak signal regions (RSRP < -110 dBm). Enters: background data synchronization is disabled, network scanning frequency is reduced, The idle mode timer is extended. This approach extends battery life by 40-60%. 30 Context-Sensitive Adaptive Optimization User's current activity status, active application requirements, data usage profile. and service quality expectations are evaluated, creating a customized force management system for each scenario. Policies are created. For example, even if the signal weakens during an active VoLTE call, the call will continue for 35 minutes. 4 To maintain quality, power saving is limited; however, the same signal is present in idle mode. Under these conditions, the system switches to maximum energy saving mode. Predictive Power Optimization By using historical signal strength data, location information, and user mobility patterns, 5 Future signal conditions are predicted with 75-85% accuracy. This proactive approach, This allows the device to prepare before entering a weak signal area: non-critical. Background tasks are completed, important data is synchronized in advance, network The parameters are optimized beforehand. Intelligent RAT Selection and Management In devices supporting multiple RATs, the optimal approach is determined based on signal strength and energy efficiency criteria. A technology choice is made. Data traffic occurs when there is a weak cellular signal but a strong Wi-Fi signal. It automatically switches to Wi-Fi (Wi-Fi offloading), which reduces battery consumption by 30-45%. Similarly, by minimizing unnecessary 5G-LTE transitions, energy from switching is reduced by 15%. Losses are prevented. Signaling Load Minimization In areas with weak signals, unnecessary network signaling operations are reduced using intelligent algorithms: RRC link setup / release cycles are optimized, and the monitoring area update frequency is 20. The values ​​are reduced, and the cell reselection cycle values ​​are dynamically adjusted. This Optimizations reduce network signaling load by 50-70% while also decreasing device battery consumption. It reduces the price by 20-30%. Adaptive Transmission Power Profiling 25 Considering the device's battery level, signal quality, and application requirements, Uplink transmission power levels are dynamically adjusted. At critical battery levels (15%). (six) Minimum transmission power is used to maintain service continuity. Strong signal. In these regions, transmission power is automatically reduced to prevent unnecessary energy consumption. It is prevented. 30 Location-Based Signal Mapping Signal strength in locations frequently visited by the user (home, workplace, shopping mall) Profiles are learned and a local signal strength map is created. Using this map, the user Proactive power saving measures are implemented when approaching known weak signal areas, or Alternative network connection methods (Wi-Fi, femtocell) are recommended. User Experience Protection Guarantee When implementing power saving optimizations, prioritize critical services (emergency calls, instant messaging, etc.). Messaging notifications and alarms are guaranteed to work without interruption. Multi-layered priority. The system classifies applications and energy saving policies are different for each level. This approach is implemented. While providing 35-50% energy savings, it also improves the user experience. It creates a minimal impact (less than 5%). Machine Learning-Based Personalization Each user has unique usage patterns, mobility profiles, and preferences. By learning about these practices, personalized power management strategies are developed. Supervised User battery consumption predictions are made using learning algorithms and daily... Adaptive plans are implemented according to usage scenarios. Personalization, general 15 It provides 20-30% more energy savings compared to other profiles. Multiple Metric Optimization The system improves not only battery life, but also user experience (QoE), network It also optimizes performance, service continuity, and data usage costs. Multi-purpose 20 The optimization framework finds Pareto-optimal equilibrium points between these objectives. and are prioritized according to user preferences. The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to these figures, 25 is more clearly understood. It will be understood. Explanation of the Figures Figure 1 is a schematic view of the system described in the invention. 30 The drawings do not necessarily need to be scaled and are useful for understanding the invention. Unnecessary details may have been omitted. 35 6 Explanation of Part References 1. Signal measurement module 2. Data collection and preprocessing unit 3. Context analysis engine 5 4. Signal quality evaluation module 5. Battery status monitoring system 6. Historical data repository 7. Location-signal mapping system 8. Machine learning and predictive engine 10 9. Power optimization algorithm 10. Power profile manager 11. Radio layer control interface 12. Application Layer Manager 13. Performance monitoring and feedback system 15 14. User interface and notification manager Detailed Description of the Invention In this detailed explanation, the preferred configurations of the invention are presented, only to better understand the subject matter. in order to facilitate understanding and without imposing any limiting effects It is explained. The system described in this invention extends the battery life of mobile devices depending on signal strength conditions. It uses a multi-layered, adaptive, and learning approach to optimize. The core study is 25. The principle is based on the direct relationship between real-time signal quality and power consumption. The advantage is: in weak signal conditions, devices are much better able to maintain network connectivity. They consume a lot of energy, therefore aggressive energy saving strategies are necessary in these situations. It needs to be implemented. In the first stage, the signal measurement module (1) continuously measures the signal strength from the modem chipset and It collects quality metrics (RSRP, RSRQ, RSSI, and SINR). This raw data is used for data collection and... The preprocessing unit (2) filters, normalizes and converts into meaningful feature vectors. It is converted. The Interquartile Range (IQR) method for outlier removal is used temporally. Exponentially weighted moving average (EWMA) filtering for correction, min-max 35 7 Normalization and scaling, time series feature extraction for trend, variance, and change. Ratio calculations are performed. In parallel, the context analysis engine (3) analyzes the user's current It analyzes activity (active application, data usage, foreground / background status). The battery status monitoring system (5) displays the current capacity (mAh), voltage (V), temperature (°C), Charge / discharge rate (mA), health status, etc., indicate the device's energy reserves and consumption rate. 5 It tracks the battery and also estimates the remaining battery life. The signal quality evaluation module (4) categorizes the signal strength into categories (excellent, good, (medium, weak) and determines the trend direction with signal stability. Fast damping detection. Its algorithm detects short-term fluctuations. All signal measurements are taken at location 10. data, power consumption metrics and usage patterns in time series database format. The historical data is stored in the data repository (6) with a timestamp. Preferably in a sliding window. Data for the last 30 days is kept in accordance with the principle. Location-signal mapping system (7), GPS Signals in locations frequently visited by the user, using coordinates and / or Cell-ID information. Profiles are learned. Geofencing identifies frequently used locations (home, work, routes). Every 15 Average signal strength, variance, and time-dependent profile for the location (day / night). It calculates the differences. It creates a grid-based heat map. Machine learning and prediction engine (8) analyzes past data to predict future signals It predicts conditions, learns user behavior patterns, and detects abnormal power consumption. Algorithms like Random Forest and LSTM artificial neural networks detect their states. Using this method, you can predict the signal strength 5-10 minutes later with high accuracy. The power optimization algorithm (9) brings together all this information to create a multi-purpose It solves the optimization problem: maximizing battery life while improving user experience by 25%. To protect and guarantee the uninterrupted operation of critical services. Algorithm, signal quality, predicted future state, battery level and user preferences It finds the optimal balance. The power profile manager (10) selects the appropriate power mode according to the optimization output and the relevant 30 It defines the parameters. It defines five different power modes: 1. Maximum Performance: Excellent signal, sufficient battery life. 2. Balanced: Moderate signal / battery. 3. Power Saving: Weak signal or low battery. 4. Extreme Power Saving: Critical battery (<15%). 35 8 5. Emergency: Only emergency calls (<5%). Radio layer control interface (11) transmission power setting, network scan frequency, DRX The configuration parameters are passed to the modem software by the application layer manager. (12) background task scheduling and data synchronization at the operating system level 5 and configures connection management settings. The performance monitoring and feedback system (13) continuously evaluates the effectiveness of the system, It calculates KPIs and compares them to baseline using A / B testing. Objectives When not met, the feedback loop is activated, adjusting the ML model parameters and 10 Optimization weights are adjusted automatically. The user interface and notification manager (14) ensures the system's transparent operation and the user It provides interaction: informing the user about the current status (power mode, battery life, etc.). It provides manual control options and communicates preferences to the system. 15

Claims

9 REQUESTS 1. Optimizing battery life and energy consumption based on signal strength in mobile devices. It is a system that enables increased efficiency; its feature is:  5 that collects real-time signal strength and quality metrics from the modem chipset. at least one signal measurement module (1),  Filtering, normalizing, and extracting meaningful features from the collected raw data. Data collection and preprocessing unit (2) which converts into vectors,  The user's current active application, data usage, and foreground / background activity. context analysis engine that analyzes the situation (3), 10  Categorizing signal strength, signal stability, and trend direction. Determining short-term fluctuations with a fast damping detection algorithm. Detecting signal quality assessment module (4),  Data on the device's current capacity, voltage, temperature, charge / discharge rate, and energy. 15 that monitors reserves and discharge rate, and estimates remaining battery life. Battery status monitoring system (5),  Time series of signal measurements, location data, and power consumption metrics. a history data repository that stores timestamped data in the format (6),  By learning the signal profiles in the locations the user frequently visits, location tracking a location-signal mapping system that creates a heat map based on (7), 20  In the historical data repository (6) and in the position-signal mapping system (7) By processing the data, future signal conditions and power consumption needs can be predicted. machine learning and prediction engine that proactively predicts (8),  Current signal quality, predicted future signal status, battery input level and activity data from context analysis engine (3) 25 taking this as a basis and striking a balance between user experience and energy saving. power optimization algorithm (9),  According to the output of the power optimization algorithm (9), for the device Power profile manager (10) determines the appropriate power mode.  Transmission power 30 in accordance with the mode determined by the power profile manager (10). modem settings, network scan frequency and DRX configuration parameters Radio layer control interface (11) which transmits to the software,  Background task scheduling at the operating system level, data The application layer that handles synchronization and connection management settings. Manager (12) 35 It includes.

2. A system that conforms to Claim 1, and its feature is that it uses Random Forest and LSTM for forecasting. Machine learning and prediction engine using artificial neural network algorithms (8) It includes. 5

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