ENERGY-EFFICIENT 5G BASE STATIONS VIA TIME SERIES CORRELATION OF POWER CONSUMPTION AND TRAFFIC DEMAND

Abstract

ABSTRACT The present disclosure introduces an energy-efficient 5G base station system 100 which optimizes power consumption by dynamically adjusting to traffic demand using real-time traffic monitoring system 102 for continuous data gathering on network usage. It leverages a data collection module 104 to aggregate real-time and historical data, with time series analysis engine 106 identifying traffic and power usage patterns. The correlation module 108 pinpoints specific periods where power adjustments can reduce energy consumption, while machine learning algorithms for demand prediction 110 forecast future traffic. A predictive traffic model 112 then enables proactive adjustments implemented by dynamic power management system 114 to optimize power output. The power mode switching system 118 transitions the station between active, idle, and sleep modes, and an adaptive load balancing module 122 distributes network traffic efficiently across base stations. The other components are edge computing integration 124 and energy harvesting mechanism 126.

Specification

Description:DETAILED DESCRIPTION

[00021] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00022] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of energy-efficient 5G base stations via time series correlation of power consumption and traffic demand and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00023] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00024] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00025] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00026] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00027] Referring to Fig. 1, energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of real-time traffic monitoring system 102, data collection module 104, time series analysis engine 106, correlation module 108, machine learning algorithms for demand prediction 110, predictive traffic model 112, dynamic power management system 114, dynamic power scaling (DPS) 116, power mode switching system 118, monitoring and feedback loop 120, adaptive load balancing module 122, edge computing integration 124, energy harvesting mechanism 126 and self-optimizing network (SON) compatibility 128

[00028] Referring to Fig. 1, the present disclosure provides details of energy-efficient 5G base stations 100 for optimizing power consumption based on real-time traffic demand analysis. It dynamically adjusts power levels to reduce energy waste during low-traffic periods using time series correlation and predictive analytics. In one embodiment, the energy-efficient 5G base station system 100 may be provided with real-time traffic monitoring system 102, data collection module 104, and time series analysis engine 106, which gather and analyze traffic patterns and power usage data. The system comprises correlation module 108 and machine learning algorithms for demand prediction 110, which identify optimal times for power adjustments. Additional components, such as dynamic power management system 114, dynamic power scaling 116, and adaptive load balancing module 122, enable efficient energy distribution across network nodes. Edge computing integration 124 and energy harvesting mechanism 126, further enhance sustainability.

[00029] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with real-time traffic monitoring system 102, which gathers continuous data on network traffic, including user density, device connections, and data transfer types. This component 102 ensures that the system can detect fluctuations in network demand instantaneously, allowing the energy-efficient 5G base station system 100 to respond dynamically. Real-time traffic monitoring system 102 works closely with data collection module 104 to provide accurate, up-to-the-minute insights that inform power adjustments and demand forecasting.

[00030] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with data collection module 104, which aggregates both real-time and historical data on traffic and power consumption. It sources information from various network nodes to create a comprehensive dataset for analysis. This module 104 is essential for feeding the time series analysis engine 106 with accurate inputs, allowing the system to identify patterns over time. By continuously updating its dataset, data collection module 104 supports the entire framework in achieving optimal energy efficiency based on traffic trends.

[00031] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with time series analysis engine 106, which applies advanced statistical and machine learning models to the collected data to detect recurring traffic patterns and trends. This component is critical in distinguishing times of high and low demand, helping the system anticipate and prepare for these fluctuations. The time series analysis engine 106 collaborates closely with correlation module 108 to refine its predictive accuracy, ensuring that energy adjustments are effectively aligned with actual traffic demand.

[00032] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with correlation module 108, which correlates the identified traffic demand patterns with corresponding power consumption levels, pinpointing opportunities for energy savings. It leverages insights from time series analysis engine 106 and data collection module 104 to understand when power can be reduced without degrading service quality. The correlation module 108 serves as the foundation for predictive traffic model 112, enabling the system to implement energy adjustments proactively.

[00033] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with machine learning algorithms for demand prediction 110, which forecast upcoming traffic demand based on historical and real-time data. This component enhances the accuracy of predictive traffic model 112 by leveraging patterns and external factors, such as time of day and weather. Machine learning algorithms for demand prediction 110 enable the system to anticipate high or low traffic periods, allowing dynamic power management system 114 to adjust energy use efficiently in response to predicted demand.

[00034] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with predictive traffic model 112, which uses the forecasted data to identify expected traffic demand and prepare the system for proactive power adjustments. By integrating insights from machine learning algorithms for demand prediction 110 and time series analysis engine 106, predictive traffic model 112 ensures accurate energy scaling to maintain network efficiency. This model directly informs the actions taken by dynamic power management system 114, making energy optimization seamless.

[00035] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with dynamic power management system 114, which adjusts the power output of the base station components in real-time based on traffic analysis. This system scales power up or down in response to traffic variations identified by predictive traffic model 112 and correlation module 108, optimizing energy usage while sustaining network performance. Dynamic power management system 114 is crucial for achieving the desired balance between service quality and energy savings.

[00036] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with dynamic power scaling (DPS) 116, which fine-tunes power levels across individual base station components, such as transmitters and processors, according to immediate demand. DPS 116 works with real-time traffic monitoring system 102 and dynamic power management system 114 to adjust power allocation granularly, reducing energy waste during low-demand periods and ensuring sufficient capacity during peak times.

[00037] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with power mode switching system 118, which enables the base station to operate in various power modes (e.g., active, idle, and sleep) depending on traffic demand. This component allows the system to conserve energy during off-peak hours by reducing power to minimal levels. Power mode switching system 118 coordinates with predictive traffic model 112 to anticipate demand fluctuations, ensuring smooth transitions between power modes without impacting user experience.

[00038] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with monitoring and feedback loop 120, which continually monitors the actual traffic demand and power consumption, refining predictive models based on real-world performance. This loop plays a key role in enhancing the accuracy of machine learning algorithms for demand prediction 110 by providing updated feedback, enabling the system to adjust dynamically to changing conditions and further optimize energy savings.

[00039] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with adaptive load balancing module 122, which efficiently distributes network load across nearby base stations to optimize power consumption and maintain service quality. This component allows for the redirection of traffic when a particular base station experiences high demand, reducing strain and enhancing energy efficiency across the network. Adaptive load balancing module 122 collaborates with real-time traffic monitoring system 102 to maintain an even distribution of demand.

[00040] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with edge computing integration 124, allowing data processing to occur closer to the base station, reducing latency and communication costs associated with remote data centers. This component 124 enhances the responsiveness of real-time traffic monitoring system 102 and dynamic power management system 114 by processing data locally. Edge computing integration 124 contributes to energy efficiency by minimizing the need for high-bandwidth communication with central servers.

[00041] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with energy harvesting mechanism 126 (optional), which uses renewable energy sources, such as solar or wind power, to supply additional energy during low-demand periods. This component reduces the base station's dependency on the grid, providing sustainable energy options that further optimize power usage. Energy harvesting mechanism 126 works in conjunction with power mode switching system 118 to allocate renewable energy when possible.

[00042] Referring to Fig. 1, energy-efficient 5G base station system 100 is provided with self-optimizing network (SON) compatibility 128, enabling the system to automatically adjust network configurations based on real-time conditions and user patterns. This component enhances the efficiency of adaptive load balancing module 122 and dynamic power management system 114, allowing the base station to respond to sudden changes in network demand autonomously. SON compatibility 128 ensures that the system maintains energy efficiency without human intervention.

[00043] Referring to Fig 2, there is illustrated method 200 for energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100. The method comprises:

At step 202, method 200 includes real-time traffic monitoring system 102 actively gathering data on network traffic demand, user density, device connections, and types of data transfer to provide immediate insights into network usage patterns;

At step 204, method 200 includes data collection module 104 aggregating this real-time data with historical traffic demand and power consumption data from various network nodes, creating a comprehensive dataset for further analysis;

At step 206, method 200 includes time series analysis engine 106 analyzing the aggregated dataset to identify recurring patterns in both traffic demand and power consumption over time, helping forecast periods of low and high traffic;

At step 208, method 200 includes correlation module 108 correlating the identified traffic patterns with corresponding power consumption levels, pinpointing opportunities for power adjustments during low-traffic periods without compromising service quality;

At step 210, method 200 includes machine learning algorithms for demand prediction 110 using the patterns identified by correlation module 108 and insights from time series analysis engine 106 to forecast future traffic demand based on real-time conditions and historical trends;

At step 212, method 200 includes predictive traffic model 112 applying these forecasts to estimate upcoming traffic levels, enabling proactive adjustments to power consumption in response to expected demand shifts;

At step 214, method 200 includes dynamic power management system 114 using the predictions from predictive traffic model 112 to adjust the power output of the base station components dynamically, scaling power up or down based on anticipated traffic demand;

At step 216, method 200 includes dynamic power scaling (DPS) 116 fine-tuning power levels for specific base station components, such as transmitters and signal processors, further optimizing energy usage during both peak and off-peak traffic periods;

At step 218, method 200 includes power mode switching system 118 transitioning the base station between active, idle, and sleep modes based on the predicted traffic demand, conserving energy by reducing power during periods of minimal activity;

At step 220, method 200 includes monitoring and feedback loop 120 continuously tracking actual traffic demand and power consumption, using real-time feedback to refine the predictive models for future demand, further enhancing the system's energy optimization accuracy;

At step 222, method 200 includes adaptive load balancing module 122 distributing traffic efficiently across multiple base stations in high-density areas to ensure optimal power consumption across the network and prevent any single station from overloading;

At step 224, method 200 includes edge computing integration 124 processing data locally at the base station to reduce latency and decrease energy demands associated with data transmission to distant data centers, supporting the energy-efficient operation of the network;

At step 226, method 200 includes energy harvesting mechanism 126, harnesses renewable energy sources, such as solar or wind, to supplement grid power during low-demand periods, further reducing the base station's overall energy consumption.


[00044] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00045] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00046] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

, Claims:WE CLAIM:
1. A energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 comprising of
real-time traffic monitoring system 102 to actively gather data on network traffic demand and user density;

data collection module 104 to aggregate real-time and historical data on traffic and power consumption;

time series analysis engine 106 to analyze patterns in traffic demand and power usage over time;

correlation module 108 to correlate traffic patterns with power consumption levels for optimization;

machine learning algorithms for demand prediction 110 to forecast future traffic demand based on analyzed data;

predictive traffic model 112 to apply forecasts and estimate upcoming traffic levels;

dynamic power management system 114 to adjust power output dynamically in response to traffic demand;

dynamic power scaling (DPS) 116 to fine-tune power levels for specific base station components;

power mode switching system 118 to transition the base station between active, idle, and sleep modes;

monitoring and feedback loop 120 to track traffic demand and refine predictive models;

adaptive load balancing module 122 to distribute network traffic across base stations for optimal power usage;

edge computing integration 124 to process data locally and reduce energy demands associated with distant data centers;

energy harvesting mechanism 126 to utilize renewable energy sources to supplement grid power during low-demand periods; and

self-optimizing network (SON) compatibility 128 to automatically adjust network configurations based on real-time conditions and user patterns, enhancing energy efficiency without manual intervention.

2. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein real-time traffic monitoring system 102 is configured to continuously gather data on user density, device connections, and network traffic types, enabling real-time adjustment of power levels based on precise, dynamic traffic demand insights.

3. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein data collection module 104 is configured to aggregate both real-time and historical data on traffic and power consumption across network nodes, providing a comprehensive dataset essential for advanced pattern analysis and accurate demand forecasting.

4. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein time series analysis engine 106 is configured to apply machine learning algorithms to detect recurring patterns in traffic demand and power usage, enabling proactive adjustments by identifying high and low traffic periods.

5. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein correlation module 108 is configured to correlate traffic patterns with power consumption, pinpointing specific periods where power can be minimized without affecting network performance, thus optimizing energy usage dynamically.

6. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein machine learning algorithms for demand prediction 110 are configured to forecast future traffic demand by leveraging identified traffic patterns and external variables, enabling preemptive power adjustments based on predicted demand.

7. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein dynamic power management system 114 is configured to adjust power output across base station components in response to traffic forecasts, enabling fine-grained scaling of power levels to reduce energy consumption during low-demand periods.

8. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein power mode switching system 118 is configured to transition the base station between active, idle, and sleep modes based on real-time and predicted traffic demand, ensuring power conservation without impacting service quality.

9. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein adaptive load balancing module 122 is configured to distribute traffic across multiple base stations in high-density areas, optimizing overall power usage by preventing any single station from becoming overloaded while maintaining efficient network operation.

10. The energy-efficient 5G base stations via time series correlation of power consumption and traffic demand 100 as claimed in claim 1, wherein method comprises of
real-time traffic monitoring system 102 actively gathering data on network traffic demand, user density, device connections, and types of data transfer to provide immediate insights into network usage patterns;

data collection module 104 aggregating this real-time data with historical traffic demand and power consumption data from various network nodes, creating a comprehensive dataset for further analysis;

time series analysis engine 106 analyzing the aggregated dataset to identify recurring patterns in both traffic demand and power consumption over time, helping forecast periods of low and high traffic;

correlation module 108 correlating the identified traffic patterns with corresponding power consumption levels, pinpointing opportunities for power adjustments during low-traffic periods without compromising service quality;

machine learning algorithms for demand prediction 110 using the patterns identified by correlation module 108 and insights from time series analysis engine 106 to forecast future traffic demand based on real-time conditions and historical trends;

predictive traffic model 112 applying these forecasts to estimate upcoming traffic levels, enabling proactive adjustments to power consumption in response to expected demand shifts;

dynamic power management system 114 using the predictions from predictive traffic model 112 to adjust the power output of the base station components dynamically, scaling power up or down based on anticipated traffic demand;

dynamic power scaling (DPS) 116 fine-tuning power levels for specific base station components, such as transmitters and signal processors, further optimizing energy usage during both peak and off-peak traffic periods;

power mode switching system 118 transitioning the base station between active, idle, and sleep modes based on the predicted traffic demand, conserving energy by reducing power during periods of minimal activity;

monitoring and feedback loop 120 continuously tracking actual traffic demand and power consumption, using real-time feedback to refine the predictive models for future demand, further enhancing the system's energy optimization accuracy;

adaptive load balancing module 122 distributing traffic efficiently across multiple base stations in high-density areas to ensure optimal power consumption across the network and prevent any single station from overloading;

edge computing integration 124 processing data locally at the base station to reduce latency and decrease energy demands associated with data transmission to distant data centers, supporting the energy-efficient operation of the network; and

energy harvesting mechanism 126, which harnesses renewable energy sources, such as solar or wind, to supplement grid power during low-demand periods, further reducing the base station's overall energy consumption.

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