Method and system for energy optimization using traffic patterns - Patents.com

Abstract

A method and system for optimizing energy using traffic patterns in a network is disclosed. The method may include receiving first identification data for one or more user devices and receiving second identification data for one or more cell sites, the second identification data being based on one or more user devices communicating with the one or more cell sites. The method may also include applying a threshold criterion or condition to the received first identification data and the received second identification data. Additionally, the method may include generating a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and the applied threshold criterion or condition.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is based on and claims priority to U.S. Provisional Patent Application No. 63 / 314,743, filed February 27, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0002] One of the primary requirements of cellular network operators is to ensure that the network is operating at maximum efficiency while minimizing resources used. As a result, cellular network monitoring and optimization is a key component of many modern cellular networks. To ensure the best possible performance for cellular network subscribers, the network is monitored and periodically optimized so that its resources can be more effectively and efficiently utilized within the core network and / or radio access network ("RAN"). Typically, network optimization is affected by manual modification of network parameters based on information about network performance in the radio network and core network. Such information is periodically retrieved and analyzed by operations support systems ("OSS") to derive key performance indicators ("KPIs"). Current KPIs include typical system-level (e.g., related to user or cell throughput) and link-level (e.g., various transmission error rates) metrics.

[0003] Currently, there are systems that map base stations located geographically close to each other to predict traffic patterns. However, one of the limitations of such systems is that they do not learn network traffic behavior from similar base stations located elsewhere in the RAN. For example, if a user equipment or user device is connecting to a cell site from a fast moving train or car, there may be several different neighboring cell sites located in different clusters in the path of the user device. This is not taken into account in conventional systems. In real applications, only a few cell sites in a cluster may receive high network traffic, while other cell sites do not. Therefore, the possibility of implementing effective energy orchestration and resource allocation based on cell site traffic is very limited, and the immutable nature of such existing systems makes them unable to adapt to various network traffic patterns, resulting in energy wastage that is very inefficient for network operators. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, what is needed is a more efficient method and system for utilizing network traffic path information from neighboring cell sites to predict accurate traffic flows and network usage between cell sites in order to better allocate network resources to improve energy savings and efficiency for network elements that operate or manage those cell sites in the network. [Means for solving the problem]

[0005] According to an exemplary embodiment, a method and system for network traffic pattern recognition and dynamic resource allocation is disclosed that utilizes network traffic path information from neighboring cell sites to predict accurate traffic flow patterns and network usage between cell sites to better allocate network resources to improve energy savings and efficiency for network elements that operate or manage those cell sites in the network. In another exemplary embodiment, a method and system for network traffic pattern recognition and dynamic resource allocation is disclosed for identifying base stations and cell sites based on user device traffic patterns to perform energy orchestration and dynamic resource allocation. In some embodiments, this can be achieved by generating multiple cell site clusters, taking input from user traffic patterns across several observation points, then generating a neural network (“NN”) based server embeddings, utilizing the embeddings to predict traffic patterns, and further modulating server sleep and awake patterns proactively. Here, the disclosed method and system described herein is adaptive and can evolve with new traffic patterns of cell sites. Furthermore, the cell site clustering based on user traffic can be generated automatically without human intervention. Additionally, the methods and systems of the present disclosure described herein can enhance energy orchestration resulting in higher savings by predicting network traffic before a user device actually reaches a cell site and evaluating how traffic will flow through any one or more cell sites or base stations, thereby minimizing processing time, workload, and operations of network elements.

[0006] According to another example embodiment, a method for optimizing energy using traffic patterns in a network is disclosed, which may include receiving first identification data for one or more user devices, receiving second identification data for one or more cell sites based on the one or more user devices communicating with the one or more cell sites, applying a threshold criterion or condition to the received first identification data and the received second identification data, and generating a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and the applied threshold criterion or condition.

[0007] Furthermore, the method may include grouping the first identification data and the second identification data with respect to one or more times, time periods, or time ranges.

[0008] Additionally, where generating a probability of network traffic for each of the one or more cell sites may be further based on a defined time of day, time period, or time range.

[0009] Additionally, generating the probability of network traffic for each of the one or more cell sites may be further based on neural network embeddings.

[0010] Additionally, the method may include obtaining one or more vectors of numbers representing each of the one or more cell sites.

[0011] Additionally, the method may include generating a cluster for the numerical representation of each of the one or more cell sites.

[0012] Additionally, the method may include determining travel routes for the one or more user devices based on the probability of network traffic generated for each of the one or more cell sites.

[0013] The method may also include generating a grouping or clustering of one or more cell sites within a travel path of the one or more user devices based on a probability of network traffic generated for each of the one or more cell sites.

[0014] Additionally, the method may include allocating network resources to each of the one or more cell sites based on a probability of network traffic generated for each of the one or more cell sites.

[0015] Additionally, allocating network resources to each of the one or more cell sites may include managing uptime of one or more servers in communication with the one or more cell sites.

[0016] In another exemplary embodiment, an apparatus for optimizing energy using traffic patterns in a network is disclosed, the apparatus including: a memory storage storing computer-executable instructions; and a processor communicatively coupled to the memory storage. The processor is configured to execute the computer-executable instructions to cause the apparatus to receive first identification data for one or more user devices, receive second identification data for one or more cell sites based on one or more user devices communicating with the one or more cell sites, apply a threshold criterion or condition to the received first identification data and the received second identification data, and generate a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and the applied threshold criterion or condition.

[0017] Additionally, the computer-executable instructions, when executed by the processor, may further cause the apparatus to group the first identification data and the second identification data with respect to one or more times, time periods, or time ranges.

[0018] Additionally, generating a probability of network traffic for each of the one or more cell sites may be further based on a defined time of day, time period, or time range.

[0019] Additionally, generating a probability of network traffic for each of the one or more cell sites may be further based on a neural network embedding.

[0020] Additionally, the computer-executable instructions, when executed by the processor, may further cause the apparatus to obtain one or more vectors of numbers representing each of the one or more cell sites.

[0021] Additionally, the computer-executable instructions, when executed by the processor, may further cause the apparatus to generate a cluster for the numerical representation of each of the one or more cell sites.

[0022] Additionally, the computer-executable instructions, when executed by the processor, may further cause the apparatus to determine travel routes for one or more user devices based on the probability of generated network traffic for each of the one or more cell sites.

[0023] The computer-executable instructions, when executed by the processor, may also cause the apparatus to generate a grouping or clustering of one or more cell sites within a travel path of the one or more user devices based on a probability of generated network traffic for each of the one or more cell sites.

[0024] Moreover, the computer-executable instructions, when executed by the processor, may further cause the apparatus to allocate network resources to each of the one or more cell sites based on a probability of generated network traffic to each of the one or more cell sites.

[0025] In another exemplary embodiment, a non-transitory computer-readable medium includes computer-executable instructions for an apparatus optimizing energy using traffic patterns in a network, the computer-executable instructions, when executed by at least one processor of the apparatus, causing the apparatus to receive first identification data for one or more user devices, receive second identification data for one or more cell sites based on one or more user devices communicating with the one or more cell sites, apply threshold criteria or conditions to the received first identification data and the received second identification data, and generate a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and the applied threshold criteria or conditions.

[0026] The features, advantages and importance of exemplary embodiments of the present disclosure will now be described with reference to the accompanying drawings, in which like reference numerals refer to like elements, and in which: [Brief description of the drawings]

[0027] [Figure 1] FIG. 1 illustrates a general system architecture for the network traffic pattern recognition method and system of the present disclosure described herein, in accordance with one or more exemplary embodiments.

[0028] [Diagram 2] FIG. 2 is a simplified overhead view of a map of cell sites in a network for the network traffic pattern recognition method and system of the present disclosure described herein in accordance with one or more exemplary embodiments.

[0029] [Diagram 3] FIG. 13 is another simplified top-down view of a geographical map of cell sites in a network with dynamic cell site clusters based on traffic patterns between cell sites in accordance with one or more exemplary embodiments of the network traffic pattern recognition method and system disclosed herein.

[0030] [Figure 4] FIG. 13 is another simplified top-down view of a geographical map of cell sites in a network, along with dynamic cell site clusters based on traffic patterns between cell sites and user device movement paths within each cluster, in accordance with one or more exemplary embodiments of the network traffic pattern recognition method and system disclosed herein.

[0031] [Diagram 5] FIG. 13 is another simplified top-down view of a geographical map of cell sites in a network, along with alternative dynamic cell site clusters based on new traffic patterns between cell sites and new movement paths of user devices within the alternative cell site clusters, in accordance with one or more exemplary embodiments of the network traffic pattern recognition method and system disclosed herein.

[0032] [Figure 6] 1 illustrates a flowchart for the network traffic pattern recognition method and system of the present disclosure described herein, in accordance with one or more exemplary embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] The following detailed description of the exemplary embodiments refers to the accompanying drawings, in which the same reference numbers in different drawings can identify the same or similar elements.

[0034] The above invention disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the precise form disclosed. Modifications and variations are possible in light of the above invention disclosure or may be acquired from practice of the implementations. Furthermore, one or more features or components of one embodiment may be incorporated in or combined with another embodiment (or one or more features of another embodiment). Furthermore, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed (at least partially) simultaneously, and the order of one or more operations may be permuted.

[0035] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

[0036] Although particular combinations of features are recited in the claims and / or disclosed herein, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features may be combined in ways not specifically recited in the claims and / or disclosed herein. Although each dependent claim listed below may depend directly on only one claim, in the disclosure of possible implementations, each dependent claim is included in conjunction with all other claims in the set of claims.

[0037] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described. Additionally, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." When only one item is intended, the term "one" or similar language is used. Additionally, as used herein, terms such as "has," "have," "having," "include," "including," and the like are intended to be open-ended terms. Additionally, the phrase "based on" is intended to mean "based at least in part on," unless specifically noted otherwise. Additionally, phrases such as "at least one of [A] and [B]" and "at least one of [A] or [B]" should be understood to include only A, only B, or both A and B.

[0038] Throughout this specification, references to "one embodiment," "an embodiment," "a non-limiting exemplary embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with the illustrated embodiment is included in at least one embodiment of the inventive solution. Thus, the phrases "in one embodiment," "in an embodiment," "in one non-limiting exemplary embodiment," and similar language throughout this specification all refer to the same embodiment, although not necessarily so.

[0039] Moreover, the described features, advantages, and characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Those skilled in the art will recognize in light of the description herein that the present disclosure can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, one may recognize additional features and advantages in certain embodiments that may not be present in all embodiments of the present disclosure.

[0040] In one implementation of the exemplary embodiment, the display page may include information residing in the memory of the computing device, which may be transmitted from the computing device to the database center and vice versa over a network. The information may be stored in the memory of each of the computing devices, in a data storage residing at the edge of the network, or in a server at the database center. The computing device or the mobile device may accept a non-transitory computer-readable medium. The non-transitory computer-readable medium may include instructions, logic, data, or code that may be stored in a permanent or temporary memory of the mobile device and may affect or initialize operations in some way by the mobile device. Similarly, one or more servers may communicate with one or more mobile devices over a network and may transmit computer files residing in the memory. The network may include, for example, the Internet, a wireless communication network, or any other network connecting one or more mobile devices to one or more servers.

[0041] Any discussion of computing or mobile devices may apply to any type of network device, including, but not limited to, mobile devices such as cellular phones (e.g., any "smartphone"), personal computers, server computers, or laptop computers, and telephones, personal digital assistants (PDAs), roaming devices such as network-attached roaming devices, wireless devices such as wireless email devices or other devices capable of wirelessly communicating with a computer network, or any other type of network device that may communicate over a network and handle electronic transactions. Any description of any mobile device mentioned may apply to other devices, such as short-range ultra-high frequency (UHF) devices, near field communication (NFC), infrared (IR), and devices that include Wi-Fi capabilities, among others.

[0042] "Software," "application," "app," and "firmware" and similar phrases and terms may include any non-transitory computer-readable medium having stored therein a program that, when executed by a computer, causes the computer to perform a method, function, or control operation.

[0043] Phrases and terms similar to "network" may include one or more data links that enable the transfer of electronic data between computer systems and / or modules. When information is transferred or provided to a computer over a network or another communications connection (either wired, wireless, or a combination of wired or wireless), the computer uses the connection as a computer-readable medium. Thus, by way of example and not limitation, a computer-readable medium may also include a network or data link, which may be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer.

[0044] Phrases and terms similar to "portal" or "terminal" may include an intranet page, an Internet page, a locally resident software or application, a mobile device graphical user interface, or a digital presentation for a user. A portal may also be any graphical user interface that accesses the various modules, components, features, options, and / or attributes of the present disclosure described herein. For example, a portal may be a web page accessed with a web browser, a mobile device application, or any application or software resident on a computing device.

[0045] FIG. 1 illustrates a diagram of a general network architecture according to one or more embodiments. Referring to FIG. 1, user devices 110, base stations / cell sites 120, and administrator terminals / dashboard users 130 can communicate bidirectionally through a secure network with a central server or application server 100 according to one or more embodiments. In addition, the components 110, 120, 130 may also communicate bidirectionally directly with each other through the network system of the present disclosure described herein according to one or more embodiments. Here, the user devices 110 can be any kind of user equipment (UE), such as users operating computing user devices A, B, and C, and network customers or telecommunication service providers. Each of the user devices 110 can communicate with the server 100 through a respective terminal or portal. The user devices 110 may communicate with the server 100 through the cell site 120 or another communication connection (e.g., Wi-Fi, Ethernet, etc.). The cell sites 120 may include any type and number of cell sites, base stations, cell towers, radios, femtocells, etc. (including any servers operating or managing each cell site) of a network service provider that provide wireless network access and services to any user of the user device 110. The administrator terminal or dashboard 130 may include any type of user having access privileges to access the dashboard or management portal of the present disclosure described herein, which may provide various user tools, maps, resource allocation, energy orchestration, and customer support options. It is contemplated within the scope of the present disclosure described herein that any user of the user device 110 may also access the administrator terminal or dashboard 130 of the present disclosure described herein.

[0046] 1, the central server 100 of the present disclosure described herein according to one or more embodiments can further bidirectionally communicate with a database / third party server 140. The database / third party server 140 can also include a user. Here, the server 140 can include a vendor and a database. Various data captured, collected, or aggregated from the cell sites 120 and / or user devices 110 can be uploaded or stored in the vendor and database, and can be retrieved from the vendor and database for network analysis and Neural Network (NN), machine learning, and artificial intelligence (AI) processing by the server 100. However, it is contemplated within the scope of the present disclosure described herein that the network traffic pattern recognition method and system of the present disclosure described herein can include any type of general network architecture.

[0047] With further reference to FIG. 1, one or more of the servers or terminals of elements 100-140 may include a personal computer (PC), a printed circuit board including a computing device, a minicomputer, a mainframe computer, a microcomputer, a telephone computing device, a wired / wireless computing device (e.g., a smartphone, a personal digital assistant (PDA)), a laptop, a tablet, a smart device, a wearable device, or any other similarly functional device.

[0048] In some embodiments, one or more of the servers, terminals, and users 100-140 may include a set of components such as a processor, memory, storage components, input components, output components, communication interfaces, and JSON UI rendering components, as shown in Figure 1. The set of components of a device may be communicatively coupled via a bus.

[0049] The bus may comprise one or more components that enable communication between the set of components of one or more servers or terminals of elements 100-140. For example, the bus may be a communications bus, a crossover bar, a network, etc. The bus may be implemented using a single or multiple (two or more) connections between the set of components of one or more servers or terminals of elements 100-140. The present disclosure is not limited in this respect.

[0050] One or more of the servers or terminals of elements 100-140 may include one or more processors. The one or more processors may be implemented in hardware, firmware, and / or a combination of hardware and software. For example, the one or more processors may include a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a general purpose single-chip or multi-chip processor, or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. The one or more processors may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration. In some embodiments, certain processes and methods may be performed by circuitry specific to a given function.

[0051] One or more processors may control the overall operation of one or more of the servers or terminals of elements 100 to 140 and / or a set of components (e.g., memory, storage components, input components, output components, communication interfaces, rendering components) of one or more of the servers or terminals of elements 100 to 140.

[0052] One or more of the servers or terminals of elements 100-140 may further comprise a memory. In some embodiments, the memory may comprise a random access memory (RAM), a read only memory (ROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a magnetic memory, an optical memory, and / or another type of dynamic or static storage device. The memory may store information and / or instructions for use (e.g., execution) by the processor.

[0053] A storage component of one or more of the servers or terminals of elements 100 through 140 may store information and / or computer readable instructions and / or code related to the operation and use of one or more of the servers or terminals of elements 100 through 140. For example, the storage component may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a universal serial bus (USB) flash drive, a Personal Computer Memory Card International Association (PCMCIA) card, a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer readable medium along with a corresponding drive.

[0054] One or more of the servers or terminals of elements 100-140 may further include an input component. The input component may include one or more components that enable the server and one or more of terminals 100-140 to receive information via user input or the like (e.g., a touch screen, a keyboard, a keypad, a mouse, a stylus, a button, a switch, a microphone, a camera, etc.). Alternatively or in addition, the input component may include a sensor that senses information (e.g., a Global Positioning System (GPS) component, an accelerometer, a gyroscope, an actuator, etc.).

[0055] Any one or more output components of the servers or terminals of elements 100-140 may include one or more components capable of providing output information from device 100 (e.g., a display, a liquid crystal display (LCD), a light emitting diode (LED), an organic light emitting diode (OLED), a haptic feedback device, a speaker, etc.).

[0056] One or more of the servers or terminals of elements 100-140 may further comprise a communication interface. The communication interface may include a receiver component, a transmitter component, and / or a transceiver component. The communication interface may allow one or more of the servers or terminals of elements 100-140 to establish a connection and / or transfer a communication with another device (e.g., a server, another device). The communication may be possible via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface may allow one or more of the servers or terminals of elements 100-140 to receive information from another device and / or provide information to another device. In some embodiments, the communication interface may provide communication with another device over a network, such as a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a private network, an ad-hoc network, an intranet, the Internet, an optical fiber-based network, a cellular network (e.g., a fifth generation (5G) network, a long term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a telephone network (e.g., a public switched telephone network (PSTN)), etc., and / or combinations thereof or other types of networks. Alternatively or in addition, the communication interface may provide communication with another device over a device-to-device (D2D) communication link, such as FlashLinQ, WiMedia, Bluetooth, ZigBee, Wi-Fi, LTE, 5G, etc. In other embodiments, the communication interface may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, etc. In an embodiment, any one of the acts or processes in the figures may be implemented by or using any one of the elements disclosed herein.It will be appreciated that other embodiments may be implemented in a variety of different architectures (e.g., without being limited thereto), including bare metal architectures, any cloud-based architecture, or deployment architectures such as Kubernetes, Docker, OpenStack, etc.

[0057] 2 is a simplified top-down view of a map of various cell sites 120 within a network of the presently disclosed network traffic pattern recognition method and system described herein, according to one or more exemplary embodiments. In particular, the cell sites 120 may include base stations 1-30, radio towers 1-30, or cell sites 1-30 distributed across various geographic regions, such as states, cities, suburbs, or regions. Additionally, each cell site 1-30 may be near a road, railroad, path, or travel route through which a user of a user device may travel. Additionally, each cell site 1-30 may be operated and / or managed via one or more servers, or various network components or elements, such as network switches and routers.

[0058] 3 is another simplified top-down view of a map of various cell sites 120 in a network of the disclosed network traffic pattern recognition method and system described herein, according to one or more exemplary embodiments. Here, based on the time, period, or time range that user devices are connected to and communicating with the network high traffic cell sites or those cell sites, certain cell sites may form groups or clusters representing cell sites that will receive higher than normal expected network traffic. For example, cell site 2, cell site 3, cell site 4, cell site 13, cell site 14, and cell site 15 (including nearby / adjacent cell sites) may be part of group or cluster B that has a higher probability of receiving higher than normal network traffic during a particular time, period, or time range (e.g., Monday 3:00-6:00 PM) based on the disclosed neural network (NN) embedded modeling, AI, or machine learning described herein. Thus, the cell sites in cluster B (including neighboring / adjacent cell sites) are allocated higher energy resources, while the cell sites (including neighboring / adjacent cell sites) with less network traffic potential have their energy consumption reduced by inactive or low network traffic such as cell site 1, cell site 9, and cell site 26-cell site 30. Thus, the disclosed method and system described herein can proactively dynamically perform energy orchestration and resource allocation of individual cell sites (including the primary cell site and their neighboring cell sites) according to the predicted usage or network traffic volume for such sites at a given time or during a period range before the cell sites receive such network traffic.

[0059] FIG. 4 is another simplified top-down view of a map of various cell sites 120 in a network of the presently disclosed network traffic pattern recognition method and system described herein, according to one or more exemplary embodiments. Here, based on the time of day, duration that user devices are connected to and communicating with the network high traffic cell sites or those cell sites, certain cell sites may form groups or clusters that represent cell sites that will receive higher than normal expected network traffic (or high utilization). Notably, FIG. 4 also illustrates the direction or path of one or more user devices within a particular group or cluster of cell sites. For example, as shown in FIG. 4, one or more user devices may travel in one direction, such as path G in cluster B, and one or more devices may travel in another direction, such as path I in cluster E. Here, the presently disclosed method and system described herein dynamically adapts and predicts the probability of network traffic for each cell site from the presently disclosed NN, AI, or machine learning model that includes either the path to or from the destination.

[0060] With further reference to FIG. 4, for example, the NN model of the present disclosure described herein may output a 0.8 or 80% probability that cell site 17 (in cluster A) will be utilized as opposed to a 0.1 or 10% probability that cell site 29 will be utilized over a given period of time. Similarly, neighboring cell sites to the path of cell site 17 may also have increased usage or network traffic probability, such as 90% of neighboring cell site 18 in the user device's previously known path. Thus, the system may dynamically allocate 80% of the power or energy usage to cell site 17 (or the server managing cell site 17) and 10% of the power or energy usage to cell site 29 (or the server managing cell site 29), resulting in very efficient energy usage and energy savings for the network. As mentioned above, cell sites within the user device's travel path (round trip) to and from the destination may also be considered. For example, this could include a user device traveling from a home location to a work location in one period (e.g., route H in cluster C) and then traveling from the work location to the home location in another period (e.g., route H1 in cluster C), along with all the cell sites (e.g., cell site 21 to cell site 25) that the user device connected to within that route during the entire round trip.

[0061] FIG. 5 is another simplified top-down view of a map of various cell sites 120 in a network of the presently disclosed network traffic pattern recognition method and system described herein, according to one or more exemplary embodiments. Here, based on the time of day or duration that user devices are connected to and communicating with the network high traffic cell sites or those cell sites, certain cell sites may form groups or clusters that represent cell sites that will receive higher than normal expected network traffic. In particular, FIG. 5 also illustrates alternative or new routes that user devices may take from previously known routes that may be considered by the presently disclosed NN models, AI, and machine learning methods and systems described herein. For example, as shown in FIG. 5, one or more user devices may travel in one direction, such as via route F in cluster B, but may instead branch off therefrom or form a new route along another set of cell sites, such as via route K in cluster J. In such a scenario, the disclosed systems and methods described herein may reallocate energy resources previously allocated to cell site 18, cell site 19, cell site 20, and cell site 22 along path F to cell site 29, cell site 28, and cell site 27 along new path K in new cluster J. Thus, the disclosed NN models, AI, and machine learning methods and systems described herein can dynamically and continuously train and retrain to learn new alternative paths for one or more user devices to provide and predict more accurate probabilities for network traffic and cell site utilization.

[0062] FIG. 6 illustrates a process flow of the disclosed network traffic pattern recognition and dynamic resource allocation method and system described herein, according to one or more exemplary embodiments. In step 200, the process can begin by tracking handovers between cell sites of each user device traveling a path (e.g., via road, rail, airplane, etc.) where each user device connects to a cell site on the path. For example, in one exemplary embodiment, as shown in FIG. 5, a user device may travel along a path F, thereby connecting and handing over between each cell site 7, cell site 6, cell site 5, cell site 4, cell site 16, cell site 17, cell site 18, cell site 19, cell site 20, and cell site 22 in cluster A. Now, in step 200, the disclosed system described herein can receive and obtain data of the identity (ID) of each user device, such as a user device connecting to cell site 16 at a particular date and time along path F, as shown in FIG. 5, the cell site identity (ID), and the time when a particular user device connected to a particular cell site. From the collected data, the system can determine the specific traffic patterns of cell sites within the network for each user device.

[0063] Still referring to FIG. 6, in step 202, the process may group cell sites that receive network traffic, and may perform this grouping for each user device that connects to a cell site in the network. Then, in step 204, the process may apply certain predefined threshold criteria or conditions to filter, refine, and limit the number of cell sites in the list. Then, in step 206, the process may also apply predefined threshold criteria or conditions to filter, refine, and limit the number of user devices. In particular, referring to steps 202-206, for every movement of each user device ID, the system generates a list of all cell site IDs that were in the path of the user device ID. Then, certain thresholds and threshold conditions may be applied to the list to filter the list for higher relevance, accuracy, and to provide a minimum number of cell sites that are in the path of the user device. Then, from the filtered list, all cell site ID paths that were in the path of any user device ID are grouped together. For example, a first group may be created for all cell sites that communicate with user device A, and a second group may be created for all cell sites that communicate with user device B. Additionally, each group may include overlapping cell site IDs that may be used by different user device IDs.

[0064] Still referring to FIG. 6, in step 208, the process can generate NN embedding models for all grouped cell sites. In particular, the algorithm of the present disclosure described herein can build a two-layer neural network that processes the journey of the user device ID by vectorizing the journey of the cell site ID. Here, the input of the NN embedding model can be the journey or path of the user device ID, and where the output can be a set of vectors. In step 210, the process can obtain vectors for values ​​or numbers representing each cell site based on the NN embedding. Then, in step 212, the process can cluster the values ​​or numerical representations of the cell sites. In particular, cell sites that are closer to other cell sites (e.g., neighboring cell sites) in any user device's route or journey are embedded closer to each other in the NN embedding. Such NN embeddings (e.g., closest neighboring cell sites) can also be visually represented on a map.

[0065] In other embodiments, the disclosed NN model, AI, or machine learning methods and systems described herein may assign a particular cell site a higher or lower weight to achieve improved probabilities for network traffic. Here, the NN embedded model may further provide as output a probability for network traffic and / or cell site utilization for a particular time, period, or range of time. Such probabilities may be used by the disclosed methods and systems described herein in step 214 to perform energy orchestration to dynamically allocate energy and network resources to each cell site based on a particular time, period, multiple periods, range of time, or dynamically adjustable schedule. For example, a highly utilized cell site may be automatically assigned higher power and energy resources, and a less utilized cell site may be automatically assigned lower power and energy resources. This may also include automatically managing and operating wake times, sleep times, and operation times of particular servers, computing devices, network routers, network switches, network elements, and various network resources associated with a particular cell site to optimize energy usage within the network.

[0066] In other embodiments, any of the foregoing discussion may be represented on a graphical user interface (GUI), such as in a dashboard or portal. For example, the GUI may display clusters of cell sites and routes of user devices in the network, as shown in Figures 3-5. Additionally, a user may be able to visually view future energy usage and consumption based on previous known traffic patterns, and may further provide network operators the ability to better manage their cell site networks during peak or low demand times and better predict future network infrastructure needs to meet the demands of the particular traffic patterns of their subscriber networks.

[0067] It is understood that the particular order or hierarchy of blocks in the processes / flowcharts disclosed herein represents an example approach. It is understood that the particular order or hierarchy of blocks within the processes / flowcharts may be rearranged based on design preferences. Also, some blocks may be combined or omitted. The accompanying method claims present various block elements in a sample order, and are not meant to be limited to the particular order or hierarchy presented.

[0068] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail of integration. Furthermore, one or more of the above components described above may be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include a computer-readable non-transitory storage medium having computer-readable program instructions for causing a processor to perform operations.

[0069] A computer readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer readable storage media includes the following: portable computer diskettes, hard disks, random access memories (RAM), read only memories (ROM), erasable programmable read only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or groove ridge structures having instructions recorded therein, and any suitable combination thereof. As used herein, computer-readable storage media should not be construed as being, per se, a transitory signal, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0070] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing / processing device.

[0071] The computer readable program code / instructions for carrying out the operations may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or object oriented programming languages ​​such as Smalltalk or C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer readable program instructions and personalize the electronic circuitry by utilizing state information of the computer readable program instructions to perform aspects or operations.

[0072] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions executing via the processor of the computer or other programmable data processing apparatus produce means for performing the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams. These computer readable program instructions may also be stored on a computer readable storage medium capable of directing a computer, programmable data processing apparatus, and / or other device to function in a particular manner such that a computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions for performing aspects of the functions / operations specified in one or more blocks of the flowcharts and / or block diagrams.

[0073] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device and cause the computer, other programmable apparatus, or other device to execute a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the function / operation specified in a block or groups of blocks of the flowcharts and / or block diagrams.

[0074] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. In this regard, each block in the flowcharts or block diagrams may represent a portion of a microservice, module, segment, or instruction that comprises one or more executable instructions that implement the specified logical functions. The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions described in the blocks may occur out of the order described in the figures. For example, two blocks shown in succession may actually be executed simultaneously or substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a dedicated hardware-based system that performs the specified functions or operations or executes a combination of dedicated hardware and computer instructions.

[0075] It will be apparent that the systems and / or methods described herein may be implemented in various forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not intended to limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to specific software code, and it will be understood that software and hardware can be designed to implement the systems and / or methods based on the description herein.

Claims

1. 1. A method for optimizing energy using traffic patterns in a network, comprising: receiving first identification data for one or more user devices; receiving, for one or more cell sites, second identification data based on the one or more user devices in communication with the one or more cell sites; applying a threshold criterion or condition to the received first identification data and the received second identification data; generating a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and applied threshold criteria or conditions; A method for providing the above.

2. Grouping the first identification data and the second identification data with respect to at least one of one or more times, time periods, and time ranges. The method of claim 1 further comprising:

3. The method of claim 1 , wherein the step of generating a probability of network traffic for each of the one or more cell sites is further based on a defined time of day, time period, or time range.

4. 2. The method of claim 1, wherein the step of generating network traffic probabilities for each of the one or more cell sites is further based on neural network embedding.

5. obtaining one or more vectors of numbers representing each of said one or more cell sites; The method of claim 4 further comprising:

6. generating a cluster for the numerical representation of each of the one or more cell sites; The method of claim 4 further comprising:

7. determining travel routes for the one or more user devices based on the generated probabilities of network traffic for each of the one or more cell sites; The method of claim 1 further comprising:

8. generating a grouping or clustering of one or more cell sites within a travel path of the one or more user devices based on the generated probability of network traffic for each of the one or more cell sites. The method of claim 1 further comprising:

9. allocating network resources to each of the one or more cell sites based on the generated probability of network traffic for each of the one or more cell sites. The method of claim 1 further comprising:

10. 10. The method of claim 9, wherein allocating network resources to each of the one or more cell sites comprises managing the operating hours of one or more servers in communication with the one or more cell sites.

11. An apparatus for optimizing energy using traffic patterns in a network, comprising: a memory storage device storing computer executable instructions; a processor communicatively connected to the memory storage; The processor executes the computer-executable instructions to cause the device to: receiving first identification data relating to one or more user devices; receiving, for one or more cell sites, second identification data based on the one or more user devices in communication with the one or more cell sites; applying a threshold criterion or condition to the received first identification data and the received second identification data; and generating a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and an applied threshold criterion or condition. Device.

12. The computer-executable instructions, when executed by the processor, further cause the apparatus to: The apparatus of claim 11 , further comprising: grouping the first identification data and the second identification data with respect to one or more times, periods or time ranges.

13. The apparatus of claim 11 , wherein generating a probability of network traffic for each of the one or more cell sites is further based on a defined time of day, time period, or time range.

14. 12. The apparatus of claim 11, wherein generating a probability of network traffic for each of the one or more cell sites is further based on neural network embedding.

15. The computer-executable instructions, when executed by the processor, further cause the apparatus to:

15. The apparatus of claim 14, further comprising: deriving one or more vectors of numbers representing each of said one or more cell sites.

16. The computer-executable instructions, when executed by the processor, further cause the apparatus to: The apparatus of claim 14 , further comprising: generating a cluster for a numerical representation of each of the one or more cell sites.

17. The computer-executable instructions, when executed by the processor, further cause the apparatus to: The apparatus of claim 11 , further comprising: determining travel paths for the one or more user devices based on the generated probabilities of network traffic for each of the one or more cell sites.

18. The computer-executable instructions, when executed by the processor, further cause the apparatus to:

12. The apparatus of claim 11, further comprising: generating a grouping or clustering of one or more cell sites within a travel path of the one or more user devices based on the generated probability of network traffic for each of the one or more cell sites.

19. The computer-executable instructions, when executed by the processor, further cause the apparatus to: The apparatus of claim 11 , further comprising: causing network resources to be allocated to each of the one or more cell sites based on the generated probability of network traffic for each of the one or more cell sites.

20. 1. A non-transitory computer-readable medium comprising computer-executable instructions for optimizing energy using traffic patterns in a network by an apparatus, the computer-executable instructions, when executed by at least one processor of the apparatus, causing the apparatus to: receiving first identification data relating to one or more user devices; receiving, for one or more cell sites, second identification data based on the one or more user devices in communication with the one or more cell sites; applying a threshold criterion or condition to the received first identification data and the received second identification data; generating a probability of network traffic for each of the one or more cell sites based on the received first identification data and the received second identification data and applied threshold criteria or conditions; Non-transitory computer-readable medium.

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