US-12628073-B2 - Edge device and method for handling service for multiple service providers

US12628073B2US 12628073 B2US12628073 B2US 12628073B2US-12628073-B2

Abstract

A central cloud server that includes a processor which periodically obtains sensing information from a plurality of edge devices at different locations and periodically obtains beam alignment information from the plurality of edge devices. The processor correlates the obtained sensing information and the beam alignment information for different times-of-day to generate a connectivity enhanced database. The connectivity enhanced database specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each edge device of the plurality of edge devices. The correlation is based on the obtained sensing information as input features and the beam alignment information as learning labels.

Inventors

Venkat Kalkunte
Mehdi Hatamian

Assignees

PELTBEAM INC.

Dates

Publication Date: 20260512
Application Date: 20250930

Claims (20)

1 . A central cloud server, comprising: a processor configured to: periodically obtain sensing information from a plurality of edge devices at different locations; periodically obtain beam alignment information from the plurality of edge devices; and correlate the obtained sensing information and the beam alignment information for different times-of-day to generate a connectivity enhanced database, wherein the connectivity enhanced database specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each edge device of the plurality of edge devices, and the correlation is based on the obtained sensing information as input features and the beam alignment information as learning labels.
2 . The central cloud server according to claim 1 , wherein the processor is further configured to periodically train a machine learning model for the different times-of-day on training data of the input features and parameters of the beam alignment information, the connectivity enhanced database is generated further based on the trained machine learning model, and the machine learning model is trained to determine patterns that map the input features to the learning labels for the correlation.
3 . The central cloud server according to claim 2 , wherein the input features comprise a distance of each edge device of the plurality of edge devices from User Equipment (UE), weather condition, a UE location, a moving direction of the UE, and a time-of-day.
4 . The central cloud server according to claim 2 , wherein the learning labels comprise initial access information, a Physical Cell Identity (PCID), a signal strength measurement of a Tx/Rx beam, a beam configuration, a transmission path, and an absolute radio-frequency channel number (ARFCN).
5 . The central cloud server according to claim 2 , wherein the machine learning model comprises a convolutional neural network (CNN).
6 . The central cloud server according to claim 1 , wherein the processor is further configured to obtain processing chain parameters from the plurality of edge devices, and wherein the processing chain parameters are additional parameters included in the learning labels in addition to the beam alignment information.
7 . The central cloud server according to claim 6 , wherein the processor is further configured to correlate the processing chain parameters with the obtained sensing information and the beam alignment information for the different times-of-day to update the generated connectivity enhanced database.
8 . The central cloud server according to claim 1 , wherein the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships comprise one or more of a transmit (Tx) beam information, a receive (Rx) beam information, a Physical Cell Identity (PCID), an absolute radio-frequency channel number (ARFCN), and a signal strength information associated with each of a Tx beam and an Rx beam of the plurality of edge devices.
9 . The central cloud server according to claim 1 , wherein the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships specify, for a set of the input features for a time-of-day of the different times-of-day, a beam index to set at a first edge device for uplink communication, a specific Physical Cell Identity (PCID) that indicates a gNB to connect to, or a selection of a Wireless Communication Network, a specific beam configuration to set, or a decision to connect to a base station directly or indirectly in a Non-Line-of-Sight (NLOS) path via a second edge device in a network of the plurality of edge devices, the decision is based on a current location of the second edge device, and each of the first edge device and the second edge device is one of the plurality of edge devices.
10 . The central cloud server according to claim 1 , wherein the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships specify, for a set of the input features for a time-of-day of the different times-of-day, a beam index to set at a first edge device for downlink communication, a selection of a Wireless Communication Network (WCN), a specific beam configuration to set, a power level of RF signal, or an expected time period to service one or more User Equipment (UEs) based on a current location of the first edge device, and the first edge device is one of the plurality of edge devices.
11 . The central cloud server according to claim 1 , wherein the correlation indicates, for the input features in the sensing information, initial access information suitable for a first edge device to service one or more User Equipment (UEs) in the surrounding area, and the first edge device is one of the plurality of edge devices.
12 . A method, comprising: periodically obtaining, by a central cloud server, sensing information from a plurality of edge devices at different locations; periodically obtaining, by the central cloud server, beam alignment information from the plurality of edge devices; and correlating, by the central cloud server, the obtained sensing information and the beam alignment information for different times-of-day to generate a connectivity enhanced database, wherein the connectivity enhanced database specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each edge device of the plurality of edge devices, and the correlation is based on the obtained sensing information as input features and the beam alignment information as learning labels.
13 . The method according to claim 12 , further comprising periodically training, by the central cloud server, a machine learning model for the different times-of-day on training data of the input features and parameters of the beam alignment information, wherein the connectivity enhanced database is generated further based on the trained machine learning model, and the machine learning model is trained to determine patterns that map the input features to the learning labels for the correlation.
14 . The method according to claim 13 , wherein the input features comprise a distance of each edge device of the plurality of edge devices from User Equipment (UE), weather condition, a UE location, a moving direction of the UE, and a time-of-day.
15 . The method according to claim 13 , wherein the learning labels comprise initial access information, a Physical Cell Identity (PCID), a signal strength measurement of a Tx/Rx beam, a beam configuration, a transmission path, and an absolute radio-frequency channel number (ARFCN).
16 . The method according to claim 13 , wherein the machine learning model comprises a convolutional neural network (CNN).
17 . The method according to claim 12 , further comprising obtaining, by the central cloud server, processing chain parameters from the plurality of edge devices, wherein the processing chain parameters are additional parameters included in the learning labels in addition to the beam alignment information.
18 . The method according to claim 17 , further comprising correlating, by the central cloud server, the processing chain parameters with the obtained sensing information and the beam alignment information for the different times-of-day to update the generated connectivity enhanced database.
19 . The method according to claim 12 , wherein the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships comprise one or more of a transmit (Tx) beam information, a receive (Rx) beam information, a Physical Cell Identity (PCID), an absolute radio-frequency channel number (ARFCN), and a signal strength information associated with each of a Tx beam and an Rx beam of the plurality of edge devices.
20 . The method according to claim 12 , wherein the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships specify, for a set of the input features for a time-of-day of the different times-of-day, a beam index to set at a first edge device for uplink communication, a specific Physical Cell Identity (PCID) that indicates a gNB to connect to, or a selection of a Wireless Communication Network, a specific beam configuration to set, or a decision to connect to a base station directly or indirectly in a Non-Line-of-Sight (NLOS) path via a second edge device in a network of the plurality of edge devices, the decision is based on a current location of the second edge device, and each of the first edge device and the second edge device is one of the plurality of edge devices.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION YB REFERENCE This Patent Application makes reference to, claims priority to, claims the benefit of, and is a Continuation Application of U.S. patent application Ser. No. 19/194,083, filed on Apr. 30, 2025, which is a Continuation Application of U.S. Pat. No. 12,363,623, issued on Jul. 15, 2025, which is a Continuation Application of U.S. Pat. No. 12,289,670, issued on Apr. 29, 2025, which is a Continuation Application of U.S. Pat. No. 11,356,936 issued on Jun. 7, 2022, which is a Continuation Application of U.S. Pat. No. 11,191,013, issued on Nov. 30, 2021. FIELD OF TECHNOLOGY Certain embodiments of the disclosure relate to wireless communication. More specifically, certain embodiments of the disclosure relate to an edge device and a method for handling service for multiple service providers. BACKGROUND Wireless telecommunication in modern times has witnessed advent of various signal transmission techniques and methods, such as use of beamforming and beam steering techniques, for enhancing capacity of radio channels. Latency and the high volume of data processing are considered prominent issues with next generation networks, such as 5G. Currently, the use of edge computing in the next generation networks, such as 5G and upcoming 6G, is an active area of research and many benefits has been proposed, for example, faster communication between vehicles, pedestrians, and infrastructure, and other communication devices. For example, it is proposed that close proximity of conventional edge devices to user equipment (UEs) may likely reduce the response delay usually suffered by UEs while accessing the traditional cloud. However, there are many open technical challenges for a successful and practical use of edge computing in the modern networks, especially in 5G or the upcoming 6G environment. In a first example, it is known that fast and efficient beam management mechanism may be a key enabler in advanced wireless communication technologies, for example, in millimeter wave (5G) or the upcoming 6G communications, to achieve low latency and high data rate requirements. One major technical challenge of the mmWave beamforming is the initial access latency. During the initial access phase, a UE and or a conventional repeater device need to scan multiple beams to find a suitable beam for attachment, for example, using the standard beam sweeping operation in the initial access phase. This process may introduce considerable latency depending on the number of beams in a beam book and a baseband decoding hardware latency. Such latency becomes even more critical for mobile systems (e.g., when UEs are in motion) in which the channel, and hence beams or base stations, such as a gNodeB (gNB), may be rapidly changing. For example, currently, an average mmWave gNB handover time is on the order of 10-20 seconds, assuming about 500 meter of cell radius and a UE (e.g., a vehicle or a UE in the vehicle) travelling at the speed of 50 miles per hour (MPH), which is not desirable. In a second example, Quality of Experience (QoE) is another open issue, which is a measure of a user's holistic satisfaction level with a service provider (e.g., Internet access, phone call, or other carrier network-enabled services). The challenge is how to ensure a seamless connectivity as well as QoE without significantly increasing infrastructure cost, which may be commercially unsustainable with present solutions. In a third example, heterogeneity may be another issue, where many UEs may use different interfaces, radio access technologies (3G, 4G, 5G, or upcoming 6G), computing technologies (e.g., hardware and operating systems) to communicate with the edge cloud. Such heterogeneity in wireless communication may further aggravate the challenges in developing a solution that is practical and upgradable across different environment. In yet another example, there is another challenge of high infrastructure cost incurred by each service provider separately to continuously upgrade their infrastructure to newer and advanced wireless communication technologies, for example, in 5G or the upcoming 6G communications, to achieve low latency and high data rate requirements. The high cost also substantially contributes to the delay in upgradation of their infrastructure, thereby leading a compromise on the QoE for the end users. Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present disclosure as set forth in the remainder of the present application with reference to the drawings. BRIEF SUMMARY OF THE DISCLOSURE An edge device and a method for handling service for multiple service providers for high performance and reliable communication, substantially as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims. Thes