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Communication system and method for high-reliability low-latency wireless … — Peltbeam Inc. (US12615695B2)

Peltbeam Inc. · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US12615695B2, Peltbeam Inc., Venkat Kalkunte, en, 2026

ABSTRACT

Abstract

An edge device including a processor that captures sensing information of surrounding area of the edge device, obtains a primary wireless connectivity option from a central cloud server for a first upcoming location along a first travel path to be traversed by the edge device, obtains a set of alternative wireless connectivity options from the central cloud server for the first upcoming location along the first travel path and one or more second upcoming locations along the first travel path, and selects one or more alternative wireless connectivity options from the set of alternative wireless connectivity options at the first upcoming location and the one or more second upcoming locations along the first travel path.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY 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. 18/630,937, filed on Apr. 9, 2024, which is a continuation application of U.S. Pat. No. 12,082,308, issued on Sep. 3, 2024, which is a Continuation application of U.S. Pat. No. 11,737,169, granted on Aug. 22, 2023, which is a Continuation-in-part application of U.S. Pat. No. 11,357,078, granted on Jun. 7, 2022, which is a continuation of U.S. Pat. No. 11,172,542, granted on Nov. 9, 2021. Each of the above-referenced applications are hereby incorporated herein by reference in its entirety

FIELD OF TECHNOLOGY

Certain embodiments of the disclosure relate to a wireless communication system. More specifically, certain embodiments of the disclosure relate to a communication system and a method for high-reliability low-latency wireless connectivity in mobility applications.

BACKGROUND

Wireless telecommunication in modern times has witnessed the advent of various signal transmission techniques and methods, such as the use of beamforming and beam steering techniques, for enhancing the 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 next-generation networks, such as 5G and upcoming 6G, is an active area of research, and many benefits have 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 successful and practical use of edge computing in the next generation networks, especially in 5G or the upcoming 6G environment.

In a first example, it is known that a 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 abeam 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 mmWavegNB handover time is on the order of 10-20 seconds, assuming about 500 meters of cell radius and a UE (e.g., a vehicle or a UE in the vehicle) traveling 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 quantitative 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 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), and even one or more carrier networks, to communicate with the edge cloud. Such heterogeneity in wireless communication may further aggravate the challenges in developing a solution that is portable, practical, and upgradable across a different environment.

In yet another example, how to consider the dynamic nature of surroundings is another open issue, especially for next-generation networks, such as mmWave communication, that may adversely impact reliability in the provisioning of consistent high-speed, low latency wireless connectivity. In certain scenarios, the known challenges of mmWave, namely signal loss, poor reach, and easy blockage by moving or stationary objects in surroundings are amplified, and uncertainty in achieving reliable wireless connectivity with QoE is increased as a result of the dynamic nature of surroundings, which is not desirable. Many communication systems work on the assumption that once the infrastructure is built, there is almost no change, which is not desirable as it may be erroneous, thereby impacting reliability, which is a prominent issue, especially in mobility applications, in which channels are rapidly changing due to movement of vehicles and UEs.

In another example, it is observed that batteries of UEs (e.g., smartphones) drain faster when the UEs are switched back and forth from the 4G to 5G radio access. In mobility scenarios, for example, when such UEs are present in a moving vehicle, the batteries of such UEs drain even faster, which is not desirable. Unfortunately, the above issues further add to this battery draining issue. For example, there is high battery consumption during the standard initial access search.

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

A communication system and a method for high-reliability low-latency wireless connectivity in mobility applications, substantially as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims.

These and other advantages, aspects, and novel features of the present disclosure, as well as details of an illustrated embodiment thereof, will be more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a network environment diagram illustrating various components of an exemplary communication system, in accordance with an exemplary embodiment of the disclosure.

FIG. 2 is a block diagram illustrating components of an exemplary central cloud server, in accordance with an embodiment of the disclosure.

FIG. 3 is a block diagram illustrating components of an exemplary RSU device, in accordance with an embodiment of the disclosure.

FIG. 4 A is an illustration that depicts an exemplary arrangement of one or more edge devices on a vehicle with exemplary components, in accordance with an embodiment of the disclosure.

FIG. 4 B is a block diagram illustrating components of an exemplary edge device arranged on a vehicle, in accordance with an embodiment of the disclosure.

FIG. 5 is a block diagram illustrating components of an exemplary user equipment, in accordance with an embodiment of the disclosure.

FIG. 6 is a block diagram illustrating a first exemplary scenario for implementation of the central cloud server for high-speed, low-latency wireless connectivity in mobility application, in accordance with an embodiment of the disclosure.

FIGS. 7 A and 7 B illustrate exemplary scenarios for implementation of the communication system and method high-speed low-latency wireless connectivity in mobility application, in accordance with an embodiment of the disclosure.

FIGS. 8 A and 8 B collectively is a flowchart that illustrates an exemplary method for high-speed, low-latency wireless connectivity in mobility application, in accordance with an embodiment of the disclosure.

FIG. 9 is a flowchart that illustrates an exemplary method for high-speed, low-latency wireless connectivity in mobility application, in accordance with another embodiment of the disclosure.

FIGS. 10 A and 10 B collectively is a flowchart that illustrates an exemplary method for high-reliability low-latency wireless connectivity in mobility application, in accordance with another embodiment of the disclosure.

FIG. 11 is a flowchart that illustrates an exemplary method for high-reliability low-latency wireless connectivity in mobility application, in accordance with yet another embodiment of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Certain embodiments of the disclosure may be found in a communication system and a method for high-reliability low-latency wireless connectivity in mobility applications. The communication system and the method of the present disclosure significantly reduces the latency involved in the initial access phase by making an edge device (e.g., a user equipment (UE) or a repeater device arranged at a vehicle) bypass a standard initial-access search. For example, the existing average mm WavegNB handover time that is on the order of 10-20 seconds for a moving device is significantly reduced by approximately 60-90% depending on the location, the speed, and the orientation of the moving device (e.g., the repeater device in the vehicle or the UE in the vehicle). Such reduction in the gNB handover time is achieved using an intelligent database that is trained previously. The intelligent database may be referred to as a connectivity enhanced database that specifies a plurality of specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices independent of a plurality of different wireless carrier networks of different service providers. A central cloud server and a plurality of inference servers of the communication system support the plurality of different wireless carrier networks, including different interfaces, radio access technologies, computing technologies (e.g., hardware and operating systems) and are easily upgradable without any need to change the infrastructure. Thus, the central cloud server in coordination with one or more inference servers of the plurality of inference servers, the one or more edge devices, one or more network nodes (e.g., RSUs including small cells and base stations) ensures seamless connectivity as well as Quality of Experience (QoE) without significantly increasing infrastructure cost separately for the plurality of different wireless carrier networks. Moreover, the central cloud server takes into account comprehensive sensing information surrounding each edge device. Thus, the dynamic nature of surroundings (e.g., any change in surroundings that has the potential to adversely impact signal propagation, cause signal loss, poor reach, or signal blockage by an object, such as a moving object or a stationary object, in the surroundings) is proactively handled and mitigated by the central cloud server by communicating not only a primary wireless connectivity option (one specific initial access information), but also provides alternative wireless connectivity options as fallback options which can be readily used to connect to a new gNB or to the existing attached gNB with new beam configuration for reduced latency when there is a signal loss or a weak signal. The central cloud server may be guided by the velocity information of the edge device, which in turn may trigger the central cloud server to elastically alter how many directives (or instructions for alternative wireless connectivity options) it queues to the edge device. Such communication by the central cloud server may be done ahead of time (i.e., much before the actual time of a handover to a new gNB) according to a predicted travel path to be undertaken by the edge device in motion that enables easy handling and mitigation of any adverse impact on signal propagation due to the dynamic nature of surroundings or overcoming any unforeseen signal obstruction situations (e.g., temporary signal loss in a tunnel) for consistent high-performance communication. In the following description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments of the present disclosure.

FIG. 1 is a network environment diagram illustrating various components of an exemplary communication system, in accordance with an exemplary embodiment of the disclosure. With reference to FIG. 1 , there is shown a network environment diagram of a communication system 100 that includes a central cloud server 102 and a plurality of edge devices 104 . In the network environment diagram, there is further shown one or more user equipment (UEs) 106 , a plurality of base stations 108 , and a plurality of different wireless carrier networks (WCNs) 110 , such as a first WCN 110 A of a first service provider and a second WCN 110 B of a second service provider. The plurality of edge devices 104 may include a first type of edge devices 104 A and a second type of edge devices 104 B. The first type of edge devices 104 A may be edge devices that are movable, for example, one or more edge devices (e.g., edge devices 112 A and 112 B) arranged at each vehicle of a plurality of vehicles 116 . The second type of edge devices 104 B may be the edge devices that are immobile and deployed at different locations, such as a plurality of RSU devices 114 . The communication system 100 may further include a plurality of inference servers 118 (such as inference servers 118 A, 118 B, . . . , 118 N) that may be communicatively coupled to the central cloud server 102 via an out-of-band communication network 120 and/or one or more in-band communication networks associated with the plurality of different WCNs 110 .

The central cloud server 102 includes suitable logic, circuitry, and interfaces that may be configured to communicate with the plurality of edge devices 104 , the one or more UEs 106 , the plurality of base stations 108 , the plurality of vehicles 116 , and the plurality of inference servers 118 . In an example, the central cloud server 102 may be a remote management server that is managed by a third party different from the service providers associated with the plurality of different WCNs 110 . In another example, the central cloud server 102 may be a remote management server or a data center that is managed by a third party, or jointly managed, or managed in coordination and association with one or more of the plurality of different WCNs 110 . In an implementation, the central cloud server 102 may be a master cloud server or a master machine that is a part of a data center that controls an array of other cloud servers communicatively coupled to it for load balancing, running customized applications, and efficient data management.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

FIELD OF TECHNOLOGY

BACKGROUND

BRIEF SUMMARY OF THE DISCLOSURE

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a network environment diagram illustrating various components of an exemplary communication system, in accordance with an exemplary embodiment of the disclosure.

FIG. 2 is a block diagram illustrating components of an exemplary central cloud server, in accordance with an embodiment of the disclosure.

FIG. 3 is a block diagram illustrating components of an exemplary RSU device, in accordance with an embodiment of the disclosure.

FIG. 4 A is an illustration that depicts an exemplary arrangement of one or more edge devices on a vehicle with exemplary components, in accordance with an embodiment of the disclosure.

FIG. 4 B is a block diagram illustrating components of an exemplary edge device arranged on a vehicle, in accordance with an embodiment of the disclosure.

FIG. 5 is a block diagram illustrating components of an exemplary user equipment, in accordance with an embodiment of the disclosure.

FIG. 9 is a flowchart that illustrates an exemplary method for high-speed, low-latency wireless connectivity in mobility application, in accordance with another embodiment of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Each edge device of the plurality of edge devices 104 includes suitable logic, circuitry, and interfaces that may be configured to communicate with the central cloud server 102 . The plurality of edge devices 104 may include the first type of edge devices 104 A and the second type of edge devices 104 B. The first type of edge devices 104 A may be the edge devices 112 A, 112 B, . . . 112 N, which are movable. In an example, some edge devices, such as a repeater device, may be installed in a vehicle, and thus the location of such repeater device may vary rapidly when the vehicle is in motion. In some implementations, an edge device may be a part of a telemetric unit of a vehicle. In some implementations, the first type of edge devices 104 A may further include UEs controlled by the central cloud server 102 . In such a case, the UEs may be controlled out-of-band, for example, in a management plane, by the central cloud server 102 . Such one or more edge devices that are movable and/or associated vehicles (such as the vehicles 116 A, 116 B, . . . , 116 N) are referred to as the first type of edge devices 104 A. Examples of the first type of edge devices 104 A may include, but may not be limited to, an XG-enabled repeater device, an XG-enabled relay device, or an XG-enabled mobile edge communication device, where the XG corresponds to 5G or 6G communication. The second type of edge devices 104 B may be edge devices that are immobile and deployed at different locations. The plurality of RSU devices 114 may be the second type of edge devices 104 B. Examples of the second type of edge devices 104 B may include, but are not limited to, an XG-enabled repeater device, an XG-enabled small cell, an XG-enabled customer premise equipment (CPE), an XG-enabled relay device, an XG-enabled RSU device, or an XG-enabled edge communication device deployed at a fixed location.

Each of one or more UEs 106 may correspond to telecommunication hardware used by an end-user to communicate. Alternatively stated, the one or more UEs 106 may refer to a combination of a mobile equipment and subscriber identity module (SIM). Each of the one or more UEs 106 may be a subscriber of at least one of the plurality of different WCNs 110 . Examples of the one or more UEs 106 may include, but are not limited to a smartphone, a virtual reality headset, an augmented reality device, an in-vehicle device, a wireless modem, a home router, a cable or satellite television set-top box, a VoIP station, or any other customized hardware for telecommunication.

Each of the plurality of base stations 108 may be a fixed point of communication that may communicate information, in form of a plurality of beams of RF signals, to and from communication devices, such as the one or more UEs 106 and the plurality of edge devices 104 . Multiple base stations corresponding to one service provider may be geographically positioned to cover specific geographical areas. Typically, bandwidth requirements serve as a guideline for a location of a base station based on the relative distance between the plurality of UEs and the base station. The count of base stations depends on population density and geographic irregularities, such as buildings and mountain ranges, which may interfere with the plurality of beams of RF signals. In an implementation, each of the plurality of base stations 108 may be a gNB. In another implementation, the plurality of base stations 108 may include eNBs, Master eNBs (MeNBs) (for non-standalone mode), and gNBs.

Each of the plurality of different WCNs 110 is owned, managed, or associated with a mobile network operator (MNO), also referred to as a mobile carrier, a cellular company, or a wireless service provider that provides services, such as voice, SMS, MMS, Web access, data services, and the like, to its subscribers, over a licensed radio spectrum. Each of the plurality of different WCNs 110 may own or control elements of network infrastructure to provide services to its subscribers over the licensed spectrum, for example, 4G LTE, or 5G spectrum (FR1 or FR2). For example, the first base station 108 A may be controlled, managed, or associated with the first WCN 110 A, and the second base station 108 B may be controlled, managed, or associated with the second WCN 110 B, different from the first WCN 110 A. The plurality of different WCNs 110 may also include mobile virtual network operators (MVNO).

Each of the plurality of vehicles 116 includes suitable logic, circuitry, and interfaces that may be configured to communicate with the central cloud server 102 , for example, via the one or more edge devices arranged at each vehicle. In some implementations, one edge device, such as the edge device 112 A, may be arranged at a given vehicle, such as the vehicle 116 A. In an example, the edge device may be a part of a telemetric unit of the vehicle. In some implementations, two edge devices, such as the edge devices 112 A and 112 B, may be arranged on some vehicles. In such a case, the edge devices 112 A and 112 B may be arranged at different positions in the vehicle. The plurality of vehicles 116 may include autonomous vehicles, semi-autonomous vehicles, and/or non-autonomous vehicles.

The plurality of inference servers 118 may be distributed at a plurality of different geographical zones such that each inference server serves a different geographical zone. Each of the plurality of inference servers 118 may be configured to obtain a subset of information from a connectivity enhanced database according to a corresponding geographical zone of the plurality of different geographical zones served by each of the plurality of inference servers 118 . Each of the plurality of inference servers 118 includes suitable logic, circuitry, and interfaces that may be configured to receive a real-time or a near real-time request from an edge device of the plurality of edge devices 104 within its geographical zone. In an example, the real-time or the near real-time request may comprise one or more input features corresponding to sensing information of a given vehicle. The plurality of edge devices 104 corresponds to the plurality of RSU devices 114 (i.e., the second type of edge devices 104 B) and the one or more edge devices (i.e., one or more of the first type of edge devices 104 A) arranged on each vehicle of the plurality of vehicles 116 . Based on the received request, a given inference server, such as the inference server 118 A, may be further configured to communicate a response within less than a specified threshold time to each of the one or more RSU devices, wherein the response comprises wireless connectivity enhanced information including a specific initial access information to each of the one or more RSU devices to bypass the initial access-search on the one or more RSU devices as well as the first edge device of the given vehicle.

Beneficially, the central cloud server 102 and the plurality of edge devices 104 exhibit a decentralized model that not only brings cloud computing capabilities closer to UEs in order to reduce latency but also manifests several known benefits for various service providers associated with the plurality of different WCNs 110 . For example, it reduces backhaul traffic by provisioning content at the edge, distributes computational resources geographically in different locations (e.g., on-premises mini cloud, central offices, customer premises, etc.,) depending on the use case requirements, and improves the reliability of a network by distributing content between edge devices and the centralized cloud server 102 . Apart from these and other known benefits (or inherent properties) of edge computing, the central cloud server 102 improves and solves many open issues related to the convergence of edge computing and the next-generation wireless networks, such as 5G or upcoming 6G. The central cloud server 102 significantly improves the beam management mechanism of 5G new radio (NR), true 5G, and creates a platform for upcoming 6G communications, to achieve low latency and high data rate requirements. Based on the various information acquired from the plurality of vehicles 116 via the one or more edge devices (i.e., the first type of edge devices 104 A) arranged on each vehicle of the plurality of vehicles 116 and the one or more network nodes, such as the plurality of RSU devices 114 (i.e., the second type of edge devices 104 B) and the plurality of base stations 108 , over a period of time, the central cloud server 102 creates a connectivity enhanced database that specifies a plurality of specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices 104 independent of the plurality of different WCNs 110 . This removes the complexity and substantially reduces the initial access latency as the standard beam sweeping operation in the initial access phase is bypassed and is not required to be performed at the end-user device (e.g., UEs) or edge devices, which in turn improves network performance of all associated WCNs of the plurality of different WCNs 110 . The central cloud server 102 is able to handle heterogeneity in wireless communication in terms of different interfaces, radio access technologies (3G, 4G, 5G, or upcoming 6G), computing technologies (e.g., hardware and operating systems), and even one or more carrier networks used by the one or more UEs 106 . Moreover, the central cloud server 102 takes into account the dynamic nature of surroundings holistically by use of the sensing information obtained from the plurality of edge devices 104 in real-time or near real-time to proactively avoid any adverse impact on reliability due to any sudden signal blockage, signal fading, signal scattering, or signal loss, thereby provisioning consistent high-reliability, high-speed, low latency wireless connectivity. Thus, the central cloud server 102 manifest higher QoE as compared to existing systems. Moreover, the central cloud server 102 provides the first edge device (e.g., the edge device 112 A) with the primary wireless connectivity option (i.e., a best initial access information) in real-time or near real-time directly or via by a relevant inference server much ahead of time before an actual handover to a new gNB is expected, the disclosed communication system is able to proactively handle and avoid existing signaling overhead issues that result from quick variations of wireless channels in mobility applications, such as V2X systems. Furthermore, advantageously, the central cloud server 102 further provides two or more choices, i.e., a set of alternative wireless connectivity options, for example, a first, second, third, and fourth choice for wireless connectivity so that the first edge device (e.g., the edge device 112 A) can continue with a mmWave connection with the least amount of service disruption. Alternatively stated, when the primary choice fails (i.e., the first communicated wireless connectivity enhanced information that includes a first specific initial access information (also referred to as the primary wireless connectivity option) is not usable for some unforeseen reasons, like loss of signal in a tunnel), other alternative wireless connectivity options can be selected by the first edge device to maintain continuous 5G connectivity for enhanced QoE. Thus, having alternative wireless connectivity options as back up act as a powerful technique to maintain consistent 5G connectivity irrespective of an internal beam acquisition process of the first edge device (e.g., the edge device 112 A). Moreover, as such alternative wireless connectivity options comprises specific initial access information, the standard beam acquisition process is shortened, i.e., the time to scan and acquire new initial access information is shortened, and consequently, failure detection and recovery therefrom using the provided multiple alternative wireless connectivity options plays a prominent role in increasing the QoE. Additionally, the one or more edge devices arranged on each vehicle substantially reduces the battery draining issue of the one or more UEs 106 when present in a vehicle in motion.

FIG. 2 is a block diagram illustrating components of an exemplary central cloud server, in accordance with an embodiment of the disclosure. FIG. 2 is explained in conjunction with elements from FIG. 1 . With reference to FIG. 2 , there is shown a block diagram 200 of the central cloud server 102 . The central cloud server 102 may include a processor 202 , a network interface 204 , and a primary storage 206 . The primary storage 206 may further include sensing information 208 and processing chain parameters 210 . In an implementation, the primary storage 206 may further include position information 212 of a plurality of network nodes that includes the plurality of RSU devices 114 and the plurality of base stations 108 . There is further shown a machine learning model 214 and connectivity enhanced database 216 .

In operation, there may be a training phase and an inference phase. In the training phase, the processor 202 may be configured to obtain sensing information 208 from the plurality of vehicles 116 as the plurality of vehicles 116 move along a first travel path. Each vehicle, such as the vehicle 116 A, may comprise one or more edge devices (e.g., one of or more of the first type of edge devices 104 A) arranged such that a donor side of each edge device faces an exterior of each vehicle to communicate with one or more network nodes and a service side of each edge device faces an interior of each vehicle to service the one or more UEs 106 within each vehicle of the plurality of vehicles 116 . Each of the plurality of vehicles 116 may be configured to communicate the sensing information 208 to the central cloud server 102 , for example, via the one or more edge devices arranged at each vehicle.

In some implementations, one edge device, such as the edge device 112 A, may be arranged at some vehicles, such as the vehicle 116 A. For example, a single edge device, such as the edge device 112 A, may be arranged at a roof panel of the vehicle 116 A such that its donor side faces an exterior of the vehicle 116 A to communicate with one or more network nodes, such as one or more base stations of the plurality of base stations 108 , or the one or more RSUs of the plurality of RSU devices 114 . A service side of the edge device, such as the edge device 112 A, may be arranged to service components of the vehicle 116 A and/or the one or more UEs 106 associated with the vehicle 116 A, such as a sensor system of the vehicle 116 A, an in-vehicle device, smartphones of users inside the vehicle 116 A, an in-vehicle infotainment system, or other components of a vehicle where connectivity is desired. In an example, the edge device, such as the edge device 112 A, may be a part of the telemetric unit of the vehicle, such as the vehicle 116 A. In some implementations, two edge devices, such as the edge devices 112 A and 112 B, may be arranged on some vehicles, such as the vehicle 116 B. In such a case, the edge devices 112 A and 112 B may be arranged at different positions of the vehicle 116 B. The plurality of vehicles 116 may include autonomous vehicles, semi-autonomous vehicles, and/or non-autonomous vehicles.

In accordance with an embodiment, at least the first UE 106 A of the one or more UEs 106 may comprise an application that may cause the first UE 106 A to be designated as a known user to the central cloud server 102 . An exemplary application is described, for example, in FIG. 5 . The first travel path may share a plurality of geographical areas that remain covered by a coverage area of one base station, uncovered by any base station, or partially or mutually covered by the plurality of base stations 108 , such as the first base station 108 A and the second base station 108 B, of different service providers (i.e., the plurality of different WCNs 110 ).

As each vehicle, such as the vehicle 116 A or the vehicle 116 B, may comprise one or more edge devices (e.g., one of or more of the first type of edge devices 104 A) mounted on it, a location of each of the one or more edge devices may change rapidly when a corresponding vehicle on which the one or more edge devices is installed is in motion. The one or more edge devices (e.g., one of or more of the first type of edge devices 104 A) may periodically sense its surroundings and communicate the sensed data as the sensing information 208 , to the central cloud server 102 . The machine learning model 214 of the central cloud server 102 may be periodically (e.g., daily and for different times-of-day) trained on data points that are uploaded to the central cloud server 102 .

In accordance with an embodiment, the sensing information 208 may comprise a location of each of the one or more edge devices arranged on each vehicle of the plurality of vehicles 116 , a moving direction of the plurality of vehicles 116 , a time-of-day, traffic information, road information, construction information, traffic light information, and information from one or more in-vehicle sensing devices of the plurality of vehicles 116 . In some implementations, the sensing information 208 may further include a location of the one or more UEs 106 . The location of the one or more UEs 106 may be obtained in a case where each of the one or more UEs 106 may have the application installed in it. The central cloud server 102 obtains the sensing information 208 and stores the data points of such sensing information 208 as input features.

In some implementations, the one or more edge devices of the first type of edge devices 104 A mounted on each vehicle may be configured to utilize external sensing devices, such as light detection and ranging (Lidar), camera, accelerometer, Global Navigation Satellite System (GNSS), gyroscope, or Internet-of-Things (IoT) devices (e.g., video surveillance devices, road-side sensor systems for measuring speed, local road conditions, local traffic, and the like) located within its communication range to acquire sensing information 208 from such external devices. For example, an edge device may be a repeater device mounted on a vehicle and communicatively coupled to different in-vehicle sensors via an in-vehicle network so as to acquire the sensing information 208 from such in-vehicle sensors (i.e., the external sensors) in real-time or near time.

In some implementations, the sensing information 208 may further be obtained from the second type of edge devices 104 B, such as the plurality of RSU devices 114 . Each edge device of the plurality of edge devices 104 may use its own sensing mechanism, such as a sensing radar, to sense its surrounding environment. In such a case, when the sensing mechanism is present, the sensing information 208 may further be obtained from the second type of edge devices 104 B, such as the plurality of RSU devices 114 . In some implementations, the sensing information 208 may be further obtained from UEs (e.g., smartphones) controlled by the central cloud server 102 . As the sensing information 208 is obtained periodically from various edge devices, such as the first type of edge devices 104 A and the second type of edge devices 104 B, of the plurality of edge devices 104 , changes in the surroundings of each edge device is adequately captured and relayed to the central cloud server 102 .

In accordance with an embodiment, the processor 202 may be further configured to generate supplementary information as insights based on a cross-correlation of data points of the obtained sensing information 208 . When such data points of the sensing information 208 are cross-correlated with each other, supplementary information may be derived as insights by the central cloud server 102 . For example, when traffic information of a surrounding area of a given RSU device, such as the RSU device 114 A, having a first position is correlated with surrounding information at different times-of-day over a period of time, the processor 202 of the central cloud server 102 may be configured to determine a trend and a load associated with the given RSU device, such as the RSU device 114 A, (and similarly for other RSU devices of the plurality of RSU devices 114 ) that may indicate an average number of vehicles and/or UEs expected to be serviced by the given RSU device, such as the RSU device 114 A, at different times-of-day, one or more peak load time periods, one or more off-peak time periods. The processor 202 may be further configured to determine how many RSU devices are active or not active, which RSU devices may be employed to increase the coverage and data throughput and reduce latency, and the like.

In another example, more supplementary information may be derived as insights taking into account traffic information, road information, construction information, and traffic light information, and other sensed information. Each RSU device of the plurality of RSU devices 114 may use its own sensing mechanism, such as sensing radar, to sense its surrounding environment and map its surrounding three-dimensional (3D) environment to generate a 3D environmental representation. The 3D environmental representation may indicate movable and immobile physical structures in the surrounding area of each of the RSU devices 114 .

In accordance with an embodiment, the sensing information 208 may further comprise a distance of each of the one or more edge devices (e.g., of the first type of edge devices 104 A) arranged on each vehicle of the plurality of vehicles 116 from other mobile objects and immobile objects in the surrounding area of each of the plurality of vehicles 116 . In an implementation, the distance of the one or more edge devices (e.g., of the first type of edge devices 104 A) arranged on each vehicle from other movable and immobile physical structures in the surrounding area may be determined at each of the one or more edge devices, such as the edge devices 112 A and 112 B, and then communicated to the central cloud server 102 as a part of the sensing information 208 . In another implementation, the processor 202 may be further configured to determine the distance of each the one or more edge devices arranged at each vehicle from its surrounding objects, such as other vehicles, buildings, or edges of a building, distance of one or more serving base stations of the plurality of base stations 108 , trees, and other immobile physical structures (such as reflective objects) or other mobile objects. Moreover, Lidar information from vehicles, information from a navigation system (such as maps, for example, identifying cross-sections of streets), satellite imagery of buildings of a surrounding area, bridges, any signal obstruction from a change in construction structure, etc., may be stored in the cloud, such as the central cloud server 102 .

In accordance with an embodiment, each of the plurality of RSU devices 114 may be further configured to determine a distance from the one or more UEs 106 and other movable objects, such as one or more vehicles of the plurality of vehicles 116 , and immobile physical structures in the surrounding area of each of the RSU devices 114 . Such determined distance by each of the plurality of RSU devices 114 may be communicated to the central cloud server 102 . In some implementations, the central cloud server 102 may be configured to determine such distance based on the position information 212 received from the plurality of the RSU devices 114 . Additionally, the processor 202 of the central cloud server 102 may be further configured to cross-correlate the distances using the generated 3D environmental representation for a given surrounding area of a given RSU device for higher accuracy.

The machine learning model 214 of the central cloud server 102 may be periodically (e.g., daily and for different times of day) updated on such data points in real-time or near time. The central cloud server 102 may be further configured to cause the machine learning model 214 to find correlation among such data points to be used for a plurality of predictions and formulate rules to establish, maintain, and select one or more RSU devices in advance for various traffic scenarios to serve the one or more edge devices of the first type of edge devices 104 A arranged at each vehicle (or the one or more UEs directly) and to identify improved (e.g., optimal) signal transmission paths to reach to the one or more edge devices of the first type of edge devices 104 A arranged at each vehicle (or the one or more UEs directly) for efficient handover for wireless connectivity at a later stage (i.e., in the inference phase). Based on the sensing information 208 obtained from the plurality of vehicles 116 (and optionally from the plurality of RSU devices 114 ), the processor 202 may be further configured to detect where reflective objects are located and used that information in the radiation pattern of the RF signals, such as 5G signals. The sensing information 208 may be used to make a radiation pattern that is correlated to areas such that the communicated RF signals are not reflected back. This means that when one or more beams of RF signals are communicated from the one or more edge devices arranged at each vehicle and/or the plurality of RSU devices 114 , comparatively significantly lower or almost negligible RF signals are reflected back to the one or more edge devices of the first type of edge devices 104 A and the plurality of RSU devices 114 of the second type of edge devices 104 B. The location of the reflective objects and the correlation of the areas associated with reflective objects with the radiation pattern to design enhanced or most suited beam configurations may be further used by the processor 202 to formulate rules for later use.

In accordance with an embodiment, the sensing information 208 may further comprise weather information. The processor 202 may be further configured to utilize the weather information to determine one or more changes in a performance state in different weather conditions of each of the one or more edge devices (i.e., the one or more edge devices arranged at each vehicle) across the plurality of geographical areas along the first travel path. It is known that more attention is provided in the region between 30-300 GHz frequencies due to the large bandwidth which is available in this region to enable the plurality of different WCNs 110 to cope with the increasing demand for higher data rates and ultra-low latency services. However, the signals at frequencies above 30 GHz may not propagate for long distances as those below 30 GHz. Moreover, there is signal attenuation due to weather factors, such as humidity, rain, ice, different types of storms, and even there is a difference observed during summer and winter on the signal power level. For example, the signal loss difference between winter and summer for 28 GHz may be about 1 dB, about 2 dB for 37 GHZ, about 4 dB for 60 GHz. Such losses may increase with frequency and distance. The processor 202 utilizes such weather information to determine one or more changes in a performance state of each of the one or more edge devices (i.e., the one or more edge devices arranged at each vehicle) as well as the plurality of RSU devices 114 across the plurality of geographical areas along the first travel path in different weather conditions. Accordingly, the processor 202 by use of the machine learning model 214 may be configured to learn a correlation between different weather condition and signal power level and other performance state of each of the one or more edge devices arranged at each vehicle in servicing the one or more UEs 106 . Accordingly, the processor 202 may be further configured to formulate rules to establish, maintain, and select one or more RSU devices in advance to mitigate signal losses in various weather conditions to serve the one or more edge devices arranged at each vehicle (or the one or more UEs 106 directly) and to identify improved (e.g., optimal) signal transmission paths to reach to the one or more edge devices arranged at each vehicle (or the one or more UEs 106 directly) at a later stage (i.e., in the inference phase). For example, the processor 202 may be further configured to cause the one or more edge devices arranged at each vehicle as well as the one or more RSU devices to select the most appropriate beam configurations or radiation pattern in real-time or near real-time in accordance with the weather condition obtained as a part of the sensing information 208 (i.e., in the inference phase).

In accordance with an embodiment, the processor 202 may be further configured to obtain processing chain parameters 210 from the donor side of each edge device of the plurality of vehicles 116 as the plurality of vehicles 116 move along the first travel path. As the one or more edge devices, such as the edge devices 112 A and 112 B, arranged at each vehicle may be in motion, the changes in a channel may be more prominent at the donor side that faces the one or more network nodes, such as the first base station 108 A, the second base station 108 B, and the one or more RSU devices, such as the RSU devices 114 A and 114 B. In accordance with an embodiment, the processing chain parameters 210 obtained from each edge device of the plurality of vehicles 116 may comprise information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, radio blocks information, and modem information of each edge device of the plurality of vehicles 116 .

In some implementations, the processor 202 may be further configured to obtain processing chain parameters 210 from the plurality of edge devices 104 that includes both the first type of edge devices 104 A (i.e., one or more edge devices arranged at each vehicle) and the second type of edge devices 104 B, such as the plurality of RSU devices 114 . Thus, the processing chain parameters 210 include information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, radio block information, and modem information of the plurality of edge devices 104 . The central cloud server 102 may be configured such that it has access to certain defined elements or all elements of one or more signal processing chains of each of the plurality of edge devices 104 . For example, each of an uplink RF signal processing chain and a downlink RF signal processing chain may include a cascading receiver chain for signal reception, which includes elements, such as a set of low noise amplifiers (LNA), a set of receiver front end phase shifters, and a set of power combiners. Similarly, each of the uplink RF signal processing chain and the downlink RF signal processing chain may further include a cascading transmitter chain for baseband signal processing or digital signal processing for signal transmission, which includes elements such as a set of power dividers, a set of phase shifters, a set of power amplifiers (PA). There may be other elements and circuits like mixers, phase-locked loops (PLL), frequency up-converters, frequency down-converters, a filter bank that may include one or more filters, such as filters for channel selection or other digital filters for noise cancellation or reduction. The central cloud server 102 may be configured to securely access, monitor, and configure the information associated with such elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device to optimize each radio blocks and overall radio frequency signals, such as 5G signals.

In a first example, the central cloud server 102 may remotely access elements of the one or more signal processing chains, like the set of phase shifters, and utilize that, for example, to train the machine learning model 214 , and optimize every block of an RF signal including phase (e.g., can control the phase-shifting), etc. In a second example, the central cloud server 102 may remotely access information associated with elements, such as a set of LNAs to train the machine learning model 214 , and utilize that information, for example, to learn and control amplification of input RF signals received by an antenna array, such as the one or more first antenna arrays or the one or more second antenna arrays, in order to amplify input RF signals, which may have low-power, without significantly degrading corresponding signal-to-noise (SNR) ratio in the inference phase. In a third example, the central cloud server 102 may remotely access information (e.g., phase values of one or more input RF signals) associated with elements, such as set of phase shifters, to train the machine learning model 214 , and control adjustment in phase values of the input RF signals, till combined signal strength value of the received input RF signals, is maximized to design beams in the inference phase. In a fourth example, the central cloud server 102 may be configured to train the machine learning model 214 with parameters (e.g., amplifier gains, and phase responses) associated with one or more first antenna arrays (e.g., the one or more first antenna arrays 306 or 426 of FIGS. 3 and 4 B ) or one or more second antenna arrays (e.g., the one or more second antenna arrays 310 or 430 of FIGS. 3 and 4 B ), and later use learning in the inference phase to send control signals to remotely configure or control such parameters. In a fifth example, the central cloud server 102 may be configured to access beamforming coefficients from elements of the one or more signal processing chains to train the machine learning model 214 and use such learning to configure, and control, and adjust beam patterns to and from each of the plurality of edge devices 104 (i.e., the one or more edge devices of each vehicle as well as the plurality of RSU devices 114 ). In a sixth example, as the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may configure dynamic partitioning of a plurality of antenna elements of an antenna array into a plurality of spatially separated antenna sub-arrays to generate multiple beams in different directions to establish independent communication channels with the one or more UEs 106 at the same time or in a different time slot. In a seventh example, since the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may be further configured to accurately determine a transmit (Tx) beam information, a receive (Rx) beam information, a Physical Cell Identity (PCID), and an absolute radio-frequency channel number (ARFCN), and a signal strength information associated with each of Tx beam and the Rx beam of the plurality of edge devices 104 for the plurality of different WCNs 110 . In an eighth example, since the central cloud server 102 has information associated with elements of one or more cascaded receiver chains and one or more cascaded transmitter chains of each edge device, the central cloud server 102 may configure and instruct an edge device (e.g., mounted at each vehicle) for a suitable adjustment of a power back-off to minimize (i.e., substantially reduce) the impact of interference (echo or noise signals) and hence only use as much power as needed to achieve low error communication with one or more base stations in the uplink or the one or more UEs 106 in the downlink communication. In accordance with an embodiment, the central cloud server 102 may be further configured to configure, monitor, and/or provide management, monitoring, and/or configuration services to, various layers of each of the plurality of edge devices 104 to optimize blocks of radio and perform Radio access network optimization to improve coverage, capacity, and service quality.

It is known and specified in 3GPP that a radio frame of a 5G NR frame structure may include ten sub-frames, where each sub-frame, includes one or more slots based on different configurations. In an example, a sub-frame may include one slot, where each slot may include 14 symbols (e.g., 14 OFDM symbols). In a case where a sub-frame has two slots, then the radio frame has 20 slots. Similarly, in a case where the sub-frame has four slots, then the radio frame has 40 slots, where the number of OFDM symbols within a slot is 14. It is also known that NR Time-division duplexing (TDD) uses flexible slot configuration, where the flexible symbol can be configured either for uplink or for downlink transmissions.

In an implementation, the central cloud server 102 may obtain radio block information and may access decoded control information from each of the plurality of edge devices 104 (i.e., one or more edge devices arranged at each vehicle and the second type of edge devices 104 B, such as the plurality of RSU devices 114 ). The decoded control information may include (or indicates) a periodicity and a downlink/uplink cycle ratio, a time division duplex (TDD) pattern, an NR TDD slot format, or a plurality of NR TDD slot formats in a sequence. In accordance with an embodiment, the central cloud server 102 may obtain a physical cell identifier (PCID), an absolute radio-frequency channel number (ARFCN), and other properties of the plurality of base station of the plurality of different WCNs 110 through the network (e.g., 4G LTE, 5G NR, Internet, or any other wireless communication network). The central cloud server 102 may further receive a channel quality indicator an

CLAIMS

Claims ( 17 )

What is claimed is:

1 . An edge device, comprising:

a processor configured to:

capture sensing information of a surrounding area of the edge device;

obtain a primary wireless connectivity option from a central cloud server for a first upcoming location along a first travel path to be traversed by the edge device;

obtain a set of alternative wireless connectivity options from the central cloud server for the first upcoming location along the first travel path and one or more second upcoming locations along the first travel path, wherein each alternative wireless connectivity option of the set of alternative wireless connectivity options includes a respective specific initial access information that enables the edge device to bypass an initial access-search;

select both, at least one alternative wireless connectivity option from the set of alternative wireless connectivity options at the first upcoming location and the one or more second upcoming locations along the first travel path, based on one of:

a failure of the primary wireless connectivity option, or

a performance state of a cellular connectivity of the edge device, to a first base station via the primary wireless connectivity option of the edge device in motion, is predicted to become less than a threshold performance value; and

bypass, based on the respective specific initial access information of the selected at least one alternative wireless connectivity option, the initial access-search at the edge device to maintain the cellular connectivity with at least one of a plurality of base stations.

2 . The edge device according to claim 1 , wherein the processor is further configured to periodically communicate the captured sensing information to the central cloud server.

3 . The edge device according to claim 1 , wherein the captured sensing information comprises one or more of: a location of the edge device, a moving direction of the edge device in the first travel path, a time-of-day, a current location of the edge device in the first travel path, upcoming locations of the edge device in the first travel path, upcoming geographical areas of the first travel path, traffic information, road information, construction information, and traffic light information.

4 . The edge device according to claim 1 , wherein

the plurality of base stations includes the first base station.

5 . The edge device according to claim 1 , wherein the processor is further configured to obtain an updated set of alternative wireless connectivity options as fallback options from the central cloud server in response to a deviation in the first travel path.

6 . The edge device according to claim 5 , wherein the processor is further configured to trigger at least one alternative wireless connectivity option from the obtained updated set of alternative wireless connectivity options when there is the deviation in the first travel path.

7 . The edge device according to claim 6 , wherein:

the trigger of the at least one alternative wireless connectivity option from the obtained updated set of alternative wireless connectivity options is to:

maintain the cellular connectivity with at least one of the plurality of base stations or a new base station, and

bypass the initial access-search at the edge device, and the plurality of base stations includes the first base station.

8 . The edge device according to claim 1 , wherein the edge device is one of a 5G-enabled user equipment (UE), a 6G-enabled UE, a 5G-enabled repeater device arranged at a vehicle, or a 6G-enabled repeater device arranged at the vehicle.

9 . The edge device according to claim 1 , wherein:

the edge device has a donor side and a service side, the donor side of the edge device faces an exterior of a vehicle to communicate with one or more network nodes including the first base station and a second base station, and the service side of the edge device faces an interior of the vehicle to service one or more user equipment (UEs) within the vehicle such that an uplink and a downlink communication is established for the one or more UEs via the edge device arranged at the vehicle.

10 . A method, comprising:

in an edge device:

capturing sensing information of a surrounding area of the edge device;

obtaining a primary wireless connectivity option from a central cloud server for a first upcoming location along a first travel path to be traversed by the edge device;

obtaining a set of alternative wireless connectivity options from the central cloud server for the first upcoming location along the first travel path and one or more second upcoming locations along the first travel path, wherein each alternative wireless connectivity option of the set of alternative wireless connectivity options includes a respective specific initial access information that enables the edge device to bypass an initial access-search;

selecting both, at least one alternative wireless connectivity option from the set of alternative wireless connectivity options at the first upcoming location and the one or more second upcoming locations along the first travel path, based on one of:

a failure of the primary wireless connectivity option fails, or

bypassing, based on the respective specific initial access information of the selected at least one alternative wireless connectivity option, the initial access-search at the edge device to maintain the cellular connectivity with at least one of a plurality of base stations.

11 . The method according to claim 10 , further comprising periodically communicating the captured sensing information to the central cloud server.

12 . The method according to claim 10 , wherein the captured sensing information comprises one or more of: a location of the edge device, a moving direction of the edge device in the first travel path, a time-of-day, a current location of the edge device in the first travel path, upcoming locations of the edge device in the first travel path, upcoming geographical areas of the first travel path, traffic information, road information, construction information, and traffic light information.

13 . The method according to claim 10 , wherein

the plurality of base stations includes the first base station.

14 . The method according to claim 10 , further comprising obtaining an updated set of alternative wireless connectivity options as fallback options from the central cloud server in response to a deviation in the first travel path.

15 . The method according to claim 14 , further comprising triggering at least one alternative wireless connectivity option from the obtained updated set of alternative wireless connectivity options when there is the deviation in the first travel path.

16 . The method according to claim 15 , wherein:

the triggering of the at least one alternative wireless connectivity option from the obtained updated set of alternative wireless connectivity options is to:

maintain the cellular connectivity with at least one of the plurality of base stations or a new base station, and

bypass the initial access-search at the edge device, and the plurality of base stations includes the first base station.

17 . The method according to claim 10 , wherein the edge device is one of a 5G-enabled user equipment (UE), a 6G-enabled UE, a 5G-enabled repeater device arranged at a vehicle, or a 6G-enabled repeater device arranged at the vehicle.

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