Patent Publication Number: US-2023133824-A1

Title: Central cloud server and edge devices assisted high speed low-latency wireless connectivity

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATIO BY REFERENCE 
     This Patent Application makes reference to, claims priority to, claims the benefit of, and is a Continuation Application of U.S Pat. Application Serial No. 17/649,193 filed on Jan. 27, 2022, which is a Continuation Application of U. S Pat. No. 11,265,733 issued on Mar. 01, 2022, which is a Continuation Application of U.S Pat. No. 11,159,958 issued on Oct. 26, 2021, and is 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 central cloud server, an edge device, and a method for the central cloud server and edge devices assisted high speed low-latency wireless connectivity. 
     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 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) 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 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 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. 
     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 central cloud server, an edge device, and a method for the central cloud server and edge devices assisted high speed low-latency wireless connectivity for high performance 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. 
     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 with a central cloud server and a plurality of edge devices, in accordance with an exemplary embodiment of the disclosure. 
         FIG.  2    is a block diagram illustrating different components of an exemplary central cloud server, in accordance with an embodiment of the disclosure. 
         FIG.  3    is a block diagram illustrating different components of an exemplary edge device, in accordance with an embodiment of the disclosure. 
         FIGS.  4 A,  4 B, and  4 C  illustrate exemplary scenarios for implementation of the communication system and method for central cloud server and edge devices assisted high speed low-latency wireless connectivity, in accordance with an embodiment of the disclosure. 
         FIGS.  5 A and  5 B  collectively is a flowchart that illustrates a method for a central cloud server and edge devices assisted high speed low-latency wireless connectivity for high performance communication, in accordance with an embodiment of the disclosure. 
         FIG.  6    is a flowchart that illustrates a method for a central cloud server and edge device assisted high speed low-latency wireless connectivity for high performance communication, in accordance with an embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     Certain embodiments of the disclosure may be found in a central cloud server, an edge device, and a method for the central cloud server and edge devices assisted high speed low-latency wireless connectivity for high performance communication. The central cloud server, the edge device, and the method of the present disclosure significantly reduces the latency involved in initial access phase by making the edge devices bypass the initial-access search. For example, the existing average mmWave gNB 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, speed, and orientation of a user equipment (UE), such as a vehicle or a smartphone, using an intelligent database that is trained previously, and may be referred to as a connectivity enhanced database that specifies a plurality of time-of-day 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. The central cloud server supports the plurality of different wireless carrier networks including different interfaces, radio access technologies, computing technologies (e.g., hardware and operating systems) and is easily upgradable without any need to change the infrastructure. Thus, the central cloud server in coordination with the plurality of edge devices ensures a seamless connectivity as well as Quality of experience (QoE) without significantly increasing infrastructure cost. Moreover, the central cloud server takes into account comprehensive sensing information surrounding each edge device. Thus, a 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 distributing a different subset of information from the connectivity enhanced database to each of the plurality of edge devices. Such distribution by the central cloud server may be done according to a corresponding position of the each of the plurality of edge devices that enables easy handling and mitigation of any adverse impact on signal propagation due to the dynamic nature of surroundings 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 with a central cloud server and a plurality of edge devices, in accordance with an exemplary embodiment of the disclosure. With reference to  FIG.  1   , there is shown a block diagram  100  of a network environment that includes a central cloud server  102 , a plurality of edge devices  104 , one or more user equipment (UEs)  106 , and a plurality of base stations  108 . There is further shown 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 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 , and the plurality of base stations  108 . 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. 
     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 . Each edge device of the plurality of edge devices  104  may be one of an edge repeater device, a relay device, a small cell, a customer premise equipment (CPE), a road side unit (RSU) device, or a UE controlled by the central cloud server  102 , or an inference server. In an example, the UE may be controlled out-of-band, for example, in a management plane, by the central cloud server  102 . In an implementation, some of the edge devices of the plurality of edge devices  104  may be deployed at a fixed location while some may be portable. For example, an edge device may be a fixed wireless access (FWA) device, a repeater device, a small-cell, or even an inference server (e.g., an edge cloud) deployed at a fixed location that covers a given geographical area. In another example, some edge devices, such as an edge repeater device may be installed in a vehicle and thus location of such edge repeater device may vary rapidly when the vehicle is in motion. Moreover, some edge device may be portable, and thus their location may change. In some implementation, an edge device may be a part of a telematics unit of a vehicle or implemented as a portable repeater device. 
     Each of one or more UEs  106  may correspond to a 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 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 vehicle, a virtual reality headset, an augment reality device, an in-vehicle device, a wireless modem, a customer-premises equipment (CPE), 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 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 a 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). 
     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 manifest several known benefits for various service providers associated with the plurality of different WCNs  110 . For example, reduces backhaul traffic by provisioning content at the edge, distributes computational resources geographically in different locations (e.g., on premise mini cloud, central offices, customer premises, etc.,) depending on the use case requirements, and improves 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 modern wireless networks, such as 5G or upcoming 6G. The central cloud server  102  significantly improves 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 edge devices  104  over a period of time, the central cloud server  102  creates a connectivity enhanced database that specifies a plurality of time-of-day 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 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 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 or signal loss, thereby provisioning consistent high-speed low latency wireless connectivity. Thus, the central cloud server  102  manifest higher QoE as compared to existing systems. 
       FIG.  2    is a block diagram illustrating different 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 beam alignment information  210 . In an implementation, the primary storage  206  may further include processing chain parameters  212 . There is further shown a machine learning model  214  and a 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 periodically obtain sensing information  208  from the plurality of edge devices  104 . Each of the plurality of edge devices  104  may be deployed at different locations. For example, each of a first set of edge devices of the plurality of edge devices  104  may be an edge repeater device deployed at a corresponding fixed location to provide a non-line-of-sight (NLOS) transmission path between one or more base stations of the plurality of the base station  108  and one or more UEs, such as the one or more UEs  106 . Similarly, each of a second set of edge devices of the plurality of edge devices  104  may be an edge device mounted on a vehicle, and thus its location may change rapidly when a corresponding vehicle on which the edge device is installed is in motion. In yet another example, some of the edge devices of the plurality of edge devices  104  may be UEs controlled by the central cloud server  102 . The plurality of edge devices  104  may periodically sense its surroundings and communicate the sensed information, such 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  from the plurality of edge devices  104 . 
     In accordance with an embodiment, the sensing information  208  may comprise a position of each of the plurality of edge devices  104 , a location of the one or more UEs  106  in the motion state or in the stationary state in the surrounding area of each of the plurality of edge devices  104 , a moving direction of different UEs (such as the one or more UEs  106 ), a time-of-day, traffic information, road information, construction information, and traffic light information. The central cloud server  102  obtains such sensing information  208  and stores the data points of such sensing information  208  as input features. As the sensing information  208  is obtained periodically from various edge devices of the plurality of edge devices  104 , all 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 the first edge device  104 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 first edge device  104 A (and similarly for other edge devices) that may indicate an average number of UEs expected to be serviced by the first edge device  104 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 edge devices are active or not active, which edge 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 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 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 plurality of edge devices  104 . In some implementations, each edge device of the plurality of edge devices  104  may be configured to utilize external sensing devices, such as Lidar, camera, accelerometer, Global Navigation Satellite System (GNSS), gyroscope, or Internet-of-Things (IoT) devices (e.g. video surveillance devices, roadside 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 an edge 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 accordance with an embodiment, the sensing information  208  may further comprise a distance of each of the plurality of edge devices  104  from the one or more UEs  106  and other movable and immobile physical structures in the surrounding area of each of the plurality of edge devices  104 . In an implementation, the distance of each of the plurality of edge devices  104  from one or more UEs within its range, such as the one or more UEs  106 , and other movable and immobile physical structures in the surrounding area of each of the plurality of edge devices  104 , may be determined at each of the plurality of edge devices  104  or at least some edge devices of the plurality of edge devices  104 , and then communicated to the central cloud server  102  as the sensing information  208 . In some implementations, the central cloud server  102  may be configured to determine such distance based on the position information received from the plurality of edge devices  104 . Additionally, the processor  202  of the central cloud server  102  may be configured to cross-correlate the distances using the generated 3D environmental representation for a given surrounding area of a given edge device for higher accuracy. 
     In an example, the processor  202  may be further configured to determine distance of each edge device (e.g. an edge repeater device) 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, 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 . 
     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 edge devices in advance for various traffic scenarios to serve UEs and to identify improved (e.g., optimal) signal transmission paths to reach to UEs and for efficient handover for a wireless connectivity at a later stage (i.e., in the inference phase). Based on the sensing information  208  obtained from the plurality of edge devices  104 , the processor  202  may be further configured to detect where reflective objects are located and used that information in radiation pattern of the RF signals, such as 5G signals. The sensing information  208  may be used to make 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 plurality of edge devices  104 , comparatively significantly lower or almost negligible RF signals are reflected back to the plurality of edge devices  104 . 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 of each of the plurality of edge devices  104  in servicing the one or more UEs  106  in its surrounding area in different weather conditions. 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, 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. 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 plurality of edge devices  104  in servicing the one or more UEs  106  in its surrounding area in different weather conditions, and accordingly may learn a correlation between different weather condition and signal power level and other performance state of each of the plurality of edge devices  104  in servicing the one or more UEs  106  in its surrounding area. Accordingly, the processor  202  may be further configured to formulate rules to establish, maintain, and select one or more edge devices in advance to mitigate signal losses in various weather conditions to serve UEs and to identify improved (e.g., optimal) signal transmission paths to reach to UEs via the edge devices 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 to select a 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). 
     The processor  202  may be further configured to periodically obtain beam alignment information  210  from the plurality of edge devices  104 . The beam alignment information  210  may be obtained and stored for the plurality of different WCNs  110 . In an implementation, the beam alignment information  210  received by the central cloud server  102  from the plurality of edge devices  104  during the training phase may comprise one or more of 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 . 
     The processor  202  may be further configured to correlate the obtained sensing information  208  and the beam alignment information  210  for different times-of-day such that the connectivity enhanced database  216  is generated that specifies a plurality of time-of-day 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 . The correlation indicates that for a given set of input features extracted from the sensing information  208 , what is the most suitable (i.e. best) initial access information for a given edge device according to its position to service one or more UEs in its surrounding area such that a high-speed and low latency wireless connectivity can be achieved with increased consistency for different times-of-day. The connectivity enhanced database  216  may be a low-latency database, for example, “DynamoDB”, “Scylla”, or other proven and known low-latency databases that can handle one or more million transactions per second on a single cloud server. The time-of day specific uplink beam-alignment-wireless connectivity relation specifies, for the given set of input features for a given time-of-day, which beam index to set at an edge device for the uplink communication, a specific Physical Cell Identity (PCID) which indicates which gNB to connect to, or which WCN to select, which specific beam configuration to set, or whether a connection to the base station is to be established directly or indirectly in a NLOS path using another edge device (e.g. another edge repeater device) in a network of edge devices depending on the current location of the edge device. Similarly, the time-of day specific downlink beam-alignment-wireless connectivity relation specifies, for the given set of input features for a given time-of-day, which beam index to set at an edge device for the downlink communication, which WCN to select, which specific beam configuration to set, what power level of the RF signal may be sufficient, or an expected time period to service one or more UEs, such as the first UE  106 A, depending on the current location of the edge device. Thus, as the set of input features changes, the initial access information also changes for the given edge device according to changed set of input features to continue servicing the one or more UEs, such as the first UE  106 A, in its surrounding area without any drop in QoE. Moreover, as the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for the surrounding area of each of the plurality of edge devices  104  is independent of the plurality of different WCNs  110 , the complexity and the initial access latency is significantly reduced 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 or edge devices, which in turn improves network performance of associated WCNs of the plurality of different WCNs  110 . Furthermore, this way a consumer, such as the first UE  106 A, is provided with the capability to choose which WCN (i.e. which service provider) they like to connect to, and this is enabled from the cloud, such as the central cloud server  102 . The processor  202  may be configured to transfer such specific initial access information associated with a WCN, such as the first WCN  110 A to the edge device, such as the first edge device  104 A, where such specific initial access information is used by the edge device to establish wireless connectivity by passing conventional initial-access search. Thus, a consumer with a UE, such as the first UE  106 A, subscribed to the first WCN  110 A can request the edge device, such as the first edge device  104 A, to relay an RF signal of the first WCN  110 A, and if the consumer with the UE, such as the first UE  106 A, is subscribed to the second WCN  110 B can request the edge device, such as the first edge device  104 A, to relay an RF signal of the second WCN  110 B. 
     In an implementation, the processor  202  may be further configured to extract and tag parameters of the beam alignment information  210  as learning labels. The obtained sensing information  208  may be considered as input features, whereas the beam alignment information  210  may be considered as learning labels for the correlation. The processor  202  may be further configured to execute a mapping of the learning labels with one or more features of the obtained sensing information  208  until the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships is established for the surrounding area of each of the plurality of edge devices  104 . In an implementation, a machine learning algorithm, for example, an artificial neural network algorithm, may be used at the beginning before training with the real-world training data of input features and parameters of the beam alignment information  210  as supervised learning labels. When the machine learning algorithm is passed through the training data of correlated input features and parameters of the beam alignment information  210 , the machine learning algorithm determines patterns such that the input features (e.g. distance of edge device with a UE, weather condition, a UE location, moving direction, time-of-day, etc.) are mapped to the learning labels (e.g., best initial access information, such as best PCID, best beam index to be used, signal strength measurement of a Tx/Rx beam, beam configuration, best transmission path, an absolute radio-frequency channel number (ARFCN) etc.). Since the machine learning model  214  is trained periodically, so if the base station (e.g. a gNB) configuration is changed (e.g., a new sector or gNB is added or the PCID, ARFCN is changed) the machine learning model  214  quickly adapts to the change. The processor  202  is further configured to cause the machine learning model  214  to assign more weight to recent data points using, for example, an exponential time decay process. In an example, the hyperparameters of the machine learning model  214  may be set and tuned depending on the formulated rules, and boundaries or limits observed based on some early training. Some examples of the hyperparameters that may be set and observed in early learning and may be tuned accordingly, may include a number of layers, layers dimensions, learning rate, and dropout regularization, and others regularization rates. The machine learning model  214  may be a learned model generated as output in the training process, and thus, over a period of time, the machine learning model  214  is able to predict the specific initial access information most suited for a given set of input features. Alternatively, in another implementation a convolutional neural network (CNN) may be used for deep learning, where the input features of the sensing information  208  and their relationship with the desired output values may be derived automatically. 
     Thus, at the end of the training phase, the connectivity enhanced database  216  is generated that specifies the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for the surrounding area of each of the plurality of edge devices  104  independent of the plurality of different WCNs  110 . Thereafter, the processor  202  may be further configured to distribute a different subset of information from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to a corresponding position of the each of the plurality of edge devices  104 . The different subset of information may cause each of the plurality of edge devices  104  to service one or more UEs  106  in a motion state or in a stationary state in its surrounding area independent of the plurality of different WCNs  110  and bypassing an initial access-search on the corresponding edge device, such as the first edge device  104 A. In the inference phase or the operational phase, whenever one or more UEs arrive in a later stage, instead of conducting an initial access-search on an edge device, the central cloud server  102  assists the edge device by providing them with optimized initial access information (e.g., best beam index, best beam configuration, best ARFCN, and PCID) that it has learned the machine learning model  214  during the training phase. Moreover, as the different subset of information from the connectivity enhanced database  216  is distributed in advance to each of the plurality of edge devices  104  according to the corresponding position of the each of the plurality of edge devices  104 , each of the edge devices of the plurality of edge devices themselves may be able to identify the optimized initial access information much faster than standard initial access procedure. Such subset of information is updated in real time or near time whenever there is a change in the surrounding environment that may potentially affect signal propagation from the corresponding edge devices of the plurality of edge devices  104 . 
     In an example, in a city, there may be thousands of edge devices, where each edge device may only require enhanced information of its surrounding area to execute high performance communication, for example, in order to increase data throughput (e.g., in multi-gigabit data rate), optimize signal propagation paths in uplink and downlink communication, reduce latency, handle heterogeneity and multiple WCNs, and improve QoE. Thus, the processor  202  of the central cloud server  102  sends only a subset of information specific to the given edge device, such as the first edge device  104 A, from the connectivity enhanced database  216 . In an implementation, the subset of information specific to the given edge device includes time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships only for a current surrounding area of the given edge device, such as the first edge device  104 A, as per current position of the given edge device. In some implementation, the subset of information specific to the given edge device includes time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a current surrounding area (N), a previous surrounding area (N-1) in vicinity, and a next surrounding area (N+1) of the given edge device, such as the first edge device  104 A, as per current position of the given edge device. In other words, the subset of information specific to the given edge device includes optimized initial access information of at least three consequent geographical areas, where the middle geographical area may be the surrounding area of the given edge device. This further improves a switchover of a UE from one edge device (e.g. a deployed repeater device) to another edge device (e.g. another deployed repeater device) to maintain consistent connectivity, high data throughput, and low latency communication as the UE moves from one geographical area to another geographical area, where the switchover is controlled by the central cloud server  102 . 
     In some implementations, some edge devices of the plurality of edge devices  104  may be UEs controlled by the central cloud server  102 . In such a case, the different subset of information causes one or more edge devices of the plurality of edge devices  104  in a motion state or in a stationary state to attach to a corresponding base station bypassing the initial access-search on the corresponding edge device when the corresponding edge device itself is the UE controlled by the central cloud server  102 . 
     In accordance with an embodiment, the processor  202  may be further configured to determine, based on position information of the first edge device  104 A, whether a handover is required, and if so communicate wireless connectivity enhanced information including a specific initial access information to the first edge device  104 A to bypass the initial access-search on the first edge device  104 A. In a case where a wireless connection (e.g., a cellular connectivity) of a UE that is in motion, such as the first UE  106 A, is about to become less than a threshold performance value, such performance drop may be predicted by the central cloud server  102  based on new sensing information received from one or more edge devices in the vicinity of the UE or from the UE itself. For example, the UE may be attached to the first base station  108 A, and as the UE moves, the distance from the first base station  108 A may increase, and the signal strength may gradually decrease. Thus, based on input features obtained from the new sensing information, such as a moving direction of the UE, a position of the UE, distance from one or more edge devices in the vicinity of the UE, a current weather condition, the location of the reflective objects around the UE, and an overall 3D environment representation around the UE, the processor  202  determines that a handover is required to maintain QoE, and accordingly selects a suitable edge device (e.g. the first edge device  104 A) among the plurality of edge devices  104  and communicates wireless connectivity enhanced information to such selected edge device so that there is no need to perform beam sweeping operation or standard initial access search on such edge device. Thus, the UE may readily connect to the edge device, and continue to perform uplink and downlink communication with high throughput without any interruptions. Similarly, in accordance with an embodiment, the processor  202  may be further configured to determine that no handover is required for the first edge device  104 A when a performance state of a wireless connection of the UE, such as the first UE  106 A, is greater than a threshold performance value. 
     Alternatively, in an implementation, the processor  202  may be further configured to obtain processing chain parameters  212  from the plurality of edge devices  104 . In an implementation, the processing chain parameters  212  may be additional parameters treated as learning labels (e.g., supervised learning labels or unsupervised output values) in addition to the beam alignment information. In another implementation, the processing chain parameters  212  may be received instead of the beam alignment information as the data obtained from processing chain parameters  212  may be a superset that includes the data points of the beam alignment information. In yet another implementation, for processing purposes, the processing chain parameters  212  may be treated and processed similar to that of the beam alignment information. The processing chain parameters  212  may be obtained for further exhaustive training and inference of the machine learning model  214 . 
     The processing chain parameters  212  includes 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 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 chain 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 a 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  314  or the one or more second antenna arrays  316 , 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 the 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 the one or more first antenna arrays  314  or the one or more second antenna arrays  316 , and later use learnings 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 learnings to configure, and control and adjust beam patterns to and from each of the plurality of edge devices  104 . In a sixth 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 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 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 configure and instruct an edge device 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 provide management, monitoring and 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 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 . The decoded control information may include (or indicates) a periodicity and a downlink/uplink cycle ratio, a time division duplex (TDD) pattern, a 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 and other channel estimates as a feedback from the plurality of edge devices  104 . 
     In accordance with an embodiment, by virtue of the obtained modem information from the plurality of edge devices  104 , the central cloud server  102  may have information of more than one device modem, and thus have holistic information (e.g. an operating behavior) of different modems of many edge devices in a geographical area, which can be used to train the machine learning model  214  and optimize the radio communication (e.g. signal propagation) holistically for the entire geographical area. In an implementation, a software application for each modem of an edge device may run on the central cloud server  102  rather in the modem of an edge device, such as a repeater device. For example, one virtual machine (VM) may be dedicated for one modem of an edge device. As the central cloud server  102  has information of more than one device modem, it will know about other modems of other edge devices in a given geographical area, and thus being a high computational resource capable device have capability to optimize radio signal propagation and channel characteristic of the given geographical area, thereby improving network performance of the plurality of different WCNs  110 , and providing high performance wireless communication for the given geographical area (and similarly other geographical areas) to improve QoE. 
     In accordance with an embodiment, the central cloud server  102  may be further configured to access a Serial Peripheral Interface (SPI) between a modem and the radio (e.g., the front-end RF section  306 ) of each of the plurality of edge devices  104 . The SPI may be a full-duplex bus interface used to send data between the control section  304  (e.g., a microcontroller or DSP) and other peripheral components, such as the modem, for example, a 5G modem, and sensing radar (when present) in an edge device. The SPI interface supports very high speeds, and throughput, and is suitable for handing a lot of data. In an example, the processing chain parameters  212  may be accesses using access to the SPI. 
     In an implementation, the processor  202  may be further configured to extract and tag parameters of the processing chain parameters  212  as learning labels. The obtained sensing information  208  may be considered as input features, whereas the processing chain parameters  212  may be considered as learning labels for the correlation. The processor  202  may be further configured to execute a mapping of the learning labels with one or more features of the obtained sensing information  208  until the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships is established and further updated for the surrounding area of each of the plurality of edge devices  104 . 
     Similar to the correlation of the obtained sensing information  208  and the beam alignment information  210 , the processor  202  may be further configured to correlate the processing chain parameters  212  with that of the obtained sensing information  208  and the beam alignment information  210  for different times-of-day such that the connectivity enhanced database  216  is updated and includes further learned information at holistic level for a plurality of different geographical areas associated with the plurality of different WCNs  110 . The correlation further improves QoE and indicates that for a given set of input features extracted from the sensing information  208 , insights are provided as to what were the processing chain parameters  212  when there was most suitable (i.e., best) initial access information for a given edge device to service one or more UEs in its surrounding area, and hence it allows optimal management of network resources including the plurality of edge devices  104  in the inference phase. 
     In an example, the central cloud server  102  by use of the connectivity enhanced database  216  and the machine learning model  214 , and based on the distribution of the different subset of information from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to a corresponding position of the each of the plurality of edge devices  104 , further achieves the following:
     (a) reduce time to align to a timing offset of a beam reception at an edge device to a frame structure of a 5G NR radio frame, and allows uplink and downlink to use complete 5G NR frequency spectrum, but in different time slots, where some short time slots are designated for uplink while other time slots are designated for downlink;   (b) perform coordination among the edge devices of the plurality of edge devices  104  for beam forming optimizations for enhanced network coverage and quality of service (QoS);   (c) remotely control the phase shifting by controlling the 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;   (d) control amplification of input RF signals, which may have low-power, without significantly degrading corresponding signal-to-noise (SNR) ratio in the inference phase;   (e) send control signals to remotely configure or control parameters (e.g., amplifier gains, and phase responses) associated with the one or more first antenna arrays  314  or the one or more second antenna arrays  316 ;   (f) configure and control and adjust beam patterns to and from each of the plurality of edge devices  104 ;   (g) remotely 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 at the same time or in different time slots;   (h) configure and instruct an edge device 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; and   (i) optimize blocks of radio and perform Radio access network optimization to improve coverage, capacity and service quality of different geographical areas.   

       FIG.  3    is a block diagram illustrating different components of an exemplary edge device, in accordance with an embodiment of the disclosure.  FIG.  3    is explained in conjunction with elements from  FIGS.  1  and  2   . With reference to  FIG.  3   , there is shown a block diagram  300  of the first edge device  104 A. The first edge device  104 A may include a control section  304  and a front-end radio frequency (RF) section  306 . The control section  304  may include a control circuitry  308  and a memory  310 . The control section  304  may be communicatively coupled to the front-end RF section  306 . The front-end RF section  306  may include front-end RF circuitry  312  and a plurality of antenna arrays, such as one or more first antenna arrays  314  and one or more second antenna arrays  316 . 
     The first edge device  104 A includes suitable logic, circuitry, and interfaces that may be configured to communicate with one or more network nodes, such as one or more base stations of the plurality of base stations  108 , another edge device of the plurality of edge devices  104 , and user equipment (UEs). In accordance with an embodiment, the first edge device  104 A may support multiple and a wide range of frequency spectrum, for example, 2G, 3G, 4G, 5G, and 6G (including out-of-band frequencies). The first edge device  104 A is one of an XG-enabled edge repeater device, an XG-enabled relay device, an XG-enabled small-cell, or an XG-enabled user equipment (UE) controlled by the central cloud server  102 , where the term “XG” refers to 5G or 6G. Other examples of the first edge device  104 A may include, but is not limited to, a 5G wireless access point, an evolved-universal terrestrial radio access-new radio (NR) dual connectivity (EN-DC) device, a Multiple-input and multiple-output (MIMO)-capable repeater device, or a combination thereof. 
     The control circuitry  308  may be communicatively coupled to the memory  310  and the front-end RF section  306  including the front-end RF circuitry  312 , the one or more first antenna arrays  314 , and the one or more second antenna arrays  316 . The control circuitry  308  may be configured to execute various operations of the first edge device  104 A. The control circuitry  308  may be configured to control various components of the front-end RF section  306 . The first edge device  104 A may be a programmable device, where the control circuitry  308  may execute instructions stored in the memory  310 . Examples of the implementation of the control circuitry  308  may include, but are not limited to an embedded processor, a microcontroller, a specialized digital signal processor (DSP), a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, and/or other processors, or state machines. 
     The memory  310  may be configured to store the subset of information obtained from the central cloud server  102 , where the subset of information specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area specific to the first edge device  104 A. Examples of the implementation of the memory  310  may include, but not limited to, a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a processor cache, a thyristor random access memory (T-RAM), a zero-capacitor random access memory (Z-RAM), a read only memory (ROM), a hard disk drive (HDD), a secure digital (SD) card, a flash drive, cache memory, and/or other non-volatile memory. It is to be understood by a person having ordinary skill in the art that the control section  304  may further include one or more other components, such as an analog to digital converter (ADC), a digital to analog (DAC) converter, a cellular modem, and the like, known in the art, which are omitted for brevity. 
     The front-end RF circuitry  312  includes receiver circuitry and transmitter circuitry. The receiver circuitry is coupled to the one or more receiving antenna arrays, such as one of the one or more first antenna arrays  114  or the one or more second antenna arrays  116 , or may be a part of the receiver chain. The transmitter circuitry may be coupled to the one or more transmitting antenna arrays, such as the one of the one or more first antenna arrays  114  or the one or more second antenna arrays  116  in an implementation. The front-end RF circuitry  312  supports millimeter wave (mmWave) communication as well communication at a sub 6 gigahertz (GHz) frequency. 
     Each of the one or more first antenna arrays  114  and the one or more second antenna arrays  116  may be one of an XG phased-array antenna panel, an XG-enabled antenna chipset, an XG-enabled patch antenna array, or an XG-enabled servo-driven antenna array, where the “XG” refers to 5G or 6G. Examples of implementations of the XG phased-array antenna panel include, but is not limited to, a linear phased array antenna, a planar phased array antenna, a frequency scanning phased array antenna, a dynamic phased array antenna, and a passive phased array antenna. 
     In operation, in accordance with an embodiment, the control circuitry  308  may be configured to capture sensing information of a surrounding of the first edge device  104 A. The control circuitry  308  may be configured to periodically sense its surroundings and communicate the sensed information, such as the sensing information  208 , to the central cloud server  102 . Based on where the first edge device  104 A is deployed, for example, whether deployed at a fixed location or as a portable device, for example, mounted on a vehicle or as a portable repeater device, the first edge device  104 A may use its own sensing mechanism, such as a sensing radar, to sense its surrounding environment, utilize external sensing devices, or utilize a combination of both. In an implementation, when deployed at the fixed location, the control circuitry  308  may utilize the sensing radar and one or more image-capture devices to 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 the first edge devices  104 A. In some implementations, when deployed at a vehicle, the first edge device  104 A may be configured to utilize external sensing devices, such as Lidar, camera, accelerometer, GNSS, gyroscope, or loT devices (e.g. video surveillance devices, roadside 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. Other examples of the sensing information  208  may include, but not limited to, a 2D position of the first edge device  104 A, a 3D position (including elevation if deployed at a fixed location like a pole), a location of the one or more UEs  106  in the motion state or in the stationary state in the surrounding area, a moving direction of different UEs, a time-of-day, traffic information, road information, construction information, traffic light information, nearby bridges, location of reflective objects in the surrounding area, weather information, a distance of the first edge device  104 A from one or more UEs  106  within its range, distance of the first edge device  104 A 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, or any change detected in the surrounding area of the first edge device  104 A. The control circuitry  308  may be further configured to periodically communicate sensing information  208  to the central cloud server  102 . 
     The control circuitry  308  may be further configured to periodically communicate beam alignment information  210  to the central cloud server  102 . The beam alignment information  210  may comprise one or more of a transmit (Tx) beam information associated with the first edge device  104 A, a receive (Rx) beam information associated with the first edge device  104 A, a Physical Cell Identity (PCID) currently used by the first edge device  104 A, an absolute radio-frequency channel number (ARFCN) used by the first edge device  104 A, and a signal strength information associated with each of Tx beam and the Rx beam of the first edge device  104 A. All such measurements and feedback are sent to the central cloud server  102  for learning. 
     In accordance with an embodiment, the sensing information  208  and the beam alignment information  210  obtained by the central cloud server  102  from the edge device and other edge devices of a plurality of edge devices  104  is correlated by the central cloud server  102  for different times-of-day such that a connectivity enhanced database  216  is generated that specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices  104  independent of a plurality of different WCNs  110 . 
     The control circuitry  308  may be configured to obtain a subset of information from the central cloud server  102  according to a position of the first edge device  104 A, where the subset of information specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area specific to the first edge device  104 A. The control circuitry  308  may be further configured to receive a corresponding connection request from one or more UEs  106 . The connection request may be received via an out-of-band communication, such as Wi-Fi™, BLUETOOTH™, Li-Fi, a sidelink request (e.g., LTE sidelink, 5G New Radio (NR) sidelink, NR C-V2X sidelink), a vehicle-to-infrastructure (V2I) request, a personal area network (PAN) connection, or other out-of-band connection requests. The control circuitry  308  may be further configured to identity the one or more UEs  106  based on the connection request. Based on the obtained subset of information and the corresponding connection request, the control circuitry  308  may be further configured to service one or UEs  106  in the surrounding area bypassing an initial access-search on the first edge device  104 A. The first edge device  104 A is independent of a plurality of different WCNs  110  such that any one of the plurality of different WCNs  110  is used to service a specific UE in accordance with an association of the specific UE to a specific wireless carrier network. Thus, a consumer, such as the first UE  106 A, has is provided with the capability to choose which WCN (i.e. which service provider) they like to connect to, and this is enabled from the cloud, such as the central cloud server  102 . The central cloud server  102  transmits a specific initial access information (optimal initial access information) associated with a WCN, such as the first WCN  110 A, to the first edge device  104 A, where such specific initial access information is used by the first edge device  104 A to establish wireless connectivity by passing conventional initial-access search. Hence, beneficially, a consumer of a UE, such as the first UE  106 A, subscribed to the first WCN  110 A can request the first edge device  104 A in the connection request to relay an RF signal of the first WCN  110 A, and if the consumer of the first UE  106 A is subscribed to the second WCN  110 B, then the first UE  106 A can request the first edge device  104 A, to relay an RF signal of the second WCN  110 B. Additionally, and advantageously, as the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for the surrounding area of the first edge device  104 A is independent of the plurality of different WCNs  110 , the complexity and the initial access latency is significantly reduced as the standard beam sweeping operation in the initial access phase is bypassed and is not required to be performed at the one or more UEs  106  and the first edge device  104 A which in turn improves network performance and reduces additional signaling load (due to standard initial-access search) on associated WCNs of the plurality of different WCNs  110 . 
     In yet another aspect of the disclosure, one or more of the plurality of edge devices may be UEs controlled by the central cloud server  102 . Thus, due to the awareness of a physical location of a given edge device (in this case, a UE), the edge device may be configured to obtain the wireless connectivity enhanced information that includes a specific initial access information for the given edge device (i.e. a UE) to bypass initial access search at the given edge device (i.e. the UE), and further may be connected (i.e., attached) to a base station (e.g., a gNB) directly (or via a nearby small cell or CPE) specified in the obtained specific initial access information from the central cloud server  102  with reduced latency as compared to standard gNB handover time. Thus, arbitrated between the central cloud server  102  and the given edge device (i.e. the UE), alleviates other network nodes (such as a CPE, or a small cell present in the vicinity of the UE) from these complex functions, thereby simplifying their beam forming design and consequently lower cost of infrastructure. 
     In some scenarios, one or more of the plurality of edge devices may be CPEs. In such a case, a given edge device, such as the second edge device  104 B, may be configured to obtain the wireless connectivity enhanced information that includes a specific initial access information for given edge device (in this scenario, a CPE), where the specific initial access information may specify to connect to a nearby small cell to service a UE for high performance communication. Thus, arbitrated between the central cloud server  102  and the given edge device (due to the cloud awareness of the physical location of the UE as well as the CPE), alleviates the CPE from these complex functions, for example, location tracking of the UE, thereby simplifying its beam forming design and consequently lowering cost. 
       FIGS.  4 A,  4 B, and  4 C  illustrate exemplary scenarios for implementation of the communication system and method for central cloud server and edge devices assisted high speed low-latency wireless connectivity, in accordance with an embodiment of the disclosure.  FIGS.  4 A,  4 B, and  4 C  are explained in conjunction with elements from  FIGS.  1 ,  2 , and  3   . With reference to  FIGS.  4 A,  4 B, and  4 C , there is shown a first vehicle  402 , a second vehicle  404 , a plurality of repeater devices, such as repeater devices  406 A and  406 B, and a plurality of base stations, such as gNBs  408 A,  408 B,  408 C, and  408 D, and the central cloud server  102  ( FIGS.  1  and  2   ). The gNBs  408 A,  408 C, and  408 D may be of the first WCN  110 A of a first service provider and the gNBs  408 B may be of the second WCN  110 B of a second service provider. In a first implementation, the first vehicle  402  may correspond to a 5G-enabled UE controlled by the central cloud server  102 . In an example, the first vehicle  402  may have an application installed in it (e.g. installed in an in-vehicle infotainment system) which is communicatively coupled to the central cloud server  102  to receive its services. Alternatively, in a second implementation, the first vehicle  402  may include a UE, for example, a smartphone or an in-vehicle device, which has the application installed in it, and which is communicatively coupled to the central cloud server  102  to receive its services. For the sake of brevity, some exemplary functions of the central cloud server  102  and the method for central cloud server  102  and edge devices assisted high speed low-latency wireless connectivity, is described by taking an example of the first implementation. However, it is to be understood that functions described for the first vehicle  402  are also applicable for the second implementation, i.e., functions of the UE within a vehicle, such as the first vehicle  402 , without limiting the scope of the disclosure. 
     With reference to  FIG.  4 A , there is shown a first exemplary scenario  400 A, in which the first vehicle  402  and the second vehicle  404  are in motion. In this case, the first vehicle  402  may be a semi-autonomous or an autonomous vehicle. The first vehicle  402  may be attached to the gNB  408 A of the first WCN  110 A while in motion. In some implementations, the first vehicle  402  may be configured to communicate sensing information in real time or near real time to the central cloud server  102 . In some implementations, the first vehicle  402  may be configured to communicate sensing information to a first inference server that may be deployed nearest to the current location of the first vehicle  402 . There may be several inference servers deployed at different locations serving different geographical areas, which may be communicatively coupled to the central cloud server  102 . The decision to whether to communicate the sensing information directly to the central cloud server  102  or to the nearest deployed inference server may be based on a configured setting on the application and/or based on an amount or a type of data that is to be communicated. This further provides a hybrid computing capability based on a user preference (e.g., as opt-in or opt-out features provided to premium users) to the communication system including the central cloud server  102  and the method of the present disclosure. The second vehicle may also be attached to the gNB  408 A. In the first exemplary scenario  400 A, the central cloud server  102  (or the first inference server based on the subset of information communicated previously by the central cloud server  102 ) may be configured to obtain the sensing information and extract features from the sensing information and determine that no handover is required for the first vehicle  402  in a real time or a near time. As a result of the machine learning model  214  and the connectivity enhanced database  216  of the central cloud server  102 , it is immediately ascertained that for the extracted features (e.g., a time-of day, a current position of the first vehicle  402 , a distance of the first vehicle  402  from the gNB  408 A, a distance of the first vehicle  402  from the repeater devices  406 A and  406 B, a current 3D environment representation that indicates any possibility of signal blockages or fading, road condition, traffic information, and a current weather condition), the performance state of a wireless connection of the first vehicle  402  is greater than a threshold performance value, and there is no need for any handover. There is no need to do any signal measurements at this point because of the low-latency connectivity enhanced database  216 , which can holistically handle multi-dimensional input features. 
     With reference to  FIG.  4 B , there is shown a second exemplary scenario  400 B in continuation to the first exemplary scenario  400 A. In the second exemplary scenario  400 B, the first vehicle  402  and the second vehicle  404  further move ahead, as shown. The first vehicle  402  may further send sensing information to the central cloud server  102  (or the first inference server based on a selected setting on the installed application). However, in this case, the central cloud server  102  (or the first inference server) may be further configured to determine that a handover is required for the first vehicle  402 , based on the recently received sensing information, which indicates that some mobile object (i.e., the second vehicle  404 ) may be blocking a 5G signal from the gNB  408 A. Accordingly, the central cloud server  102  (or the first inference server) selects an appropriate repeater device, i.e., the repeater device  406 B, to communicate wireless connectivity enhanced information including a specific initial access information to the repeater device  406 B to bypass the initial access-search on the repeater device  406 B and the first vehicle  402 . In this case, the repeater device  406 B may be attached to the gNB  408 B of the second WCN  110 B initially, but quickly switches over to the gNB  408 C of the first WCN  110 A based on the specific initial access information (e.g. a given donor beam index, PCID of gNB  408 C, and related ARFCN) received from the central cloud server  102 . Thus, the repeater device  406 B may be independent of the plurality of different WCNs  110 , such as the first WCN  110 A and the second WCN  110 B. The specific initial access information may further indicate to select a particular service side beam index, e.g., a beam index#19 out of 0-63 and a particular beam configuration based on time-of-day and other sensing information, to service the first vehicle  402  bypassing the initial access search at the repeater device  406 B as well as the first vehicle  402 , where the handover time is much lesser than the standard average mm-wave gNB handover time under same scenarios, such as same cell radius and vehicle travelling speed. 
     With reference to  FIG.  4 C , there is shown a third exemplary scenario  400 C in continuation to the second exemplary scenario  400 B. In the third exemplary scenario  400 C, the first vehicle  402  and the second vehicle  404  further move ahead, where first vehicle  402  is about to move beyond a coverage area of the gNB  408 C. The first vehicle  402  (i.e., a 5G-enabled UE controlled by the central cloud server  102 ) may further send updated sensing information to the central cloud server  102  (or the first inference server). Based on the updated sensing information, the central cloud server  102  (or the first inference server) may predict that will be no deployed repeater devices or other network nodes (such as a small cell, an RSU, etc.) that may be in a communication range of the first vehicle  402  in the travel path based on a moving direction and speed of the first vehicle  402  and that a handover to a new gNB, such as the gNB  408 D of the first WCN  110 A, will need to be executed by the first vehicle  402  as the first vehicle  402  moves beyond the coverage area of the gNB  408 C. Thus, the central cloud server  102  (or the first inference server) may be further configured to communicate a wireless connectivity enhanced information including a new specific initial access information to the first vehicle  402  to bypass the initial access-search on the first vehicle  402  and quickly attach to the gNB  408 D, say less than one or two seconds. The second vehicle  404 , may be a conventional vehicle, and may not be a known user to the central cloud server  102  (or may not be communicatively coupled to the central cloud server  102  to receive its services), and thus may need to perform standard initial-access search to attach to the gNB  408 D, which may take a standard time (e.g. the average mmWave gNB handover time is on the order of 10-20 sec, assuming ~ 500 m cell radius (i.e. coverage area) and travelling speed of 50 MPH). For example, the second vehicle  404  may need to perform following four beam management operations: a) Beam sweeping, where an exhaustive scanning of a spatial area with a set of beams transmitted and received needs to done; b) Beam measurement, where signal quality, such as received power (RSRP), Signal to Interference plus Noise Ratio (SINR), of the received beam of RF signals, may need to be executed; c) Beam determination, where an optimal beam (or set of beams) may be selected for establishing directional communications; and d) Beam reporting, it is reported to network of the signal quality and on the decisions made in the previous phase. The first vehicle  402  by virtue of the obtained wireless connectivity enhanced information that includes optimal initial access information is able to bypass the initial access-search and reduce signaling overhead usually incurred by network processes by avoiding many of such standard beam management operations on the first vehicle  402  without any adverse impact while still maintaining QoE with high reliability and consistency. 
       FIGS.  5 A and  5 B  collectively is a flowchart that illustrates a method for a central cloud server and edge devices assisted high speed low-latency wireless connectivity for high performance communication, in accordance with an embodiment of the disclosure.  FIGS.  5 A and  5 B  are explained in conjunction with elements from  FIGS.  1 ,  2 ,  3 ,  4 A and  4 B . With reference to  FIGS.  5 A and  5 B , there is shown a flowchart  500  comprising exemplary operations  502  through  518 . The operations of the method depicted in the flowchart  500  may be implemented in the central cloud server  102  ( FIG.  1   ). 
     At  502 , sensing information  208  may be periodically obtained from the plurality of edge devices  104 . The processor  202  may be configured to periodically obtain sensing information  208  from the plurality of edge devices  104 . The sensing information  208  may comprise a position of each of the plurality of edge devices  104 , a location of the one or more UEs  106  in the motion state or in the stationary state in the surrounding area of each of the plurality of edge devices  104 , a moving direction of the one or more UEs  106 , a time-of-day, traffic information, road information, construction information, traffic light information, and weather information. 
     At  504 , a distance of each of the plurality of edge devices  104  from one or more UEs  106  and other movable and immobile physical structures in the surrounding area of each of the plurality of edge devices  104  may be determined. The processor  202  may be further configured to determine such distance. 
     At  506 , supplementary information may be generated as insights based on cross-correlation of data points of the sensing information  208 . The processor  202  may be further configured to generate the supplementary information as insights based on cross-correlation of data points of the sensing information  208 . 
     At  508 , beam alignment information  210  may be periodically obtained from the plurality of edge devices  104 . The processor  202  may be further configured to periodically obtain beam alignment information  210  from the plurality of edge devices  104 . The beam alignment information  210  received by the central cloud server  102  from the plurality of edge devices  104  during a training phase may comprise one or more of 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 . 
     At  510 , the weather information may be utilized to determine one or more changes in a performance state of each of the plurality of edge devices  104  in servicing the one or more UEs  106  in its surrounding area in different weather conditions. The processor  202  may be further configured to utilize the weather information to determine one or more changes in a performance state of each of the plurality of edge devices  104   in servicing the one or more UEs  106  in its surrounding area in different weather conditions. 
     At  512 , the obtained sensing information  208  and the beam alignment information  210  may be correlated for different times-of-day such that a connectivity enhanced database  216  is generated that specifies a plurality of time-of-day 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 . The processor  202  may be further configured to correlate the obtained sensing information  208  and the beam alignment information  210  for different times-of-day. In an implementation, the operation  512  may include sub-operations  512 A and  512 B. 
     At  512 A, parameters of the beam alignment information  210  may be extracted and tagged as learning labels. The processor  202  may be further configured to extract and tag parameters of the beam alignment information  210  as learning labels. 
     At  512 B, a mapping of the learning labels may be executed with one or more features of the obtained sensing information  208  until the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships is established for the surrounding area of each of the plurality of edge devices  104 . The processor  202  may be further configured to execute the mapping of the learning labels with one or more features of the obtained sensing information  208  until the plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships is established for the surrounding area of each of the plurality of edge devices  104 . 
     At  514 , a different subset of information may be distributed from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to a corresponding position of the each of the plurality of edge devices  104 , where the different subset of information may cause each of the plurality of edge devices  104  to service one or more UEs  106  in a motion state or in a stationary state in its surrounding area independent of the plurality of different WCNs  110  bypassing an initial access-search on the corresponding edge device, such as the first edge device  104 A. The processor  202  may be further configured to distribute the different subset of information from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to the corresponding position of the each of the plurality of edge devices  104 . 
     At  516 , it may be determined, based on position information of the first edge device  104 A, whether a handover is required, and if so, communicate wireless connectivity enhanced information including a specific initial access information to the first edge device  104 A to bypass the initial access-search on the first edge device  104 A. The processor  202  may be further configured to determine, based on the position information of the first edge device  104 A, whether the handover is required, and if so, communicate wireless connectivity enhanced information including the specific initial access information to the first edge device  104 A to bypass the initial access-search on the first edge device  104 A. 
     At  518 , it may be determined that no handover is required for the first edge device  104 A when a performance state of a wireless connection of the first UE  106 A is greater than a threshold performance value. The processor  202  may be further configured to determine that no handover is required for the first edge device  104 A. 
       FIG.  6    is a flowchart that illustrates a method for a central cloud server assisted high speed low-latency wireless connectivity for high performance communication, in accordance with an embodiment of the disclosure.  FIGS.  6 A and  6 B  are explained in conjunction with elements from  FIGS.  1 ,  2 ,  3 ,  4 A, and  4 B . With reference to  FIGS.  6 A and  6 B , there is shown a flowchart  600  comprising exemplary operations  602  through  612 . The operations of the method depicted in the flowchart  600  may be implemented in an edge device, such as the first edge device  104 A ( FIG.  1   ). 
     At  602 , sensing information of a surrounding area of the first edge device  104 A may be captured. The sensing information captured by the first edge device  104 A is described in details, for example, in  FIG.  3   . At  604 , sensing information  208  may be periodically communicated to the central cloud server  102 . 
     At  606 , beam alignment information  210  may be periodically communicated to the central cloud server  102 . In accordance with an embodiment, the sensing information  208  and the beam alignment information  210  obtained by the central cloud server  102   from the edge device and other edge devices of a plurality of edge devices  104  is correlated by the central cloud server  102  for different times-of-day such that a connectivity enhanced database  216  is generated that specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices  104  independent of a plurality of different WCNs  110 . 
     At  608 , a subset of information may be obtained from the central cloud server  102  according to a position of the first edge device  104 A, where the subset of information specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area specific to the first edge device  104 A. 
     At  610 , a corresponding connection request may be received from one or more UEs  106 . The connection request may be received via an out-of-band communication, such as Wi-Fi™, BLUETOOTH™, Li-Fi, a sidelink request (e.g. LTE sidelink, 5G New Radio (NR) sidelink, NR C-V2X sidelink), a vehicle-to-infrastructure (V2I) request, a personal area network (PAN) connection, or other out-of-band connection requests. The one or more UEs  106  may be identified as priority users based on the connection request in order to prioritize servicing the one or more UEs  106 . 
     At  612 , based on the obtained subset of information and the corresponding connection request, one or UEs  106  in the surrounding area may be serviced bypassing an initial access-search on the first edge device  104 A, where the first edge device  104 A is independent of a plurality of different WCNs  110  such that any one of the plurality of different WCNs  110  is used to service a specific UE in accordance with an association of the specific UE to a specific wireless carrier network. 
     Various embodiments of the disclosure may provide a non-transitory computer-readable medium having stored thereon, computer implemented instructions that when executed by a computer causes the computer to execute operations to periodically obtain sensing information from a plurality of edge devices  104  and periodically obtain beam alignment information from the plurality of edge devices  104 . The operations also include correlating the obtained sensing information and the beam alignment information for different times-of-day such that a connectivity enhanced database  216  is generated that specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices  104  independent of a plurality of different WCNs  110 . The operation further includes distributing a different subset of information from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to a corresponding position of the each of the plurality of edge devices  104 . The different subset of information causes each of the plurality of edge devices  104  to service one or more user equipment (UEs)  106  in a motion state or in a stationary state in its surrounding area independent of the plurality of different WCNs  110  and bypassing an initial access-search on the corresponding edge device. 
     Various embodiments of the disclosure may include a central cloud server  102  ( FIG.  1   ). The central cloud server  102  comprises a processor  202  configured to periodically obtain sensing information from a plurality of edge devices  104 . The processor  202  may be further configured to periodically obtain beam alignment information from the plurality of edge devices  104 . The processor  202  may be further configured to correlate the obtained sensing information and the beam alignment information for different times-of-day such that a connectivity enhanced database  216  is generated that specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of the plurality of edge devices  104  independent of a plurality of different wireless carrier networks  110 . The processor  202  may be further configured to distribute a different subset of information from the connectivity enhanced database  216  to each of the plurality of edge devices  104  according to a corresponding position of the each of the plurality of edge devices  104 , wherein the different subset of information causes each of the plurality of edge devices  104  to service one or more user equipment (UEs)  106  in a motion state or in a stationary state in its surrounding area independent of the plurality of different WCNs  110  bypassing an initial access-search on the corresponding edge device. 
     Various embodiments of the disclosure may include a first edge device  104 A, for example, a relay device, a small cell, or an edge repeater device. The first edge device  104 A comprises control circuitry  308  configured to obtain a subset of information from a central cloud server  102  according to a position of the first edge device  104 A, wherein the subset of information specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area specific to the first edge device  104 A. The control circuitry  308  may be further configured to receive a corresponding connection request from one or more user equipment (UEs)  106 . Based on the obtained subset of information and the corresponding connection request, the control circuitry  308  may be further configured to service one or more user equipment (UEs)  106  in the surrounding area bypassing an initial access-search on the first edge device  104 A, wherein the first edge device  104 A is independent of a plurality of different wireless carrier networks (WCNs)  110  such that any one of the plurality of different wireless carrier networks  110  is used to service a specific UE in accordance with an association of the specific UE to a specific wireless carrier network. 
     While various embodiments described in the present disclosure have been described above, it should be understood that they have been presented by way of example, and not limitation. It is to be understood that various changes in form and detail can be made therein without departing from the scope of the present disclosure. In addition to using hardware (e.g., within or coupled to a central processing unit (“CPU”), microprocessor, micro controller, digital signal processor, processor core, system on chip (“SOC”) or any other device), implementations may also be embodied in software (e.g. computer readable code, program code, and/or instructions disposed in any form, such as source, object or machine language) disposed for example in a non-transitory computer-readable medium configured to store the software. Such software can enable, for example, the function, fabrication, modeling, simulation, description and/or testing of the apparatus and methods describe herein. For example, this can be accomplished through the use of general program languages (e.g., C, C++), hardware description languages (HDL) including Verilog HDL, VHDL, and so on, or other available programs. Such software can be disposed in any known non-transitory computer-readable medium, such as semiconductor, magnetic disc, or optical disc (e.g., CD-ROM, DVD-ROM, etc.). The software can also be disposed as computer data embodied in a non-transitory computer-readable transmission medium (e.g., solid state memory or any other non-transitory medium including digital, optical, analog-based medium, such as removable storage media). Embodiments of the present disclosure may include methods of providing the apparatus described herein by providing software describing the apparatus and subsequently transmitting the software as a computer data signal over a communication network including the internet and intranets. 
     It is to be further understood that the system described herein may be included in a semiconductor intellectual property core, such as a microcontroller (e.g., embodied in HDL) and transformed to hardware in the production of integrated circuits. Additionally, the system described herein may be embodied as a combination of hardware and software. Thus, the present disclosure should not be limited by any of the above-described exemplary embodiments but should be defined only in accordance with the following claims and their equivalents.