Google Patentsopen access

Cellular communication — Bao Tran (US11894620B2)

ABSTRACT

Abstract

A system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.

Description

The present invention relates to cellular systems.

5G cell phones use radio frequencies in various bands as per country wise allocations. Typically it uses less than 1 GHz, below 6 GHz and above 6 GHz (i.e. mmwave) frequency bands. It delivers fast uplink/downlink throughput due to massive MIMO and lower latency between 5G network (i.e. 5GNB) and itself. The 5G cell phone supports 10 times throughput compare to 4G phones. They are backward compatible to 4G standards such as LTE and LTE-advanced. Moreover latest 5G phones will support Bluetooth, Wi-Fi and NFC based short distance wireless technologies. GPS is also incorporated to support various GPS based applications including location tracking, google maps etc.

SUMMARY

A system to perform edge processing for a predetermined target includes one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target; a processor to control communication with the predetermined target; one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and a container to house the transceiver, processor, and one or more edge processing modules, wherein the container is stackable laterally or on top of each other, and wherein the container conforms to a shipping standard.

In implementations, the system can include an antenna mast, wherein the antenna mast is inside the container or external to the container. If the antenna mast is inside the container, the mast can be actuated to increase height via a pump. If the mast is external the container, the mast and the container can be secured to the ground. The radio heads can have cables that are routed through the RF entry points and connected to an antenna with a mast. The antenna mast supports the mounting of antenna. The antenna may be used for reception, transmission or both reception and transmission of an electromagnetic signal. The mast may be limited in height because of obstructions in the environment. Obstructions may include vegetation, vine canopies, tree canopies, bridges, traffic signals, buildings or otherwise. The limitation in height of the antenna may limit the maximum range of effective communications between the vehicle and a communications device located remotely apart from the vehicle. For example, electromagnetic radiation that is in the microwave frequency range may be limited to propagation in line-of-sight paths or may be severely attenuated by ground clutter where antenna height is insufficient for a requisite level of clearance. Accordingly, the mast 78 is used to extend the height for maximizing the available antenna height of an antenna mast mounted on a vehicle to improve the range and reliability of communications.

Advantages may include quick installation as no permit is needed. The container can be moved to particular events that demand higher capability, or can be moved to avoid natural disasters.

Various other inventive aspects are disclosed below.

Edge Processing with Low Latency

A system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.

Implementations can include one or more of the following. The processor calibrates a radio link between a transceiver in the housing and a client device. The processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN. The processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached. The edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). The edge processing module is embedded in the antenna housing. The edge processing module can be a pole, a building, or a light. The cellular transceiver can be a 5G or 6G transceiver. The processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history. The beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices. A neural network coupled to a control plane, a management plane, and a data plane to optimize 5G or 6G parameters. One or more cameras and sensors in the housing to capture security information. Edge sensors mounted on the housing of the antenna can include LIDAR and RADAR. A camera can perform individual identity identification. The edge processing module streams data to the predetermined target to minimize loading the target. The edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). An edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access. The edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task. A cellular device for a person crossing a street near a city light or street light can emit a person to vehicle (P2V) or a vehicle to person (V2P) safety message. A cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network.

Beam Sweeping with Low Latency

A system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically steerable to the predetermined target; and a processor to generate an antenna beam sweeping command based upon traffic distribution data, device networking requirements or a prior beam sweeping history to focus at least one beam for communication with the predetermined target.

Implementations can include one or more of the following. The traffic distribution data may be generated by collecting traffic data from radio nodes and/or UE devices, or the networking requirements may include, for example, service requirements associated with one or more applications (vehicular or reality application) on one or more UE devices. The processor calibrates a radio link between a transceiver in the housing and a client device. The processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN. The processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached. The cellular transceiver can be a 5G or 6G transceiver. The processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history. The beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices. A neural network (NN) can be used to determine the beam sweeping. The NN can be connected to a control plane, a management plane, and a data plane to optimize 5G or 6G parameters. One or more cameras and sensors in the housing to capture security information. Edge sensors mounted on the housing of the antenna can include LIDAR and RADAR and such data can be sent at top priority to vehicles passing by. A camera can perform individual identity identification. The edge processing module streams data to the predetermined target to minimize loading the target. The edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). An edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access. The edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task. A cellular device for a person crossing a street near a city light or street light can emit a person to vehicle (P2V) or a vehicle to person (V2P) safety message. A cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. An edge processing module can be connected to the processor and the one or more antennas to provide low-latency computation for the predetermined target. The edge processing module can be at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). The edge processing module is embedded in the antenna housing. The edge processing module can be part of a pole, a building, or a light. The processor can run code including requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group. The request can be for enhanced services for autonomous vehicles. The request can be for reality applications such as virtual reality or augmented reality. Upon request, the system determines a candidate network slice deployment layout that satisfies the network level requirements and network layouts of a service request and costs associated with a candidate network resource and/or a candidate network slice deployment layout, which is used as a basis to optimally use available network resources. The system coordinates, authorizes, releases and/or engages network resources in network. The system obtains network slice deployment layout descriptors corresponding to a network slice deployment layout and the system may manage the provisioning of the network slice deployment layout to satisfy the service request. The system may perform various functions such as, for example, network slice life cycle management, configuration management (e.g., policies, isolation of management), performance management (e.g., service level agreement (SLA) management, service assurance and programmability), service mapping. The system updates in real time network resource device regarding availability of network resources based on the current state of network resources in network and provisioned network resources that support service requests. The system can respond to the request with a monetary cost associated with a candidate network slice deployment layout, quality-of-service values associated with the candidate network slice deployment layout (e.g., minimum value and/or maximum value pertaining to latency, bandwidth, reliability, etc.) and/or other information representative of the configuration and/or service such as virtual network resource, non-virtual network resource, cloud, 5G or 6G RAN access, for example. The method includes storing network resource and capability information pertaining to network resources of a network and generating network level requirement information that would support the network service. The method includes creating end-to-end network slice deployment information that includes parameters to provision an end-to-end network slice deployment layout in the network that supports the network service.

Network Slicing for Groups of Devices

A method to manage a cellular network includes requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group.

In one implementation, the request can be for enhanced services for autonomous vehicles. The request can be for reality applications such as virtual reality or augmented reality. Upon request, the system determines a candidate network slice deployment layout that satisfies the network level requirements and network layouts of a service request and costs associated with a candidate network resource and/or a candidate network slice deployment layout, which is used as a basis to optimally use available network resources. The system coordinates, authorizes, releases and/or engages network resources in network. The system obtains network slice deployment layout descriptors corresponding to a network slice deployment layout and the system may manage the provisioning of the network slice deployment layout to satisfy the service request. The system may perform various functions such as, for example, network slice life cycle management, configuration management (e.g., policies, isolation of management), performance management (e.g., service level agreement (SLA) management, service assurance and programmability), service mapping. The system updates in real time network resource device regarding availability of network resources based on the current state of network resources in network and provisioned network resources that support service requests. The system can respond to the request with a monetary cost associated with a candidate network slice deployment layout, quality-of-service values associated with the candidate network slice deployment layout (e.g., minimum value and/or maximum value pertaining to latency, bandwidth, reliability, etc.) and/or other information representative of the configuration and/or service such as virtual network resource, non-virtual network resource, cloud, 5G or 6G RAN access, for example. The method includes storing network resource and capability information pertaining to network resources of a network and generating network level requirement information that would support the network service. The method includes creating end-to-end network slice deployment information that includes parameters to provision an end-to-end network slice deployment layout in the network that supports the network service.

Liquid Lens Antenna

A system, includes a liquid lens with moveable surface, wherein liquid is added or removed to adjust the curvature of the movable surface; and an antenna mounted on the moveable surface to change a direction of the antenna to a predetermined target. The system may include one or more of the following: A viscous liquid in the lens can be injected under processor control to change the curvature of the lens and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the lens can be done by injecting or removing liquid from the lens. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of liquid lens can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the lens can be automatically done using processor with iterative changes in the orientation of the antenna by changing the lens shape until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. This is similar to the way human vision eyeglass correction is done. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors.

Steerable Actuated Antenna

A system, includes a moveable surface; and one or more antennas mounted on the moveable surface to change a direction of the antenna to a predetermined target. The system may include one or more of the following. A pneumatic actuator or electrical motor can be placed under processor control to change the curvature of the lens and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable surface until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target device can be done by moving the actuators up or down. Opposing actuator arrays can be formed to provide two-sided communication antennas. An array of actuators can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the actuators can be automatically done using processor with iterative changes in the orientation of the antenna by changing the actuators until predetermined criteri

The present invention relates to cellular systems.

5G cell phones use radio frequencies in various bands as per country wise allocations. Typically it uses less than 1 GHz, below 6 GHz and above 6 GHz (i.e. mmwave) frequency bands. It delivers fast uplink/downlink throughput due to massive MIMO and lower latency between 5G network (i.e. 5GNB) and itself. The 5G cell phone supports 10 times throughput compare to 4G phones. They are backward compatible to 4G standards such as LTE and LTE-advanced. Moreover latest 5G phones will support Bluetooth, Wi-Fi and NFC based short distance wireless technologies. GPS is also incorporated to support various GPS based applications including location tracking, google maps etc.

SUMMARY

A system to perform edge processing for a predetermined target includes one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target; a processor to control communication with the predetermined target; one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and a container to house the transceiver, processor, and one or more edge processing modules, wherein the container is stackable laterally or on top of each other, and wherein the container conforms to a shipping standard.

In implementations, the system can include an antenna mast, wherein the antenna mast is inside the container or external to the container. If the antenna mast is inside the container, the mast can be actuated to increase height via a pump. If the mast is external the container, the mast and the container can be secured to the ground. The radio heads can have cables that are routed through the RF entry points and connected to an antenna with a mast. The antenna mast supports the mounting of antenna. The antenna may be used for reception, transmission or both reception and transmission of an electromagnetic signal. The mast may be limited in height because of obstructions in the environment. Obstructions may include vegetation, vine canopies, tree canopies, bridges, traffic signals, buildings or otherwise. The limitation in height of the antenna may limit the maximum range of effective communications between the vehicle and a communications device located remotely apart from the vehicle. For example, electromagnetic radiation that is in the microwave frequency range may be limited to propagation in line-of-sight paths or may be severely attenuated by ground clutter where antenna height is insufficient for a requisite level of clearance. Accordingly, the mast 78 is used to extend the height for maximizing the available antenna height of an antenna mast mounted on a vehicle to improve the range and reliability of communications.

Advantages may include quick installation as no permit is needed. The container can be moved to particular events that demand higher capability, or can be moved to avoid natural disasters.

Various other inventive aspects are disclosed below.

Edge Processing with Low Latency

A system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically or mechanically steerable to the predetermined target; a processor to control a directionality of the one or more antennas in communication with the predetermined target; and an edge processing module coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target.

Implementations can include one or more of the following. The processor calibrates a radio link between a transceiver in the housing and a client device. The processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN. The processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached. The edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). The edge processing module is embedded in the antenna housing. The edge processing module can be a pole, a building, or a light. The cellular transceiver can be a 5G or 6G transceiver. The processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history. The beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices. A neural network coupled to a control plane, a management plane, and a data plane to optimize 5G or 6G parameters. One or more cameras and sensors in the housing to capture security information. Edge sensors mounted on the housing of the antenna can include LIDAR and RADAR. A camera can perform individual identity identification. The edge processing module streams data to the predetermined target to minimize loading the target. The edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). An edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access. The edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task. A cellular device for a person crossing a street near a city light or street light can emit a person to vehicle (P2V) or a vehicle to person (V2P) safety message. A cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network.

Beam Sweeping with Low Latency

A system includes a cellular transceiver to communicate with a predetermined target; one or more antennas coupled to the 5G or 6G transceiver each electrically steerable to the predetermined target; and a processor to generate an antenna beam sweeping command based upon traffic distribution data, device networking requirements or a prior beam sweeping history to focus at least one beam for communication with the predetermined target.

Implementations can include one or more of the following. The traffic distribution data may be generated by collecting traffic data from radio nodes and/or UE devices, or the networking requirements may include, for example, service requirements associated with one or more applications (vehicular or reality application) on one or more UE devices. The processor calibrates a radio link between a transceiver in the housing and a client device. The processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN. The processor calibrates a connection by analyzing RSSI and TSSI and moves the antennas until predetermined cellular parameters are reached. The cellular transceiver can be a 5G or 6G transceiver. The processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history. The beam sweeping is directed at a group of autonomous vehicles or a group of virtual reality devices. A neural network (NN) can be used to determine the beam sweeping. The NN can be connected to a control plane, a management plane, and a data plane to optimize 5G or 6G parameters. One or more cameras and sensors in the housing to capture security information. Edge sensors mounted on the housing of the antenna can include LIDAR and RADAR and such data can be sent at top priority to vehicles passing by. A camera can perform individual identity identification. The edge processing module streams data to the predetermined target to minimize loading the target. The edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). An edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access. The edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task. A cellular device for a person crossing a street near a city light or street light can emit a person to vehicle (P2V) or a vehicle to person (V2P) safety message. A cloud trained neural network whose network parameters are down-sampled and filter count reduced before transferring to the edge neural network. An edge processing module can be connected to the processor and the one or more antennas to provide low-latency computation for the predetermined target. The edge processing module can be at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD). The edge processing module is embedded in the antenna housing. The edge processing module can be part of a pole, a building, or a light. The processor can run code including requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group. The request can be for enhanced services for autonomous vehicles. The request can be for reality applications such as virtual reality or augmented reality. Upon request, the system determines a candidate network slice deployment layout that satisfies the network level requirements and network layouts of a service request and costs associated with a candidate network resource and/or a candidate network slice deployment layout, which is used as a basis to optimally use available network resources. The system coordinates, authorizes, releases and/or engages network resources in network. The system obtains network slice deployment layout descriptors corresponding to a network slice deployment layout and the system may manage the provisioning of the network slice deployment layout to satisfy the service request. The system may perform various functions such as, for example, network slice life cycle management, configuration management (e.g., policies, isolation of management), performance management (e.g., service level agreement (SLA) management, service assurance and programmability), service mapping. The system updates in real time network resource device regarding availability of network resources based on the current state of network resources in network and provisioned network resources that support service requests. The system can respond to the request with a monetary cost associated with a candidate network slice deployment layout, quality-of-service values associated with the candidate network slice deployment layout (e.g., minimum value and/or maximum value pertaining to latency, bandwidth, reliability, etc.) and/or other information representative of the configuration and/or service such as virtual network resource, non-virtual network resource, cloud, 5G or 6G RAN access, for example. The method includes storing network resource and capability information pertaining to network resources of a network and generating network level requirement information that would support the network service. The method includes creating end-to-end network slice deployment information that includes parameters to provision an end-to-end network slice deployment layout in the network that supports the network service.

Network Slicing for Groups of Devices

A method to manage a cellular network includes requesting a portion of a network for a group of devices, checking for available resources to satisfy the request and assigning a network slice deployment layout satisfying the requested portion of the network including antenna level layout, and managing resources at the antenna level as part of the requested portion of the network to provide communication for the group.

In one implementation, the request can be for enhanced services for autonomous vehicles. The request can be for reality applications such as virtual reality or augmented reality. Upon request, the system determines a candidate network slice deployment layout that satisfies the network level requirements and network layouts of a service request and costs associated with a candidate network resource and/or a candidate network slice deployment layout, which is used as a basis to optimally use available network resources. The system coordinates, authorizes, releases and/or engages network resources in network. The system obtains network slice deployment layout descriptors corresponding to a network slice deployment layout and the system may manage the provisioning of the network slice deployment layout to satisfy the service request. The system may perform various functions such as, for example, network slice life cycle management, configuration management (e.g., policies, isolation of management), performance management (e.g., service level agreement (SLA) management, service assurance and programmability), service mapping. The system updates in real time network resource device regarding availability of network resources based on the current state of network resources in network and provisioned network resources that support service requests. The system can respond to the request with a monetary cost associated with a candidate network slice deployment layout, quality-of-service values associated with the candidate network slice deployment layout (e.g., minimum value and/or maximum value pertaining to latency, bandwidth, reliability, etc.) and/or other information representative of the configuration and/or service such as virtual network resource, non-virtual network resource, cloud, 5G or 6G RAN access, for example. The method includes storing network resource and capability information pertaining to network resources of a network and generating network level requirement information that would support the network service. The method includes creating end-to-end network slice deployment information that includes parameters to provision an end-to-end network slice deployment layout in the network that supports the network service.

Liquid Lens Antenna

A system, includes a liquid lens with moveable surface, wherein liquid is added or removed to adjust the curvature of the movable surface; and an antenna mounted on the moveable surface to change a direction of the antenna to a predetermined target. The system may include one or more of the following: A viscous liquid in the lens can be injected under processor control to change the curvature of the lens and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the lens can be done by injecting or removing liquid from the lens. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of liquid lens can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the lens can be automatically done using processor with iterative changes in the orientation of the antenna by changing the lens shape until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. This is similar to the way human vision eyeglass correction is done. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors.

Steerable Actuated Antenna

A system, includes a moveable surface; and one or more antennas mounted on the moveable surface to change a direction of the antenna to a predetermined target. The system may include one or more of the following. A pneumatic actuator or electrical motor can be placed under processor control to change the curvature of the lens and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable surface until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target device can be done by moving the actuators up or down. Opposing actuator arrays can be formed to provide two-sided communication antennas. An array of actuators can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the actuators can be automatically done using processor with iterative changes in the orientation of the antenna by changing the actuators until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. This is similar to the way human vision eyeglass correction is done. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors.

Learning System Plane

A system to optimize data flow in a 5G or 6G network, includes a neural network plane; a control plane coupled to the neural network plane; a management plane coupled to the neural network plane; a data plane coupled to the neural network plane, wherein the neural network plane receives cellular network statistics from the data plane for training, and during run-time, the neural network provides operating parameters to the data, control and management planes; and one or more operations sending resource request to the neural network plane for autonomous resolution that maximizes data flow in the system. The system may include one or more of the following: A moveable surface; and one or more antennas mounted on the moveable surface to change a direction of the antenna to a predetermined target. A pneumatic actuator or electrical motor can be placed under processor control to change the curvature of the lens and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable surface until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target device can be done by moving the actuators up or down. Opposing actuator arrays can be formed to provide two-sided communication antennas. An array of actuators can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. Focus the antenna on BS and UE, and then combine antennas for orthogonal transmissions based on various factors. The focusing of the actuators can be automatically done using processor with iterative changes in the orientation of the antenna by changing the actuators until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. This is similar to the way human vision eyeglass correction is done.

Man-Hole Antenna

A system, comprising one or more actuators; a ground cover above the one or more actuators providing a moveable surface, wherein the actuators move to adjust the curvature of the movable surface; and an antenna mounted on the moveable surface to change a direction of the antenna to a predetermined target.

A system, includes a ground cover (such as a manhole cover) that allows radio signal to pass through; a moveable surface coupled to the cover; and one or more antennas mounted on the moveable surface to change a direction of the antenna to a predetermined target.

The system may include one or more of the following. The processor can control to change the curvature of the surface and to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. The cover is a drop in replacement for existing city light housing to enable ease of upgrading city/street lights to add 5G or 6G/6G active antenna capability.

3G/4G Cell Towers

A system includes a cell tower with a pole and a top portion to mount 4G antennas and a 5G or 6G housing; one or more mechanically steerable active antennas mounted on the 5G or 6G housing and in communication with a predetermined target using 5G or 6G protocols. The system may include one or more of the following. A processor can control to change the curvature of the surface and/or to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A movable surface can be provided on the housing to steer the antenna. The moveable surface can be liquid lens or actuator array as described above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge.

Actuator-Based Active Antenna Array

An antenna, includes an array of antenna element, each connected to a separate transceiver; an array of actuators to point the antenna elements; data converters coupled to the transceivers for up conversion and down conversion; a baseband unit (BBU) with one or more digital signal processors coupled to the data converters; and a broadband connection connecting the baseband unit to a wide area network (WAN). The system may include one or more of the following. A processor can control to change the curvature of the surface and/or to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others.

A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. The learning machine can be used to help steering the antennas to improve connections with UEs. The learning machine can also optimize operation based on data collected from other elements in the transceiver and/or the BBU. The broadband connection can be fiber optic or wireless connection (UWB). The baseband unit can have a high-speed serial link as defined by the Common Public Radio Interface (CPRI), Open Base Station Architecture Initiative (OBSAI), or Open Radio Interface (ORI). The high speed serial link is used to transport the Tx and Rx signals from the BBU to the antennas. The AAS can have passive cooling fins on the housing, or can use evaporative cooling techniques, for example with an enhanced boiling or evaporation microstructure surface including microporous structures; and an electro-deposited surface to enhance a vapor condensation rate, wherein the surface includes a porous medium to replenish condensed liquid back to the microstructure surface by capillary pumping force, wherein the surface is part of an antenna. Since there are many more transceivers/amplifiers in an AAS, each amplifier in an AAS delivers a much lower power when compared to an amplifier in an equivalent RRH.

Beamforming Actuator Driven Active Antenna to Track Moving UEs

A method of communicating data with a UE using an array antenna onboard a cell tower and having a digital beam former (DBF), said array antenna having a plurality of actuators moving the RF radiating elements for providing steerable antenna beams within an antenna footprint region, said DBF providing for each radiating element, beam forming coefficients for controlling characteristics of said steerable antenna beams. The method includes receiving a signal from the UE within a receive one of said steerable antenna beams; determining a location direction of the UE using said signal; generating digital beam forming coefficients to provide a transmit one of said steerable antenna beams in said location direction of the UE; transmitting data from said cell tower to said UE within said one transmit steerable antenna beam; tracking said location direction of said UI as said cell tower and said UE move relative to each other; adjusting said beam forming coefficients associated with one transmit steerable antenna beam in response to the tracking step to maintain said one transmit steerable antenna beam in the location direction of said UE; further adjusting said beam forming coefficients associated with one transmit steerable antenna beam to improve a signal quality of communication signal received at said communication station. The system may include one or more of the following. The antenna arrays can have shape shifting or moving surfaces to directionally aim the antennas. The system remaps the beams to avoid obstructions or issues that affect 5G or 6G/6G transmissions. The Beams can also be changed according to load, usage, time of day, or other factors. The processor can calibrate the connection by examining the RSSI and TSSI and scan the antenna actuators or moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others.

A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. The learning machine can be used to help steering the antennas to improve connections with UEs. The learning machine can also optimize operation based on data collected from other elements in the transceiver and/or the BBU. The broadband connection can be fiber optic or wireless connection (UWB). The baseband unit can have a high-speed serial link as defined by the Common Public Radio Interface (CPRI), Open Base Station Architecture Initiative (OBSAI), or Open Radio Interface (ORI). The high speed serial link is used to transport the Tx and Rx signals from the BBU to the antennas. The AAS can have passive cooling fins on the housing, or can use evaporative cooling techniques, for example with an enhanced boiling or evaporation microstructure surface including microporous structures; and an electro-deposited surface to enhance a vapor condensation rate, wherein the surface includes a porous medium to replenish condensed liquid back to the microstructure surface by capillary pumping force, wherein the surface is part of an antenna. Since there are many more transceivers/amplifiers in an AAS, each amplifier in an AAS delivers a much lower power when compared to an amplifier in an equivalent RRH. Once the learning machine determines the beam sweeping patterns for antenna beams of radio nodes and/or UE devices, beam sweeping commands may be provided to individual radio nodes and/or UE devices via core network and mobile backhaul network. Radio nodes may forward beam sweeping commands to respective UE devices over a wireless channel (e.g., a wireless control channel). Additionally, network control device may prioritize particular antenna beams, where high priority beams are reserved to service users having high networking requirements. In an embodiment, high priority beams may be classified as “active” beams. Beams having lower priority than active beams may be classified as “candidate” beams, which may be selected to replace active beams if necessary. Beams having lower priority than active and candidate beans may be classified as “alternative” beams, which may be used as backup beams in case an active beam is temporarily blocked and a suitable candidate beam is unavailable. In addition, the priority of beams may be updated according to the time of day, particular days or dates (e.g., workdays, weekends, holidays, etc.), and/or the time of season (to account for seasonal effects of propagation, seasonal variations of the density of users, and/or variations in objects which may block signal propagation). In addition, network control device may also use prior knowledge of prior beam sweeping patterns to influence the determination of current and/or future beam sweeping patterns. Moreover, the beam sweeping patterns associated with control signaling broadcast between radio nodes and UE devices may be adjusted differently than antenna beams associated with data bearing channels. Additionally, differences between beam sweeping patterns may be based on the beam width of individual antenna beams and/or the number of beam sweeping positions.

Multi-Level 5G or 6G/6G Antenna

An antenna system, includes a high power active antenna array mounted on a cell tower, balloon, or a drone, the high power active antenna array controlled by a BBU with a broadband connection; a plurality of medium power active antenna arrays wirelessly coupled to the high power active antenna, wherein the medium power antenna array relays data transmission between the high power active antenna array and a UE to reduce RF exposure on biologics. This reduces cancer risk on users. The system may include one or more of the following: The antenna arrays can have shape shifting or moving surfaces to directionally aim the antennas. The high power active antenna can have an array of antenna element, each connected to a separate transceiver; an array of actuators to point the antenna elements; data converters coupled to the transceivers for up conversion and down conversion; the baseband unit (BBU) with one or more digital signal processors coupled to the data converters. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others.

A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. The learning machine can be used to help steering the antennas to improve connections with UEs. The learning machine can also optimize operation based on data collected from other elements in the transceiver and/or the BBU. The broadband connection can be fiber optic or wireless connection (UWB). The baseband unit can have a high-speed serial link as defined by the Common Public Radio Interface (CPRI), Open Base Station Architecture Initiative (OBSAI), or Open Radio Interface (ORI). The high speed serial link is used to transport the Tx and Rx signals from the BBU to the antennas. The AAS can have passive cooling fins on the housing, or can use evaporative cooling techniques, for example with an enhanced boiling or evaporation microstructure surface including microporous structures; and an electro-deposited surface to enhance a vapor condensation rate, wherein the surface includes a porous medium to replenish condensed liquid back to the microstructure surface by capillary pumping force, wherein the surface is part of an antenna. Since there are many more transceivers/amplifiers in an AAS, each amplifier in an AAS delivers a much lower power when compared to an amplifier in an equivalent RRH.

The medium power antenna arrays can be mounted on traffic lights or street lights as replacement lights with 5G or 6G relay capacity, and further can provide fast response time for vehicular navigation/control. The medium power antenna arrays can be mounted on car, bus, trucks, drones, local stores, mailboxes. The host for the medium power antenna can collect a usage fee in exchange.

Car/Truck/Van/Bus/Vehicle with 5G or 6G Antenna Small Cells

A system, includes a moveable vehicle including a pole and a top portion to mount 4G antennas and a 5G or 6G housing, wherein the pole is retractable and extendable during 5G or 6G operation; one or more antennas mounted on the 5G or 6G housing and in communication with a predetermined target using 5G or 6G protocols. The system may include one or more of the following: A processor can control to change the curvature of the surface and/or to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A movable surface can be provided on the housing to steer the antenna. The moveable surface can be liquid lens or actuator array as described above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge. A frame can be used with an antenna support structure having means to permit its collapsing and a waveguide antenna mounted to said support structure and including a plurality of integrally connected tubular waveguide cells that form a cell array that focuses transmitted signals onto a signal processing device; said lens waveguide antenna having means to permit its collapsing and a second support structure mount that operatively connects said collapsible support structure to a mounting surface to correctly position said collapsible lens waveguide antenna relative to said signal processing device when said antenna is operationally deployed. A fleet of drones can operate and navigate as a flock of birds to provide real time adjustment in coverage as needed. The flock of birds antenna has power and autonomous navigation and can self-assemble and scatter as needed to avoid physical and wireless communication obstacles.

Glider/Helicopter/Baloon/Ship/Low Earth Orbit Drone with 5G or 6G Antenna

A system, includes an airborne frame to mount 4G antennas and a 5G or 6G housing; one or more antennas mounted on the 5G or 6G housing and in communication with a predetermined target using 5G or 6G protocols.

The system may include one or more of the following: A processor can control to change the curvature of the surface and/or to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A movable surface can be provided on the housing to steer the antenna. The moveable surface can be liquid lens or actuator array as described above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge. The air frame has an antenna support structure having means to permit its collapsing and a waveguide antenna mounted to said support structure and including a plurality of integrally connected tubular waveguide cells that form a cell array that focuses transmitted signals onto a signal processing device; said lens waveguide antenna having means to permit its collapsing and a second support structure mount that operatively connects said collapsible support structure to a mounting surface to correctly position said collapsible lens waveguide antenna relative to said signal processing device when said antenna is operationally deployed. A fleet of drones can operate and navigate as a flock of birds to provide real time adjustment in coverage as needed. The flock of birds antenna has power and autonomous navigation and can self-assemble and scatter as needed to avoid physical and wireless communication obstacles. The cars/trucks/buses can carry ads as a monetization system. Alternatively, personal vehicles can be paid a percentage of the traffic relayed by their vehicles.

Cell Phone Antenna

A system, includes a cell phone housing; and one or more antennas mounted on the housing, the antenna being selectable to avoid discharging RF energy into a human body and to target RF energy at a predetermined target. The system may include one or more of the following: A processor can control to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A processor controlled moveable surface can be provided on the tree (such as a leaf, flower, or fruit on the tree), wherein the moveable surface can be liquid lens or actuators that move the surface as detailed above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge.

Cell Phone Body with Movable Antenna

A system, includes a cell phone housing having a moveable surface; and one or more antennas mounted on a moveable surface, wherein the antenna direction is changed by the moveable surface to target RF energy at a predetermined target. The system may include one or more of the following: A processor can control to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens or actuators until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A processor controlled moveable surface can be provided on the tree (such as a leaf, flower, or fruit on the tree), wherein the moveable surface can be liquid lens or actuators that move the surface as detailed above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge.

Cell Phone Liquid Metal Antenna

A system, includes a cell phone housing; a plurality of channels on the housing; and one or more liquid antenna movable on the channels to change a frequency or a direction of the antenna to a predetermined target. The system may include one or more of the following: A processor can control to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A processor controlled moveable surface can be provided on the tree (such as a leaf, flower, or fruit on the tree), wherein the moveable surface can be liquid lens or actuators that move the surface as detailed above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge. The liquid antenna can be a liquid metal such as mercury or non-toxic material. The liquid antenna can be electrically controlled to travel to predetermined channels to change frequency or direction of the antenna. The liquid antenna can be MEMS based.

Cancer Minimization of 5G or 6G Cell Phones

1. A system, includes a 5G or 6G transceiver spaced apart from a user to minimize 5G or 6G radiation directly on the user body; and a display and microphone/speaker coupled to the 5G or 6G transceiver which is nearer to the user body than the 5G or 6G transceiver. The system may include one or more of the following: A processor can control to change the directionality of the antenna to reduce RF energy on human body. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the antennas until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A processor controlled moveable surface can be provided on the tree (such as a leaf, flower, or fruit on the tree), wherein the moveable surface can be liquid lens or actuators that move the surface as detailed above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge. The 5G or 6G transceiver can be part of a portable computer, laptop computer, tablet computer, brief case, or any utensil/appliance that can be away from the body to reduce RF energy on the human body, but still linked to the display and mike/speaker to act as a UI for the user. The user clothing can be electrically conductive to reduce RF energy on the body.

Cancer Minimization of 5G or 6G Vehicles

A vehicle, includes a 5G or 6G transceiver to receive 5G or 6G transmission; a faraday cage isolating the user from the 5G or 6G transceiver; and a display and microphone/speaker in the faraday cage and in communication with the 5G or 6G transceiver which is nearer to the user body than the 5G or 6G transceiver.

Powering of IOT Devices Using 5G or 6G Energy

An IOT system, includes a housing having a moveable surface; one or more antennas mounted on a moveable surface, wherein the antenna direction is changed by the moveable surface to receive RF energy from a small cell; a capacitor, battery or energy storage device coupled to the antennas to store received energy; and a power regulator coupled to the capacitor, battery, or energy storage A processor can control to change the directionality of the antenna. The processor can calibrate the RF link between the tower and the client device. The processor can calibrate the connection by examining the RSSI and TSSI and scan the moveable lens or actuators until the optimal RSSI/TSSI levels (or other cellular parameters) are reached. The scanning of the target client/device can be done by injecting or removing liquid from moveable surface, or can be done by moving actuators coupled to the surface. Opposing pairs of lenses can be formed to provide two-sided communication antennas. An array of actuator/antenna can be used (similar to bee eyes), each antenna is independently steerable to optimize 5G or 6G transmission. Fresnel lens can be used to improve SNR. The focusing of the 5G or 6G signals to the target client/device can be automatically done using processor with iterative changes in the orientation of the antenna by changing the curvature or shape of the surface until predetermined criteria is achieved such as the best transmission speed, TSSI, RSSI, SNR, among others. A learning machine such as neural network or SVM can be used over the control/management plane of the 5G or 6G network to optimize 5G or 6G parameters based on local behaviors. A processor controlled moveable surface can be provided on the tree (such as a leaf, flower, or fruit on the tree), wherein the moveable surface can be liquid lens or actuators that move the surface as detailed above. Cameras and sensors can be positioned to capture security information. Learning machine hardware can provide local processing at the edge.

Antenna with Evaporative Cooling for 5G or 6G Power Amplifiers

A heat spreader to cool a heated region of a device, includes an enhanced boiling or evaporation microstructure surface including microporous structures; and an electro-deposited surface to enhance a vapor condensation rate, wherein the surface includes a porous medium to replenish condensed liquid back to the microstructure surface by capillary pumping force, wherein the surface is part of an antenna. In implementations, each surface comprises a plate. The electro-deposited surfaces utilize boiling (evaporation), condensation, and capillary pumping action. Liquid is vaporized or boiled from the electro-deposited surface designed for boiling (evaporation) enhancement. Vapor is condensed at the enhanced surface for condensation. The enhanced surface for condensation is formed by electro-deposition. Condensed liquid is supplied back to the heated region by another electro-deposited surface aimed for capillary pumping action. One or more structures are mounted on at least one of the opposing surfaces. The first and second opposing surfaces are separated by a small gap. The first and second opposing surface have a first separation distance above a predetermined region on device and a second separation distance surrounding the predetermined region and wherein the second separation distance is larger than the first separation distance.

A method to cool an electronic device includes forming an enhanced boiling or evaporation surface including microporous structures; forming an electro-deposited surface to improve a condensation rate of vapor, wherein the surface includes a porous medium to replenish condensed liquid back to the first surface by capillary pumping force; and communicating radio signal using the other side of the condensation surface. In implementation, the method includes improving a heat transfer coefficient in a low-profile vapor chamber using wick structures having projections formed by electrodepositing a metal on the target surfaces at a first current density followed by strengthening at one or more second current densities lower than the first current density. Projections are formed with nucleate boiling cavities on the surface from local heating source, increased capillary pumping action of the wick structure, and augmented condensation rate of vapors. The electronic device is cooled using

CLAIMS

Claims ( 20 )

What is claimed is:

1. A system to perform edge processing for a predetermined target, comprising:

one or more cellular transceivers with one or more antennas that are electrically or mechanically steerable to the predetermined target;

a processor to control communication with the predetermined target; and

one or more edge processing modules coupled to the processor and the one or more antennas to provide low-latency computation for the predetermined target; and

a container to house the transceiver, processor running a virtual radio access network, and one or more edge processing modules, the container moveable to a location requiring increased edge processing.

2. The system of claim 1 , wherein the container fits requirement to be deployed without a construction permit.

3. The system of claim 1 , wherein the processor is coupled to fiber optics cable to communicate with a cloud-based radio access network (RAN) or a remote RAN.

4. The system of claim 1 , comprising an antenna mast, wherein the antenna mast is inside the container or external to the container.

5. The system of claim 1 , wherein the edge processing module comprises at least a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).

6. The system of claim 1 , wherein the edge processing module and the antenna comprise one unit.

7. The system of claim 1 , comprising a cryogenic cooling system to cool the container.

8. The system of claim 1 , wherein the cellular transceiver comprises a 5G or 6G transceiver.

9. The system of claim 1 , wherein the processor coordinates beam sweeping by the one or more antennas with radio nodes or user equipment (UE) devices based upon service level agreement, performance requirement, traffic distribution data, networking requirements or prior beam sweeping history.

10. The system of claim 9 , wherein the beam sweeping is directed at a group of autonomous vehicles, a group of virtual reality devices, or a group of devices having a service agreement with a cellular provider.

11. The system of claim 1 , comprising a neural network coupled to a control plane, a management plane, or a data plane to optimize 5G or 6G parameters.

12. The system of claim 1 , comprising one or more cameras and sensors to capture security information.

13. The system of claim 1 , wherein the container includes edge sensors including LIDAR and RADAR.

14. The system of claim 1 , comprising a camera for individual identity identification.

15. The system of claim 1 , wherein the edge processing module streams data to the predetermined target to minimize loading the target.

16. The system of claim 1 , wherein the edge processing module shares workload with a core processing module located at a head-end and a cloud module located at a cloud data center, each processing module having increased latency and each having a processor, a graphical processing unit (GPU), a neural network, a statistical engine, or a programmable logic device (PLD).

17. The system of claim 1 , comprising an edge learning machine in the housing to provide local edge processing for Internet-of-Things (IOT) sensors with reduced off-chip memory access.

18. The system of claim 17 , wherein the edge learning machine uses pre-trained models and modifies the pre-trained models for a selected task.

19. The system of claim 1 , comprising a cellular device for a person crossing a street near a city light or street light, the cellular device emitting a person to vehicle (P2V) or a vehicle to person (V2P) safety message.

20. The system of claim 1 , comprising a cloud trained neural network whose network parameters are reduced before transferring to an edge neural network in the container.

US18/128,969

2019-05-07

2023-03-30

Cellular communication

Active

US11894620B2

( en )

Priority Applications (1)

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Priority Date

Filing Date

Title

US18/128,969

US11894620B2

( en )

2019-05-07

2023-03-30

Cellular communication

Applications Claiming Priority (4)

Application Number

Priority Date

Filing Date

Title

US16/404,853

US10461421B1

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