Cellular system

Patent metadata

Field	Value
Publication number	US-12021314-B2
Application number	US-202318135125-A
Country	US
Kind code	B2
Filing date	Apr 15, 2023
Priority date	May 7, 2019
Publication date	Jun 25, 2024
Grant date	Jun 25, 2024

Abstract

A system includes a housing with one or more edge processors to handle processing on behalf of a mobile target or to provide local data to the mobile target or to provide artificial intelligence for the mobile target; one or more antennas coupled to the housing; and a processor to control a directionality of the antennas in communication with the mobile target using 5G or 6G protocols.

First claim

What is claimed is:

1. A system, comprising:
a mobile target to receive road or traffic parameters from one or more traffic sensors, road sensors or cameras;
a housing with one or more edge processors coupled to the one or more traffic sensors, road sensors or cameras to handle processing on behalf of the mobile target with a predetermined latency to provide augmented or virtual reality rendering data to the mobile target or to provide artificial intelligence for the mobile target, wherein the mobile target optimizes performance and power consumption by offloading augmented or virtual processing to the one or more edge processors and applying a received edge processing result within the predetermined latency to augment processing by the mobile target; one or more antennas coupled to the housing; and
a processor to communicate with the mobile target using 5G protocols.

2. The system of claim 1 , wherein the processor calibrates a radio link between a transceiver in the housing and a client device.

3. The system of claim 1 , wherein local data comprises images and wherein the processor process images from one or more mobile target cameras for location identification, ridesharing pick-up, or delivery.

4. The system of claim 1 , wherein local data comprises images and wherein the one or more edge processors detect real time hazard detection or road signs.

5. The system of claim 1 , wherein the processor moves actuators coupled to the antennas.

6. The system of claim 1 , wherein local data comprises weather or location data.

7. The system of claim 1 , wherein the one or more edge processors handle video content, healthcare, robotics, autonomous vehicle, augmented reality, virtual reality, extended reality, factory automation, gaming, asset tracking, or surveillance.

8. The system of claim 1 , wherein the mobile target receives high definition local road map data from the edge processors.

9. The system of claim 1 , wherein the local data comprises data affecting road conditions, and wherein the one or more edge processors provide traffic, transit, search, routing, telematics, weather, tracking, positioning, high-definition map, or geoenrichment data.

10. The system of claim 1 , wherein the one or more edge processors comprise one or more learning machines or neural networks.

11. The system of claim 1 , comprising one or more cameras and sensors in the housing to capture security information.

12. The system of claim 1 , wherein the one or more edge processors perform predictive analytics, consumer targeting, fraud detection, or demand forecast.

13. The system of claim 1 , comprising a camera and a processor for individual identity identification.

14. The system of claim 1 , wherein the one or more edge processors applies artificial intelligence to location data.

15. The system of claim 1 , wherein the one or more edge processors analyze sound, scent, or chemical data from sensors in the housing.

16. The system of claim 1 , comprising an edge learning machine in the housing to provide local edge processing for one or more Internet-of-Things (IOT) sensors.

17. The system of claim 1 , comprising an edge learning machine that uses pre-trained models and modifies the pre-trained models for a selected task.

18. 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.

19. A system for A system for real-time resource allocation in a wireless network, comprising:
a mobile target to receive parameters from one or more sensors;
a housing with one or more edge processors coupled to one or more sensors to handle processing on behalf of a target with a predetermined latency or to provide artificial intelligence for a target;
determining resource allocation in response to numbers of users and use cases on the wireless network;
applying artificial intelligence (AI) to allocate resources based on real-time demand and network conditions for beam management, spectrum allocation, and scheduling function to handle resource allocation demands and resource utilization with AI to process data with a predetermined latency;
one or more antennas coupled to the housing; and
a processor to communicate with the mobile target using 5G protocols.

20. A method in a wireless network, comprising:
receiving traffic parameters from one or more sensors or cameras;
with one or more edge processors coupled to the one or more sensors or cameras, processing on behalf of a target with a predetermined latency of providing artificial intelligence operation for the target;
determining resource allocation in response to numbers of users and use cases on the wireless network; and
applying AI to a physical layer (PHY) to perform digital predistortion, channel estimation, and channel resource optimization;
adjusting transceiver parameters for optimizing resource allocation with the applied AI at the PHY.

Assignees

• Tran Bao
• Tran Ha

Inventors

• Tran Bao
• Tran Ha

Classifications

• H04B17/309

  Measuring or estimating channel quality parameters · CPC title

• H01Q3/46 Primary

  Active lenses or reflecting arrays · CPC title

• H04L9/50 Primary

  using hash chains, e.g. blockchains or hash trees · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title