Cellular system

1. A method for improving call performance in a wireless network, comprising:

applying Artificial Intelligence (AI) techniques at the physical layer (PHY) to perform digital predistortion, channel estimation, and channel resource optimization including applying AI-based channel state information compression to compress feedback data from user equipment to a base station and applying AI-based fingerprinting processes to optimize positioning and localization in indoor environments and mapping disruptions to propagation patterns caused by individuals in a wireless environment;

adjusting transceiver parameters during a call using an AO-based autoencoder design; and

optimizing resource allocation and improving call quality between two devices by deploying AI at the PHY.

2. The method of claim 1 , comprising applying AI above the physical layer (above-PHY) to perform beam management, spectrum allocation, and scheduling functions.

3. The method of claim 1 , comprising responding to real-time allocation demands by leveraging AI algorithms and optimization techniques.

4. The method of claim 1 , comprising optimizing the management of network resources for competing users and use cases in a core system.

5. The method of claim 1 , comprising:

estimating user position based on individualized 5G signal variations, overcoming traditional obstacles associated with localization methods that rely on comparisons between received signal strength indication and signal strength in providers' databases; and

using a feedback loop informing the base station.

6. The method of claim 1 , comprising applying AI-based fingerprinting to optimize positioning and localization in indoor environments, by mapping disruptions to propagation patterns caused by individuals entering and disrupting the environment.

7. The method of claim 1 , comprising estimating user position based on individualized 5G signal variations, overcoming traditional obstacles associated with localization methods that rely on comparisons between received signal strength indication and signal strength in providers' databases.

8. The method of claim 1 , comprising compressing data with AI-based channel state information and providing compressed feedback data from user equipment to a base station.

9. The method of claim 1 , comprising providing a feedback loop informing the base station's attempt to improve call performance remains within the available bandwidth for preventing dropped calls.

10. The method of claim 1 , comprising

collecting and analyzing individualized 5G signal variations caused by disruptions to propagation patterns in indoor environments; and

mapping disruptions to specific locations within the environment to create a fingerprint database;

applying AI techniques to match real-time signal variations with the fingerprint database to estimate the position of a user.

11. A system for efficient channel state information (CSI) management in wireless networks, comprising:

using Artificial Intelligent (AI) to compress feedback data from user equipment to a base station and to reduce the size of the CSI feedback to use available bandwidth including applying AI-based channel state information compression to compress feedback data from user equipment to a base station and applying AI-based fingerprinting processes to optimize positioning and localization in indoor environments and mapping disruptions to propagation patterns caused by individuals in a wireless environment;

maintaining a reliable feedback loop between user equipment and the base station for improving call performance; and

preventing the feedback loop from exceeding the available bandwidth to avoid dropped calls and maintaining optimal network performance.

12. The method of claim 11 , comprising transmitting the compressed CSI feedback data from user equipment to a base station.

13. The method of claim 11 , comprising applying AI to optimize resource allocation in response to increasing numbers of users and use cases on the network.

14. The method of claim 11 , comprising using AI to dynamically allocate resources based on real-time demand and network conditions.

15. The method of claim 11 , comprising applying beam management, spectrum allocation, and scheduling function to optimize the management of wireless system resources.

16. The method of claim 11 , comprising:

applying AI techniques at the physical layer (PHY) to perform digital predistortion, channel estimation, and channel resource optimization;

adjusting transceiver parameters during a call using an AI-based autoencoder design; and

optimizing resource allocation and improving call quality between two devices by deploying AI at the PHY.

17. A method for network management and resource optimization in a wireless system, comprising:

analyzing network conditions and user demands using Artificial Intelligent (AI) techniques above the physical layer;

applying AI algorithms to dynamically manage and allocate resources in real-time including applying AI-based channel state information compression to compress feedback data from user equipment to a base station and applying AI-based fingerprinting processes to optimize positioning and localization in indoor environments and mapping disruptions to propagation patterns caused by individuals entering and disrupting a wireless environment;

optimizing beam management, spectrum allocation, and scheduling function to efficiently utilize the resources of the core system; and

applying AI-based network management and resource optimization to increase wireless network capacity.

18. The method of claim 17 , comprising:

applying AI-based fingerprinting processes to optimize positioning and localization in indoor environments, by mapping disruptions to propagation patterns caused by individuals entering and disrupting a wireless environment;

estimating user position based on individualized 5G signal variations, overcoming traditional obstacles associated with localization methods that rely on comparisons between received signal strength indication and signal strength in providers' databases;

applying AI-based channel state information compression to compress feedback data from user equipment to a base station; and

using a feedback loop informing the base station.

19. The method of claim 17 , comprising adjusting transceiver parameters during a call using an AI-based autoencoder design.

20. The method of claim 17 , comprising:

using AI to compress feedback data from user equipment to a base station and to reduce the size of channel state information (CSI) feedback to use available bandwidth;

maintaining a reliable feedback loop between user equipment and the base station for improving call performance; and

preventing the feedback loop from exceeding the available bandwidth to avoid dropped calls and maintaining optimal network performance.

21. The method of claim 17 , comprising:

applying AI techniques at the physical layer (PHY) to perform digital predistortion, channel estimation, and channel resource optimization;

adjusting transceiver parameters during a call using an AI-based autoencoder design; and

optimizing resource allocation and improving call quality between two devices by deploying AI at the PHY.