Methods and apparatus for monitoring performance of two-sided ai/ml models in a wireless communication system

Patent metadata

Field	Value
Publication number	US-2025142366-A1
Application number	US-202418934128-A
Country	US
Kind code	A1
Filing date	Oct 31, 2024
Priority date	Nov 1, 2023
Publication date	May 1, 2025
Grant date	—

Abstract

The present disclosure provides methods and apparatuses for monitoring performance of two-sided AI/ML models in a wireless communication system. The method comprises of computing at least one first basis vector of an input data matrix and obtaining a compressed data matrix, by passing the data matrix through an AI/ML encoder model. The method further comprises of transmitting, the compressed data matrix in one of a control or a data channel.

First claim

What is claimed is:

1 . A method for monitoring performance of a two-sided artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the method comprising:
computing at least one first basis vector of an input data matrix; obtaining a compressed data matrix, by passing the data matrix through an AI/ML encoder model; and transmitting, the compressed data matrix in one of a control or a data channel.

2 . The method as claimed in claim 1 , wherein computing the at least one first basis vector of the input data matrix further comprises;
obtaining an at least encoded basis vector using at least one basis vector of the data matrix; obtaining an at least one quantized basis vector by quantizing the at least one encoded basis vector; and transmitting, the at least one quantized basis vector in one of a control or a data channel.

3 . A method for monitoring performance of a two-sided artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the method comprising:
computing at least one first basis vector of an input data matrix; obtaining a compressed data matrix, by passing the data matrix through an AI/ML encoder model; receiving at least one second basis vector, wherein the at least one second basis vector is obtained by encoding a decompressed data matrix; and obtaining a performance metric (ρ), by computing an error between the at least one first basis vector and the at least one second basis vector.

4 . A method for monitoring performance of a two-sided artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the method comprising:
receiving a compressed data matrix in one of a control or a data channel; obtaining a decompressed data matrix, by passing the compressed data matrix through the AI/ML decoder model; computing at least one first basis vector using the decompressed data matrix; and transmitting the at least one first basis vector.

5 . A method for monitoring performance of a two-sided artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the method comprising:
receiving a compressed data matrix in one of a control or a data channel; receiving, at least one quantized basis vector of the data matrix; obtaining a decompressed data matrix, by passing the compressed data matrix through the AI/ML decoder model; computing at least one first basis vector using the decompressed data matrix; reconstructing at least one second basis vector using the at least one quantized basis vector of the data matrix; and obtaining a performance metric (ρ), by computing an error between the at least one first basis vector and the at least one second basis vector.

6 . The method as claimed in claim 1 , wherein an error is computed as one of:
a difference between the least one first basis vector and the at least one second basis vector; a dot product of least one first basis vector and the at least one second basis vector; and a correlation between the least one first basis vector and the at least one second basis vector.

7 . The method as claimed in claim 1 , wherein the at least one of the at least one first basis vector and the at least one second basis vector is obtained by applying a standard basis function.

8 . The method as claimed in claim 1 , wherein a standard basis function is one of Fourier transform based or eigen valued decomposition based.

9 . The method as claimed in claim 1 , wherein encoding and quantizing of at least one basis vector of the input is based on sparsity of the input data.

10 . The method as claimed in claim 1 , wherein the data matrix comprises at least one of information related to location, channel impulse response (CIR), channel state information (CSI) matrix.

11 . The method as claimed in claim 1 , wherein the performance metric (ρ) is at least one of indicators for fallback to legacy non-AI/ML based methods, AI/ML model retraining or AI/ML model switching in a lifecycle of an AI/ML model.

12 . The method as claimed in claim 1 , wherein the AI/ML model is a two-sided autoencoder based model.

13 . The method as claimed in claim 12 ,
wherein the two-sided model consists of an encoder model and a decoder model; wherein the encoder model obtains relevant feature from the data matrix and maps it into a compressed representation and the decoder model reconstructs the original data matrix from the compressed representation.

14 . The method as claimed in claim 13 ,
wherein the encoder model is at a wireless entity and the decoder model is at a different wireless entity and a final output of the two-sided model is obtained at a location of the AI/ML decoder model.

15 . The method as claimed in claim 1 , wherein a periodicity of monitoring the performance of a two-sided AI/ML model in the wireless communication system at least one of,
every inference operation, several coherence units of the channel.

16 . An apparatus for monitoring artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the apparatus comprising:
one or more processor; one or more memory in electronic communication with the one or more processor, and instructions stored in the one or more memory and executable by the one or more processor configured to: compute at least one first basis vector of an input data matrix; obtain a compressed data matrix, by passing the data matrix through an AI/ML encoder model; and transmit, the compressed data matrix in one of a control or a data channel.

17 . The apparatus as claimed in claim 16 , wherein the one or more processor configured to compute the at least one first basis vector of the input data matrix further configured to:
obtain an at least encoded basis vector using the at least one basis vector of the data matrix; obtain an at least one quantized basis vector by quantizing the at least one encoded basis vector; and transmit, the at least one quantized basis vector in one of a control or a data channel.

18 . An apparatus for monitoring artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the apparatus comprising:
one or more processor; one or more memory in electronic communication with the one or more processor, and instructions stored in the one or more memory and executable by the one or more processor configured to: compute at least one first basis vector of an input data matrix; obtain a compressed data matrix, by passing the data matrix through an AI/ML encoder model; receive at least second basis vector; wherein the at least one second basis vector is obtained by encoding a decompressed data matrix; and obtain a performance metric (ρ), by computing the error between the at least one first basis vector and the at least one second basis vector.

19 . An apparatus for monitoring artificial intelligence/machine learning (AI/ML) model in a wireless communication system, the apparatus comprising:
one or more processor; one or more memory in electronic communication with the one or more processor, and instructions stored in the one or more memory and executable by the one or more processor configured to: receive a compressed data matrix in one of a control or a data channel; receive, at least one quantized basis vector of the data matrix; obtain a decompressed data matrix, by passing the compressed data matrix through an AI/ML decoder model; compute at least one first basis vector using the decompressed

Assignees

• Centre Of Excellence In Wireless Tech
• Indian Inst Tech Madras

Inventors

• Arun Advith
• Sankar Shiv
• Klutto Milleth Jeniston Deviraj
• Ramamurthi Bhaskar

Classifications

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04W24/02 Primary

  Arrangements for optimising operational condition · CPC title

• G06F16/1744 Primary

  using compression, e.g. sparse files · CPC title