System and methods for AI-assisted wireless channel prediction and estimation
AssigneeAira Technologies, Inc.
InventorsYihan Jiang, RaviKiran Gopalan, Arman Rahimzamani, Anand Chandrasekher
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Abstract
This disclosure relates to methods, systems, and devices for AI/ML assisted wireless channel fingerprinting, estimation, and prediction. In one example embodiment, a method of combined AI/ML assisted wireless channel fingerprinting and channel prediction is disclosed. The method includes using a trained neural network to fingerprint the channel with the channel fingerprinting results advantageously being leveraged to improve the channel prediction.
Claims20
A system, comprising:
- one or more hardware processors;
- and
- one or more non-transitory machine-readable storage media encoded with instructions that, when executed by the one or more hardware processors, cause the system to perform operations comprising:
- receiving an uplink signal from a user equipment (UE), the uplink signal comprising an sounding reference signal (SRS) slot followed by multiple non-SRS slots;
- obtaining an SRS by demodulating the SRS slot;
- formatting the SRS into a column vector;
- and
- generating a predicted channel estimate for the non-SRS slots by applying the column vector as input to a trained artificial intelligence (AI) model, wherein responsive to the input, the trained AI model outputs the predicted channel estimate, wherein the trained AI model has been trained with a training data set, and wherein the training data set includes historical low-resolution two dimensional image representations (TDIR) of channel estimates over time and frequency.
One or more non-transitory machine-readable storage media encoded with instructions that, when executed by one or more hardware processors of a computing system, cause the computing system to perform operations comprising:
- receiving an uplink signal from a user equipment (UE), the uplink signal comprising an sounding reference signal (SRS) slot followed by multiple non-SRS slots;
- obtaining an SRS by demodulating the SRS slot;
- formatting the SRS into a column vector;
- and
- generating a predicted channel estimate for the non-SRS slots by applying the column vector as input to a trained artificial intelligence (AI) model, wherein responsive to the input, the trained AI model outputs the predicted channel estimate, wherein the trained AI model has been trained with a training data set, and wherein the training data set includes historical low-resolution two dimensional image representations (TDIR) of channel estimates over time and frequency.
A computer-implemented method comprising:
- receiving an uplink signal from a user equipment (UE), the uplink signal comprising an sounding reference signal (SRS) slot followed by multiple non-SRS slots;
- obtaining an SRS by demodulating the SRS slot;
- formatting the SRS into a column vector;
- and
- generating a predicted channel estimate for the non-SRS slots by applying the column vector as input to a trained artificial intelligence (AI) model, wherein responsive to the input, the trained AI model outputs the predicted channel estimate, wherein the trained AI model has been trained with a training data set, and wherein the training data set includes historical low-resolution two dimensional image representations (TDIR) of channel estimates over time and frequency.
Dependencies are inferred from the claim text.