Digital UE Receiver Model for Link Adaptation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently adapting to dynamic channel conditions due to high overhead in channel state feedback (CSF) reporting and limited tracking rates, which affect communication link performance.
Innovation Solution
The method involves estimating the response of a user equipment (UE) receiver using a digital representation, such as a machine learning model, to determine optimal link adaptation parameters, thereby reducing the need for frequent CSF reporting and enhancing tracking rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent channel state feedback reporting is used to track channel conditions, then link adaptation performance is improved, but signaling overhead increases
Solution Approach 1:
The patent creates a digital representation (copy) of the UE receiver that includes a machine learning model. This copy allows the network entity to simulate receiver behavior and predict decoding performance without requiring frequent feedback reports from the UE, thereby reducing signaling overhead while maintaining link adaptation accuracy
Solution Approach 2:
The patent performs preliminary actions by obtaining receiver parameters and training machine learning models in advance. These pre-trained models enable the network entity to quickly simulate receiver responses to different modulation and coding schemes without needing real-time feedback, reducing the frequency of channel state feedback reporting
2Reliability
If traditional channel state feedback mechanisms are used, then communication reliability is maintained, but tracking rate is limited
Solution Approach 1:
The patent replaces the traditional mechanical feedback loop (UE measures channel -> UE reports to network -> network adapts) with a simulation-based system using machine learning models. The network entity directly simulates receiver performance using pre-trained models, eliminating the feedback delay and enabling faster tracking of channel conditions
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the channel conditions and the link adaptation decisions. These models mediate by predicting receiver performance for different modulation and coding schemes, allowing the network to quickly determine optimal parameters without waiting for actual feedback
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for simulating a receiver for link adaptation. A method for wireless communications by an apparatus includes obtaining, from an UE, parameters of a UE receiver; determining channel characteristics of a communication channel between the apparatus and the UE based on a measurement of a signal received from the UE; estimating a response of the UE receiver communicating on the communication channel having the channel characteristics based on a digital representation of the UE receiver, wherein the digital representation of the UE receiver is based on the parameters of the UE receiver; determining, based on the estimated response, at least one parameter for communication on the communication channel with the UE; sending, to the UE, an indication of the at least one parameter; and communicating with the UE in accordance with the at least one parameter.