Context-Aware Data Receiver for Low-Complexity Signal Detection
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Solution Overview
Problem
Existing data receivers struggle to adapt efficiently to varying operating conditions such as hardware impairments and noise variations, leading to suboptimal performance and complexity in signal detection.
Innovation Solution
A method involving a first function to determine a compact context representation of the receiver's operating conditions, followed by a second function to detect data based on this context, using machine learning techniques like CNNs and FCNNs to reduce complexity and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning approaches are used to adapt receiver processing to current operating conditions, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the receiver processing into two distinct functions: a first function that determines context parameters representing operating conditions, and a second function that performs data detection based on these context parameters. This segmentation allows the complex machine learning model to be divided into manageable parts, improving detection accuracy while controlling device complexity through functional separation.
Solution Approach 2:
The patent extracts the context determination as a separate function that outputs compact context parameters. By taking out the context determination from the main detection process and representing operating conditions in a compact form, the system achieves high detection accuracy without overwhelming device complexity, as the context parameters serve as condensed information about receiver and transmitter conditions.
2Reliability
If receiver processing is adapted to account for hardware impairments and noise variations, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the receiver to automatically determine its own context parameters and adapt its detection processing accordingly. The first function autonomously assesses operating conditions including hardware impairments and noise variations, and the second function automatically adjusts detection based on these parameters, improving reliability without requiring manual intervention or complex user operation.
Solution Approach 2:
The patent employs feedback mechanisms where the determined context parameters continuously inform and adjust the detection processing. The system monitors operating conditions, determines context parameters that reflect current receiver and transmitter states, and uses these parameters to adaptively adjust detection algorithms, creating a closed-loop system that maintains high reliability under varying conditions while operating autonomously.
Data Source
AI summary
A computer implemented method for detecting data (y) comprised in a part (x) of a received signal (w) of a communication system (100), wherein the received signal (w) is associated with a population and where the part (x) of the received signal (w) is associated with a sub-population of the population, the method comprising: configuring (S1) a first function (f1) to determine a context (c) of the received signal (w), wherein the context (c) is indicative of a state of the received signal (w), configuring (S2) a second function (f2) to detect the data (y) based on the part (x) of the received signal, wherein the second function (f2) is arranged to be parameterized by the context (c), and detecting (S3) the data (y) by the first (f1) and second (f2) functions.