Context-Aware Data Receiver for Low-Complexity Signal Detection
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Solution Overview
Problem
Existing data receivers struggle to adapt effectively to varying operating conditions, such as hardware impairments and noise variations, leading to suboptimal performance and complexity in data detection.
Innovation Solution
A method involving a first function to determine a compact context representation of the receiver operating conditions using machine learning techniques, followed by a second function to detect data based on this context, utilizing neural networks or clustering algorithms to optimize detection under varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If receiver processing is adapted to current operating conditions using machine learning, then data detection reliability is improved, but device complexity increases
Solution Approach 1:
The receiver processing is segmented into two distinct functions: a first function that determines a compact context representation of operating conditions, and a second function that detects data based on this context. This segmentation reduces overall complexity by allowing each function to be optimized independently and enabling efficient training and maintenance of the adaptive processing system.
2Measurement precision
If receiver processing is adapted to current operating conditions, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The context determination function extracts and processes only the essential information from the received signal to create a compact representation of operating conditions. By taking out only the necessary contextual features rather than processing the entire signal for adaptation, the system achieves accurate detection while reducing computational complexity.
Solution Approach 2:
The system changes parameters by using a compact context representation that captures operating conditions in a reduced-dimensional space. This parameter transformation allows the detection function to operate with simplified inputs, maintaining detection accuracy while significantly reducing computational requirements compared to full signal processing for adaptation.
3Reliability
If receiver processing is adapted to current operating conditions, then detection performance improves, but ease of operation deteriorates
Solution Approach 1:
By segmenting the system into separate context determination and data detection functions, the patent makes the system easier to train and maintain. Each function can be trained independently on relevant data, and the modular architecture allows for easier debugging and updates compared to a monolithic adaptive processing system.
Data Source
AI summary
A computer implemented method for detecting data comprised in a part of a received signal of a communication system, wherein the received signal is associated with a population and where the part of the received signal is associated with a sub-population of the population, the method comprising configuring a first function to determine a context of the received signal, wherein the context is indicative of a state of the received signal, configuring a second function to detect the data based on the part of the received signal, wherein the second function is arranged to be parameterized by the context, and detecting the data by the first and second functions.