Structural Causal Modeling for 5G Signal Recovery
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Solution Overview
Problem
In wireless communication, particularly in 5G networks, accurately recovering the original signal at the user equipment side is hindered by channel distortion and noise, leading to high transmission error rates due to the limitations of statistical channel estimation methods.
Innovation Solution
A communication method and device that utilize a structural causal model to correlate transmitted and received signals, determining causal variables and structures through abductive reasoning to infer the original signal, thereby reducing transmission errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical channel estimation methods are used to recover the original signal, then the signal can be decoded at the user equipment side, but the transmission error rate is high due to channel distortion and noise
Solution Approach 1:
The patent introduces an intermediary processing step between signal transmission and reception that uses causal reasoning to model the channel behavior. Instead of directly estimating channel parameters through statistical methods, the system uses an intermediary causal model that represents the cause-and-effect relationships in the communication channel, allowing for more accurate signal recovery by understanding the underlying causal mechanisms rather than just statistical correlations
Solution Approach 2:
The patent replaces the traditional statistical channel estimation mechanism with a causal reasoning mechanism. Instead of relying on statistical associations and mathematical estimation algorithms, the system substitutes a causal model that explicitly represents the cause-and-effect relationships between transmitted signals, channel effects, and received signals, leading to improved signal recovery accuracy and reduced transmission errors
2Ease of operation
If traditional channel estimation is performed at the user side to recover the original signal, then signal decoding is possible, but design complexity, power consumption and CPU utilization are significantly increased
Solution Approach 1:
The patent extracts the causal reasoning capability from complex statistical processing and implements it as a separate, simplified module. By taking out the essential causal relationships from the complicated channel estimation process, the system achieves signal recovery capability with reduced design complexity and lower computational requirements at the user equipment side
Solution Approach 2:
The causal model is designed to automatically infer channel behavior and signal characteristics without requiring extensive computational resources for statistical estimation. The model serves itself by using the inherent causal structure of the communication channel to perform signal recovery, reducing the need for complex processing algorithms and high-performance hardware at the user side
Data Source
AI summary
A communication method, for a receiver, including receiving a received signal, and obtaining information of an original signal according to the received signal. A transmitter obtains a transmitted signal according to the original signal. The transmitter sends the transmitted signal. The transmitted signal is changed to the received signal after passing through a channel. The transmitted signal and the received signal are correlated using a structural causal model. A number of a plurality of causal variables of a causal graph of the structural causal model and a causal structure of a causal graph of the structural causal model are determined together.