MIMO Preprocessor Stabilizing Noise in Wireless Downlink
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Solution Overview
Problem
In wireless communication systems, MIMO detectors using deep neural networks face challenges in maintaining noise state stability during channel changes, leading to increased complexity and generation of colored noise.
Innovation Solution
A method and apparatus for preprocessing downlink signals in a wireless communication system, where a terminal receives and processes downlink signals using a deep neural network, applying reference signals while maintaining statistical noise features, and projecting signals to a transmission antenna domain using a matched filter, with channel coefficients as input factors to stabilize noise state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a preprocessor transforms the received downlink signal into a signal projected into a transmit antenna-dimensional domain, then the MIMO detection capability is improved, but the noise state is changed and colored noise is generated
Solution Approach 1:
The patent introduces an intermediary component (the preprocessor with specific projection operation) that transforms the received signal while managing the noise characteristics. The preprocessor acts as a mediator between the raw downlink signal and the MIMO detector, projecting the signal into transmit antenna-dimensional domain while attempting to preserve noise properties through careful design of the projection operation.
Solution Approach 2:
The patent changes the parameter of noise statistical characteristics by designing the projection operation to maintain the original noise properties. The preprocessor transforms signal parameters (projecting into different dimensional domain) while preserving noise parameters (statistical characteristics), thereby improving detection capability without generating colored noise.
2Productivity
If observation adaptation is performed in an MIMO detector using a fixed neural network, then the processing efficiency is improved, but the noise state changes depending on channel changes
Solution Approach 1:
The patent applies dynamics by making the preprocessor adaptive to channel changes. Instead of using a completely fixed neural network, the system dynamically adjusts the projection operation based on observed channel conditions, allowing the preprocessor to maintain noise state stability while adapting to varying channels for efficient processing.
Solution Approach 2:
The observation adaptation mechanism incorporates feedback from channel observations to adjust the preprocessing operation. The system observes channel changes and uses this information to modify the projection operation, creating a feedback loop that maintains noise state stability while preserving processing efficiency across different channel conditions.
Data Source
AI summary
Disclosed is a method for controlling, by a terminal, an operation of a deep neural network in a wireless communication system. The method according to an embodiment of the present disclosure receives a downlink from abase station in a wireless communication system; and preprocesses the downlink on the basis of the result of an operation of a deep neural network of a terminal, wherein at least one reference signal is applied to the downlink while a statistical feature related to noise of the downlink are maintained. The terminal of the present disclosure may be linked to an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 6G services, and the like.