MLP Network for CSI Feedback RI and CQI Estimation
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Solution Overview
Problem
Existing methods for channel state information (CSI) feedback are inefficient and inaccurate, particularly in non-additive white Gaussian noise channels, as they rely on static tables that require frequent updates and cannot effectively handle multiple channel types, leading to suboptimal performance and increased block error rates.
Innovation Solution
A system and method utilizing a multi-layer perceptron (MLP) network trained with reinforcement learning to estimate and map channel features to rank indicator (RI) and channel quality indicator (CQI, with an online adaptation algorithm to refine decisions based on current channel conditions, eliminating the need for static tables and enabling simultaneous handling of multiple channel features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static table is used to map channel features to CQI and RI, then the implementation is simple, but the accuracy and adaptability to different channel conditions deteriorates
Solution Approach 1:
The patent replaces the traditional static table-based mapping mechanism with a neural network model. The neural network learns complex non-linear relationships between channel features and CQI/RI indicators through training, substituting the rigid mechanical table lookup with an adaptive intelligent system that can generalize to unseen channel conditions while maintaining implementation feasibility through standardized deep learning frameworks.
Solution Approach 2:
The patent transforms the fixed threshold parameters in static tables into dynamic learned parameters within the neural network. The network weights and biases are adjusted during training to optimize the mapping from channel features to CQI/RI, allowing the system to adapt to different channel conditions by changing these parameters based on learned patterns rather than relying on pre-defined static thresholds.
2Measurement precision
If the table is updated frequently to improve CQI and RI decisions, then the accuracy improves, but the complexity and processing time increases
Solution Approach 1:
The patent performs the complex adaptation process in advance by training the neural network offline using historical channel data and feedback. The pre-trained model captures the optimal mapping relationships, eliminating the need for frequent online table updates. The network is deployed in its finalized state, providing accurate CQI/RI decisions without requiring continuous reconfiguration or updates during operation.
Solution Approach 2:
The patent creates a learned representation (copy) of the complex channel-to-CQI/RI mapping relationships within the neural network structure. Instead of maintaining and updating multiple static tables, the system uses a single trained network that encapsulates the adaptive behavior, simplifying the deployment and update mechanism while maintaining high accuracy across diverse channel conditions.
3Adaptability or versatility
If multiple tables are used for different channel types, then the adaptability to various channels improves, but the device complexity and resource requirements increase
Solution Approach 1:
The patent designs a universal neural network model that can handle multiple channel types through a single unified structure. The network takes channel features as input and automatically learns the appropriate mapping to CQI/RI for different channel conditions during training. This single multi-functional model replaces the need for separate dedicated tables for each channel type, reducing complexity while maintaining versatility across diverse wireless environments.
Solution Approach 2:
The patent merges multiple channel-specific mapping tables into a single neural network model. By combining the knowledge from various channel types during the training phase, the network learns to generalize across different channel characteristics. This consolidation reduces the number of separate data structures needed while preserving the ability to adapt to multiple channel types through the unified learned representation.
Data Source
AI summary
An apparatus and method are provided for using an MLP algorithm to map channel features to an RI and/or a CQI for CSI feedback. The method includes estimating a channel in the communication network for a signal; extracting at least one channel feature related to the estimated channel; determining RI and CQI pairs; inputting, to an MLP network, the extracted at least one channel feature and the RI and CQI pairs; receiving, for each of the RI and CQI pairs, an output of the MLP network, wherein the outputs of the MLP network indicate throughput or spectral efficiency for the electronic device; and selecting an RI and CQI pair of the RI and CQI pairs based on the received outputs.