MIMO Channel Estimation for Variable 5G Transmit Layers

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Solution Overview

Problem

Existing neural network-based channel estimation techniques are inflexible and cannot accommodate varying numbers of transmit layers in 5G NR broadband cellular communications, requiring new architectures and training for each different channel estimation problem size.

Innovation Solution

Implement pre-processing and post-processing units to transform channel estimation arrays before and after neural network processing, allowing a single neural network architecture to handle arbitrary numbers of transmit layers by splitting and combining arrays with dimensions independent of the number of transmit layers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing neural network-based channel estimation techniques are used, then channel estimation can be performed, but the system cannot accommodate varying numbers of transmit layers and requires new architectures and training for each different problem size

Engineering Contradiction:

Improveadaptability to varying numbers of transmit layersVSAvoidcomplexity of neural network architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the channel estimation problem by separating the transmit layer dimension from the neural network processing. The input array is transformed by removing the transmit layer dimension, allowing the neural network to process a fixed-dimensional representation that can accommodate any number of transmit layers. This segmentation enables the same neural network architecture to handle variable numbers of transmit layers without requiring architectural changes or retraining.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If a single neural network architecture is used for arbitrary numbers of transmit layers, then flexibility is improved, but array transformation complexity increases

Engineering Contradiction:

Improveflexibility with single neural network architectureVSAvoidcomplexity of pre-processing and post-processing units

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by performing array transformations before the neural network processing. The pre-processing unit transforms the input array by removing the transmit layer dimension and reshaping the data into a fixed-dimensional format that the neural network can process. This preliminary transformation enables the single neural network architecture to handle variable transmit layers without requiring the network itself to be complex or adaptable.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces intermediary transformation units (pre-processing and post-processing) that mediate between the variable-dimensional input/output and the fixed-dimensional neural network processing. These intermediary units handle the dimensionality transformation, allowing the neural network to remain simple and fixed-architecture while still accommodating varying numbers of transmit layers through the intermediary transformations.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If traditional channel estimation methods are used, then implementation is straightforward, but efficiency and performance for varying transmit layers deteriorates

Engineering Contradiction:

Improveefficiency of channel estimationVSAvoidease of implementation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent creates a universal channel estimation system where a single neural network architecture can process channel estimation for any number of transmit layers. The pre-processing and post-processing units transform the input and output arrays to match the fixed-dimensional requirements of the neural network, enabling one system to perform multiple functions across different transmit layer configurations. This universal approach improves efficiency compared to traditional methods that would require separate models for each transmit layer scenario.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260032020A1Channel Estimation with Varying Numbers of Transmit Layers

Publication Date: 2026.01.29 DELL PROD LP

AI summary

A system can produce a rough channel estimate that comprises a first array of a first number of dimensions, wherein a communications channel enables broadband cellular communications, and wherein the communications channel is configured for multiple-input multiple-output transmission. The system can transform the first array into second arrays, wherein respective second arrays of the second arrays have a second number of dimensions that is one less than the first number of dimensions. The system can process the respective second arrays using a neural network that is configured to process arrays having the second number of dimensions, to produce third arrays. The system can combine the third arrays into a fourth array having the first number of dimensions, wherein the fourth array indicates a second channel estimate. The system can use the fourth array for the broadband cellular communications with the at least one user equipment.