Radio Receiver Multi-Stage Neural Equalization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing radio receivers face challenges in achieving optimal performance across various radio channel conditions due to labor-intensive manual adaptation of receiver algorithms to different reference signal configurations.

Innovation Solution

A radio receiver architecture that employs a sequential plurality of equalization stages, including an equalizer and a neural network, to determine a refined channel estimate for subsequent stages, thereby improving signal equalization and channel estimation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual adaptation of receiver algorithms is used for different reference signal configurations, then the receiver can be customized for specific conditions, but the development process becomes labour intensive and time-consuming

Engineering Contradiction:

Improveadaptability to different reference signal configurationsVSAvoidcomplexity of algorithm development and adaptation

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The receiver algorithm automatically adapts to different reference signal configurations through neural network-based channel estimation and equalization. The system self-adjusts by processing reference signals and data through the neural network, which learns optimal parameters without manual intervention, thereby maintaining versatility while eliminating labour-intensive development

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The neural network dynamically changes its internal parameters (weights and biases) based on the input reference signal configurations. This automatic parameter adaptation allows the receiver to handle different signal configurations effectively without requiring manual reconfiguration of the algorithm structure

Inventive Principle:

Principle #35Parameter changes

2Reliability

If manual adaptation of receiver algorithms is used, then customization for specific conditions is possible, but the overall processing time and development effort increase

Engineering Contradiction:

Improveperformance reliability under specific conditionsVSAvoidtime for algorithm development and adaptation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The neural network is pre-trained during an offline phase to learn optimal channel estimation and equalization strategies for various reference signal configurations. This preliminary training stores learned knowledge in the network weights, enabling the receiver to quickly adapt to different conditions during runtime without time-consuming manual adjustments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically processes different reference signal configurations through the pre-trained neural network, which self-adjusts its output based on the input configuration. This eliminates the need for manual algorithm adaptation time while maintaining reliable performance across different conditions

Inventive Principle:

Principle #25Self-service

3Productivity

If traditional equalization methods are used, then the system is simpler to implement, but the performance is not optimal across all radio channel conditions

Engineering Contradiction:

Improvesignal processing efficiencyVSAvoidcomplexity of multi-stage equalization system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The equalization process is divided into multiple sequential stages, where each stage processes the signal through a neural network layer. This segmentation allows the complex task of optimal equalization across diverse channel conditions to be broken down into manageable stages, with each stage contributing to the overall performance improvement

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The neural network-based equalization system serves multiple functions: it performs channel estimation, equalization, and adaptation to different reference signal configurations within a single unified framework. This multi-functionality achieves optimal performance across all radio channel conditions without requiring separate specialized algorithms for each scenario

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250184186A1Radio receiver with multi-stage equalization

Publication Date: 2025.06.05 NOKIA SOLUTIONS & NETWORKS OY

AI summary

Various example embodiments may relate to relate radio receivers. A radio receiver may receive data and reference signals; determine a channel estimate for the received data based on the reference signals; and equalize the received data with a sequential plurality of equalization stages, one or more of the sequential plurality of equalization stages comprising: an equalizer configured to determine an equalized representation of input data based a previous channel estimate, and a channel estimator neural network configured to determine at least a refined channel estimate for a subsequent equalization stage based on the previous channel estimate and the input data.