PRACH Preamble Detection Feedback for Congested Random Access

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Solution Overview

Problem

The existing random access (RA) process in LTE and 5G cellular networks suffers from frequent collisions, poor preamble detection, and inefficient resource utilization due to dynamic changes in cell profiles and increasing UE population, leading to PRACH congestion and bandwidth wastage.

Innovation Solution

Implementing a machine learning (ML) engine at the base station for optimized preamble detection, triggered by UE reports of poor performance, which collects controlled and uncontrolled data to improve detection accuracy and reduce collisions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If traditional threshold-based preamble detection is used, then the detection process is simple, but detection accuracy deteriorates under dynamic cell profile changes and increasing UE population

Engineering Contradiction:

Improvedetection process complexityVSAvoidpreamble detection accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent changes the detection parameters from fixed thresholds to dynamic machine learning models that adapt to cell profile changes and UE population variations, enabling accurate preamble detection under diverse conditions without increasing operational complexity

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements feedback mechanisms where UE performance reports trigger ML model retraining at the base station, allowing the detection system to continuously improve its accuracy based on actual performance data while maintaining simple operation at the UE level

Inventive Principle:

Principle #23Feedback

2Productivity

If machine learning engine is implemented at base station, then preamble detection efficiency is improved, but device complexity increases

Engineering Contradiction:

Improvepreamble detection efficiencyVSAvoidbase station complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The base station performs self-training using UE performance reports and controlled random access data, automatically improving its ML model without external intervention. This self-service approach enables the base station to handle increasing UE population and dynamic conditions while managing complexity through autonomous adaptation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary ML training using controlled CFRA data collection before handling production traffic. This preliminary action allows the base station to prepare optimized detection models in advance, improving detection efficiency for subsequent random access procedures without impacting operational complexity

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If contention-free random access is used for training data collection, then detection accuracy is improved, but bandwidth utilization deteriorates due to repeated transmissions

Engineering Contradiction:

Improvedetection accuracyVSAvoidbandwidth utilization

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent implements periodic training data collection using CFRA in intervals between normal RA operations. This periodic action allows the base station to collect controlled data for ML training without continuously occupying bandwidth, balancing detection accuracy improvement with bandwidth conservation during production traffic

Inventive Principle:

Principle #19Periodic action

4Ease of operation

If traditional random access procedure is used, then ease of operation is maintained, but collisions increase due to dynamic cell profile changes

Engineering Contradiction:

ImproveUE operation simplicityVSAvoidrandom access success rate

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent implements feedback through UE performance reporting to the base station, which triggers ML model retraining. This feedback loop improves random access reliability by adapting to changing cell conditions while maintaining simple UE operation, as UEs merely report performance without controlling the complex detection process

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces the mechanical threshold-based detection system with a machine learning-based system that learns from UE performance data. This substitution maintains ease of operation at the UE level while significantly improving reliability through intelligent adaptation to dynamic cell profiles and traffic conditions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12593351B2User equipment and method for PRACH performance reporting to base station

Publication Date: 2026.03.31 ULAK HABERLESME AS

AI summary

A user equipment (UE) sends a simple message on a control channel to the base station (BS) that is using Machine Learning (ML) engine for random access (RA) procedures, the message being an indicator of a poor preamble detection performance of the BS upon UE observing repeated failure of random access attempts on the physical random access channel (PRACH). In response, the BS collects controlled preamble detection training data by selecting a plurality of trainer UEs and sending each UE a training random access configuration to use during a training cycle.