Signal Parameter Estimation Model for 5G Beam Measurement
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Solution Overview
Problem
In 5G cellular communications networks, the lack of always-on reference signals and beams poses inefficiencies in activating and measuring neighboring cell signals, particularly when thousands of beams need to be activated for UE measurement, which is resource-intensive and inefficient.
Innovation Solution
A machine learning (ML) model is configured based on configuration information from a second cell to estimate signal parameters of neighboring cells or beams, allowing the UE to report these estimates without activating the signals, and send indications to the second cell if criteria are met, such as exceeding signal strength thresholds, to trigger handover decisions or signal activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all neighboring beams are activated for UE measurement, then measurement accuracy is improved, but resource consumption and system complexity increase significantly
Solution Approach 1:
The network node performs preliminary actions by configuring the UE with a signal parameter estimation model and necessary configuration information before actual measurement is needed. This allows the UE to estimate signal parameters of neighboring beams without requiring those beams to be activated, thereby avoiding the resource consumption that would result from activating all neighboring beams for measurement.
2Reliability
If thousands of beams are activated for UE measurement, then coverage and signal quality assessment are improved, but device complexity and processing overhead increase
Solution Approach 1:
Instead of directly measuring all neighboring beams, the UE uses a signal parameter estimation model to generate copies or predictions of what the signal parameters would be. The model takes configuration information as input and produces estimated signal parameters that mirror what would be obtained from actual measurements, thereby avoiding the complexity of managing thousands of active beams while maintaining reliable coverage assessment.
3Measurement precision
If neighboring cells transmit reference signals continuously, then measurement capability is improved, but energy consumption and network overhead increase
Solution Approach 1:
The neighboring cells perform preliminary action by providing configuration information about their reference signals in advance, rather than continuously transmitting them. The UE uses this configuration information together with the estimation model to determine signal parameters without requiring the neighboring cells to maintain continuous transmission, thereby reducing their energy consumption while preserving measurement capability.
Data Source
AI summary
In one example aspect, a method is provided of reporting an indication of one or more estimated signal parameters of a signal from a first cell, beam or frequency in a cellular communications network. The method comprises configuring a signal parameter estimation model based on configuration information from a second cell in the cellular communications network, and determining an indication of one or more estimated signal parameters of a signal from the first cell, beam or frequency using the parameter estimation model. The method also comprises sending the indication to the second cell in response to the indication or the one or more estimated signal parameters satisfying one or more criteria.