Cell Quality Evaluation Using Fictive Beam Standardization
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Solution Overview
Problem
Current methods for evaluating cell quality in 5G networks, particularly in conditions with high carrier frequencies, face challenges such as unfair comparisons due to differing numbers of detected beams and fast fading effects, leading to suboptimal handover decisions and cell re-selection.
Innovation Solution
The introduction of fictive beams with assigned quality values to complement the number of good beams, ensuring a fair comparison by standardizing the number of beams considered for each cell, thereby stabilizing cell quality assessment and reducing the impact of fast fading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If beam sweeping is used to cover the entire cell area with multiple beamformed transmissions, then coverage is improved, but the number of detected beams varies between cells leading to unfair quality comparison
Solution Approach 1:
The network node pre-configures a maximum number of beams N for cell quality evaluation before measurements are performed. This preliminary setting ensures that all cells are evaluated using the same number of beams, preventing unfair comparisons due to varying beam counts across different cells.
Solution Approach 2:
The invention changes the evaluation parameter from using the actual number of detected beams (which varies) to using a standardized maximum number N of beams for all cells. This parameter standardization allows fair comparison of cell qualities across different cells regardless of their actual beam coverage variations.
2Device complexity
If only the best beam is considered for cell quality evaluation, then the evaluation is simple, but fast fading effects cause premature triggering of measurement reports
Solution Approach 1:
The invention merges multiple beam qualities (up to N best beams) into a single cell quality evaluation metric. By combining information from multiple beams rather than relying on just the best beam, the evaluation becomes more reliable and less susceptible to fast fading effects that can cause temporary anomalies in single-beam measurements.
Solution Approach 2:
The system uses feedback from multiple beam measurements to establish a more stable cell quality baseline. By considering N best beams and their respective qualities, the system creates a more robust feedback mechanism that reduces premature triggering of measurement reports caused by temporary fading conditions on a single beam.
3Reliability
If multiple beams are considered for cell quality evaluation, then reliability is improved, but the complexity of the evaluation process increases
Solution Approach 1:
The network node pre-configures the maximum number of beams N and quality thresholds before evaluation. This preliminary configuration simplifies the actual evaluation process by establishing clear criteria in advance, allowing the UE to systematically evaluate up to N beams without complex real-time decision-making, thus balancing reliability with manageable complexity.
Data Source
AI summary
A method for evaluating cell quality includes obtaining cell quality information and determining, based on the cell quality information, a first number, X, of beams whose qualities are above a threshold, T, for a first cell and a second number, Y, of beams whose qualities are above the threshold, T, for a second cell. The method includes complementing a third number, M, of fictive beams to offset a difference between the first number, X, of beams and the second number, Y, of beams, and measuring a first average beam quality, Q1, for the first cell and a second average beam quality, Q2, for the second cell. The method may include a certain number fictive beams with assigned beam quality when calculating cell qualities for cells which are included in comparison.