Noise Floor Estimation Using Time Bin Segmentation
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Solution Overview
Problem
Accurate estimation of the noise floor power level in a radio receiver is challenging due to interference from neighboring cells and variations in thermal noise, leading to inaccuracies in air interface load estimation, which affects uplink throughput in WCDMA systems, especially with the increasing number of 'always connected' devices.
Innovation Solution
A method that sorts estimated noise floor power level values into time bins, determines their average values, and applies a scale factor to compensate for variations by dividing each time bin's average by the smallest average value, thereby improving noise floor estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the period of time over which the internal noise floor is estimated is extended to cope with time-varying interference from neighboring cells, then the accuracy of noise floor estimation is improved, but the bandwidth of the noise floor estimator is reduced and the amount of data needed for estimations is increased
Solution Approach 1:
The patent divides the time domain into discrete time bins and groups them into time periods. By segmenting the estimation process into multiple time periods with different weighting factors, the system can incorporate historical data without treating all data points equally, thus maintaining estimator bandwidth while improving accuracy through extended temporal observation.
Solution Approach 2:
The patent introduces time-period-dependent weighting factors that dynamically adjust the influence of historical measurements. Recent time periods receive higher weights while older periods receive lower weights, creating a dynamic estimation process that adapts to changing interference conditions without requiring excessively long integration periods.
2Measurement precision
If the period of time over which the internal noise floor is estimated is extended to cope with time-varying interference from neighboring cells, then the accuracy of noise floor estimation is improved, but the amount of data needed for estimations is increased
Solution Approach 1:
By segmenting data into time bins and time periods with assigned weights, the patent reduces the effective amount of data that needs to be processed. Instead of treating all historical data points equally, the system focuses computation on weighted segments, reducing data processing requirements while maintaining estimation accuracy.
Solution Approach 2:
The patent applies partial action by using weighted contributions from historical data rather than incorporating all historical data with equal weight. This selective approach reduces the computational burden while still capturing the essential temporal variations in noise floor characteristics.
3Measurement precision
If known methods with bias estimation are used to estimate the internal noise floor, then some bias reduction is achieved, but the methods are not accurate enough and do not provide bias estimations with a sufficiently high degree of resolution
Solution Approach 1:
The patent segments the bias estimation process into multiple time periods, each contributing to the overall estimate with appropriate weighting. This segmentation allows for higher resolution bias estimation by capturing temporal variations in bias characteristics that single-period methods would miss.
Solution Approach 2:
The patent changes the parameter of time period weighting to improve bias estimation accuracy. By varying the weight assigned to different time periods based on their relevance to current conditions, the system achieves both accurate bias reduction and high-resolution bias estimation without requiring auxiliary measurements.
Data Source
AI summary
A method for determining the noise floor in a receiver includes sorting received estimated values of the noise floor by time bins in a time cycle, determining and storing the average value of the received values in each time bin for a previous time cycle, and determining a scale factor for each time bin in the current time cycle by dividing the average value of each time bin in the previous time cycle by the smallest average value of the time bins in the previous time cycle. The division for time bin k in the previous time cycle is used as scale factor for time bin k in the current time cycle. A compensated noise floor power level for each time bin in the current time cycle may be determined by applying the scale factor of the current time bin to the currently received estimated value.