Neighbor Cell Interference Estimation in WCDMA Networks
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Solution Overview
Problem
Current inter-cell interference power estimation algorithms in CDMA communication systems, particularly in WCDMA networks, face challenges in accurately handling noise floor and interference power variations in heterogeneous networks, leading to inaccurate neighbor cell interference estimation and suboptimal scheduling decisions due to high computational complexity and inaccuracy.
Innovation Solution
A method using extended Kalman filter techniques for neighbor cell interference estimation, which involves measuring uplink received total wideband power and load utilization, jointly estimating neighbor cell interference power and noise power floor, and applying power scaling based on a running estimate of thermal noise power floor to adapt to long-term power changes, thereby improving estimation accuracy and handling variations in noise floor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high order Kalman filtering is used to estimate neighbor cell interference and thermal noise power floor, then estimation capability is improved, but computational complexity becomes very high
Solution Approach 1:
The patent segments the estimation process into two separate stages: first estimating the thermal noise power floor using long-term measurements, then estimating neighbor cell interference by subtracting the noise floor from total interference plus noise measurements. This segmentation reduces computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent performs preliminary estimation of the thermal noise power floor before estimating neighbor cell interference. By first determining the noise floor level and subtracting it from total measurements, the system simplifies the subsequent interference estimation process and reduces overall computational burden.
2Device complexity
If Kalman filter operates in linear power domain at specific operating point, then filter design is simplified, but accuracy deteriorates when power operating point varies significantly
Solution Approach 1:
The patent implements a dynamic scaling mechanism where the Kalman filter's measurement noise variance is continuously scaled based on the ratio of current RTWP to a reference RTWP value. This dynamic adaptation allows the filter to maintain optimal performance across varying power operating points without requiring complex redesign.
Solution Approach 2:
The patent changes the measurement noise variance parameter of the Kalman filter dynamically based on operating conditions. By scaling this parameter with the power ratio, the system adapts to varying interference and noise levels, maintaining estimation accuracy across different operating points.
3Reliability
If advanced receivers are introduced to suppress interference, then receiver performance is improved, but neighbor cell interference estimation becomes more difficult
Solution Approach 1:
The patent introduces RTWP measurements as an intermediary indicator that reflects the actual interference situation at the receiver. Instead of directly measuring interference components that are distorted by advanced receiver processing, the system uses RTWP as a proxy measurement that captures the overall interference plus noise level, which can then be processed through the scaled Kalman filter.
Data Source
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AI summary
A method for neighbor cell interference estimation in the UpLink of a CDMA communication system comprises obtaining measurements of an uplink received total wideband power (RTWP) and obtaining measurements of a load utilization of the uplink. At least a sum of the neighbor cell interference power and the noise power floor as well as a load utilization probability are jointly estimated from the measurements of an uplink RTWP and the measurements of a load utilization of the uplink. A thermal noise power floor is monitored by performing a running estimate of a long-time average uplink wideband power, preferably an estimated thermal noise power floor level or an operational level of the RTWP. A subset of the states of the estimation is selected, comprising only all states of the estimation that are associated with powers. The states of the selected subset and quantities associated therewith are scaled with a scaling factor. The scaling factor depends on the running estimate.