Demodulation Noise Weighting for Partial Wireless Interference
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Solution Overview
Problem
Wireless systems face interference issues, leading to reduced signal-to-interference-plus-noise ratio (SINR) and packet-error-rates, with existing methods simplifying interference estimation by assuming white Gaussian noise, resulting in imperfect demodulation.
Innovation Solution
A technique that weights noise power in demodulation/demapping using an estimate of interference and its associated power, represented by the formula σ2=σN2+qσI2, where σN2 is noise power, σI2 is interference power, and q is an interference correction factor, allowing for partial interference amelioration in wide-band and narrow-band systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimal algorithms are used to estimate interference accurately, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter representation of interference by introducing the correction factor q that transforms interference power σI2 into an equivalent noise power term qσI2. This allows the interference to be handled using noise processing algorithms, maintaining estimation accuracy while reducing computational complexity through parameter transformation.
Solution Approach 2:
The correction factor q acts as an intermediary that bridges interference and noise processing. By multiplying interference power by q, the patent creates an equivalent noise representation that can be processed through existing noise filtering algorithms, avoiding the need for complex interference-specific algorithms.
2Device complexity
If interference is assumed to be white Gaussian noise, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent converts the harmful assumption of treating colored interference as white Gaussian noise into a benefit by introducing the correction factor q. This factor compensates for the incorrect assumption by scaling the noise power to effectively represent the actual interference power, thereby maintaining accuracy despite the simplified white noise assumption.
Solution Approach 2:
By changing the noise power parameter from σN2 to σ2 = σN2 + qσI2, the patent effectively transforms the simplified white noise model into an accurate representation of colored interference, allowing complex interference to be handled with simple noise processing algorithms.
3Reliability
If σ2 is adjusted using the formula σ2=σN2+qσI2, then reliability is improved by mitigating interference effects, but device complexity increases due to additional estimation requirements
Solution Approach 1:
The patent segments the total noise power σ2 into two distinct components: actual noise power σN2 and corrected interference power qσI2. This segmentation allows the system to separately estimate and process noise and interference, improving demodulation reliability while managing complexity through modular estimation approaches.
Solution Approach 2:
The correction factor q is designed to be dynamically adjustable based on interference conditions. This dynamics allows the system to adapt the noise power estimation in real-time according to actual interference levels, maintaining high reliability across varying channel conditions without requiring complex fixed algorithms.
Data Source
AI summary
A technique weights noise power used in a demodulation/demapping process using on an estimate of interference and its associated power. Using this technique the effect of partial interference can be ameliorated. For example, a value, σ2, can be used to represent the estimated noise and interference power, and σ2 can be used to modify a received signal to ameliorate the effects of noise and interference. σ2 can be adjusted in response to partial interference, and can be represented by the formula: σ2=σN2+q σI2, where σN2 is “noise power,”σI2 is “interference power,” and q is an interference correction factor.