Cross-Correlation Modulation Quality Measurement for Dynamic Range
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Solution Overview
Problem
Modern wireless communication systems face challenges in isolating noise and distortion from a device-under-test (DUT) due to interference from the transmission medium and measurement system, limiting the dynamic range of modulation quality measurements.
Innovation Solution
A cross-correlation calculation is performed between first and second error vectors from two vector signal analyzers (VSAs) to isolate the noise and distortion of the DUT by averaging complex cross-correlation measurements, enhancing the dynamic range of modulation quality measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional modulation quality measurement methods are used, then the measurement process is simple, but the dynamic range is limited due to inability to isolate DUT noise and distortion from other sources
Solution Approach 1:
The measurement system is segmented into multiple independent measurement channels (at least two VSAs) that separately measure the same modulated signal. Each channel independently captures noise and distortion, allowing subsequent differentiation between DUT-originated impairments and measurement system-originated impairments through cross-correlation processing.
Solution Approach 2:
Cross-correlation calculation serves as an intermediary processing step between raw measurements and final modulation quality results. This mathematical operation identifies and isolates the correlated noise and distortion components that originate from the DUT, separating them from uncorrelated measurement system noise.
2Measurement precision
If multiple VSAs are used to improve measurement accuracy, then the dynamic range increases, but the device complexity and measurement processing complexity increase
Solution Approach 1:
The measurement function is segmented across multiple VSAs, where each VSA independently performs demodulation and error vector calculation on the same input signal. This parallel segmentation enables comprehensive capture of measurement system noise while maintaining functional independence of each measurement channel.
Solution Approach 2:
The measurement process is copied across multiple VSAs, creating redundant measurement channels that independently process the same modulated signal. These copied measurement channels provide multiple observations of the same physical phenomenon, enabling statistical separation of DUT impairments from measurement system impairments through cross-correlation.
3Measurement precision
If cross-correlation calculation is performed on all complex components, then the measurement is comprehensive, but the convergence speed is slow
Solution Approach 1:
The real components are extracted from the complex cross-correlation measurements, separating them from the imaginary components. This extraction focuses the averaging process on the relevant measurement information while discarding components that do not contribute to the modulation quality assessment, thereby accelerating convergence.
Solution Approach 2:
Instead of processing all complex components equally, the method applies partial action by focusing averaging only on the real components of the cross-correlation measurements. This selective processing achieves sufficient measurement precision without the computational overhead of processing both real and imaginary components, resulting in faster convergence.
Data Source
AI summary
Techniques are disclosed related to determining a modulation quality measurement of a device-under-test (DUT). A modulated signal is received from a source a plurality of times, and each received modulated signal is transmitted to each of a first vector signal analyzer (VSA) and a second VSA. The first VSA and the second VSA demodulate the received modulated signals to produce first error vectors and second error vectors, respectively. A cross-correlation calculation is performed on the first error vectors and second error vectors of respective received modulated signals to produce a complex-valued cross-correlation measurement, and a real component of the cross-correlation measurement is averaged over the plurality of received modulated signals. A modulation quality measurement is determined based on the averaged cross-correlation measurement.