研究目的
To mitigate third-order intermodulation distortion produced by a nonlinear tunable bandpass filter in a digital receiver channel using an adaptive nonlinear equalization approach.
研究成果
The LMS algorithm effectively mitigates third-order IMD in a nonlinear tunable bandpass filter by adapting correction coefficients to frequency changes. Coefficients trained at one frequency are not suitable for another, but LMS allows quick adaptation. This method is promising for digital receiver channels, with future work needed on real-time tuning and temperature effects in phased arrays.
研究不足
The study is limited to a specific two-pole varactor-loaded filter and the AD9371 transceiver; results may not generalize to other filter types or systems. The approach requires an auxiliary receiver for reference, which may not be feasible in all applications. Computational complexity increases with memory terms, and real-time adaptation for temperature and phase changes in phased arrays is not addressed.
1：Experimental Design and Method Selection:
The study uses the least-mean-square (LMS) algorithm for adaptive nonlinear equalization (NLEQ) to correct for intermodulation distortion (IMD) in a tunable bandpass filter. A memory polynomial basis is employed to account for frequency dependence.
2：Sample Selection and Data Sources:
Data is gathered from an Analog Devices AD9371 transceiver using sinusoidal inputs at center frequencies of 2.1, 2.4, and 2.7 GHz, with tones offset by 8 and 13 MHz.
3：1, 4, and 7 GHz, with tones offset by 8 and 13 MHz.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Equipment includes an Analog Devices AD9371 transceiver, MiniCircuits ZRL-3500+ and ZVE-8G+ amplifiers, Agilent Technologies E8267 PSG Vector Signal Generator, power divider, attenuators (58 dB and 50 dB), a two-pole varactor-loaded bandpass filter with Skyworks SMV1405 varactor diodes on a Rogers RO4350B substrate, and auxiliary receivers.
4：Experimental Procedures and Operational Workflow:
Signals are generated and combined, passed through amplifiers and the filter, attenuated, and sampled by receivers at 122.88 Msps. The LMS algorithm is applied in MATLAB for correction, iterating through data sets to adapt coefficients.
5：88 Msps. The LMS algorithm is applied in MATLAB for correction, iterating through data sets to adapt coefficients.
Data Analysis Methods:
5. Data Analysis Methods: Mean-square error (MSE) is minimized using the LMS algorithm. IMD magnitudes are analyzed before and after correction, with results visualized in plots.
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