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Forward Behavioral Modeling of a Three-Way Amplitude Modulator-Based Transmitter Using an Augmented Memory Polynomial
Jatin Chatrath1, Mohsin Aziz2, Mohamed Helaoui3
1iRadio Lab, Department of Electrical and Computer Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada. jatin.chatrath2@ucalgary.ca.
Sensors (Basel, Switzerland)
|March 8, 2018
Summary
This study introduces an augmented memory polynomial model for mixer-less transmitters, enhancing signal quality in wireless communications. The model accurately predicts performance for Long-Term Evolution (LTE) signals, crucial for the Internet of Things.
Area of Science:
- Electrical Engineering
- Wireless Communication Systems
- Signal Processing
Background:
- Reconfigurable radio frequency (RF) front-ends are vital for the Internet of Things (IoT).
- Mixer-less transmitter architectures offer improved signal quality by avoiding distortions from imperfect mixers.
- Efficient transmitter designs are key for advanced wireless communication and sensor networks.
Purpose of the Study:
- To propose an augmented memory polynomial model for behavioral modeling of mixer-less, three-way amplitude modulator-based transmitter architectures.
- To validate the accuracy of the proposed modeling strategy through extensive simulations and measurements.
- To evaluate the model's performance using normalized mean square error (NMSE) for Long-Term Evolution (LTE) signals.
Main Methods:
- Development of an augmented memory polynomial model tailored for mixer-less transmitter architectures.
- Extensive simulations and experimental measurements to verify the model's predictive capabilities.
- Performance evaluation using NMSE, Amplitude-to-Amplitude (AM-AM), Amplitude-to-Phase (AM-PM) distortion, and spectral response analysis.
Main Results:
- The proposed model achieved low NMSE values for LTE signals: -36.41 dB (1.4 MHz, digital combining) and -31.93 dB (5 MHz, digital combining).
- Analog combining yielded comparable results: -36.9 dB (1.4 MHz) and -32.08 dB (5 MHz).
- Modeled and measured data for AM-AM, AM-PM, and spectral response showed reasonable agreement, validating the model's accuracy.
Conclusions:
- The augmented memory polynomial model effectively captures the behavior of mixer-less transmitter architectures.
- This modeling approach contributes to the development of higher-quality, efficient transmitters for wireless communication and IoT applications.
- The model's accuracy is confirmed by its ability to predict signal impairments and spectral characteristics.
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