Polarization Diversity and Transfer Learning Based Modulation Optimization for High-Speed Dual Channel MIMO Backscatter Communication

This paper presents a novel approach to address the challenges in backscatter communication for the Internet of Things. The traditional use of I/Q load modulator based on parametric transistor models often suffers from performance degradation due to discrepancies with actual transistor models caused by thermal and environmental noise sources. To overcome this issue, the paper proposes an active circuit modeling technique based on artificial neural network (ANN)-based transfer learning, which utilizes actual measurement data to model the I/Q load modulator accurately. Furthermore, an optimization algorithm is applied to achieve an optimal high-order modulation scheme, leading to improved energy efficiency by 40%. By leveraging machine learning-based modeled I/Q modulators, the proposed approach enables high-speed wireless data communication in a dual-channel configuration. The paper also conducts theoretical analysis to define the required performance of a dual-polarized Vivaldi antenna for implementing polarization diversity in backscatter communication. This analysis provides guidelines for achieving optimal performance in terms of spectral efficiency and error vector magnitude (EVM). The experimental results demonstrate that the proposed approach achieves a spectral efficiency of 2.0 bps/Hz based on 4-QAM modulation within a 150 MHz bandwidth. The measured EVM is 9.35%, indicating the effectiveness of the proposed technique in achieving reliable and efficient wireless data communication in backscatter systems. This paper presents a comprehensive approach combining accurate circuit modeling, optimization algorithms, and theoretical analysis to enable high-speed, ultra-low-power wireless data communication in backscatter communication systems.