Antenna Coding Optimization Based on Pixel Antennas for MIMO Wireless Power Transfer with DC Combining

This paper investigates antenna coding based on pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. Antenna coding is closely related to the Fluid Antenna System (FAS) concept and further generalizes the radiation pattern reconfigurability. We first introduce a beamspace channel model to demonstrate reconfigurable radiation patterns enabled by antenna coders. By jointly optimizing the antenna coding and transmit beamforming with perfect channel state information (CSI), we exploit gains from antenna coding, transmit beamforming, and rectenna nonlinearity to maximize the output DC power. We adopt an alternating optimization approach with the quasi-Newton method and Successive Exhaustive Boolean Optimization (SEBO) method with warm-start to handle the transmit beamforming design and antenna coding design respectively. Finally, simulation results show that the proposed MIMO WPT system with pixel antennas achieves up to 15 dB gain in average output DC power compared with a conventional system with fixed antenna configuration, highlighting the potential of pixel antennas for boosting the WPT efficiency.

💡 Research Summary

This paper proposes a novel approach to enhance Multiple-Input Multiple-Output (MIMO) Wireless Power Transfer (WPT) systems by introducing “antenna coding” based on reconfigurable pixel antennas. The core idea is to treat the switch states controlling the connections between the sub-wavelength metallic elements (“pixels”) of an antenna as binary optimization variables (coders). This allows the radiation pattern of each antenna to be dynamically reconfigured, providing a new degree of freedom beyond traditional beamforming with fixed antennas.

The authors begin by modeling a pixel antenna as a multi-port network characterized by an impedance matrix. The antenna coder vector determines the load impedances at the pixel ports, which in turn shapes the current distribution and the final radiation pattern. For a MIMO system with multiple such antennas at the transmitter and receiver, a beamspace channel model is formulated. This model elegantly integrates the reconfigurable patterns (represented by matrices E_T and E_R) with a virtual channel matrix H_V, resulting in an overall channel matrix H(B_T, B_R) that is a function of all antenna coders. A key insight is that a pixel antenna possesses a finite number of Effective Aerial Degrees of Freedom (EADoF), enabling it to synthesize various orthogonal radiation patterns. This allows the entire system to be equivalently viewed as a conventional MIMO system with a larger number of spatially separated antennas, described by a compact channel matrix H_C.

The system employs a DC combining scheme at the receiver, where the RF signal from each receive pixel antenna is rectified independently by a nonlinear rectifier (modeled accurately using a 4th-order Taylor expansion in the low-power regime) and then combined at the DC level. The objective is to jointly optimize the digital transmit beamformer (p) and the antenna coders at both ends (B_T, B_R) to maximize the total output DC power, subject to a transmit power constraint and the binary nature of the coders.

Recognizing the non-convex and NP-hard nature of this joint optimization problem, the authors devise an alternating optimization framework. The problem is decoupled into two sub-problems solved iteratively: 1) Transmit Beamforming Optimization: With fixed antenna coders, the beamformer is optimized using a quasi-Newton method, efficiently handling the rectifier nonlinearity. 2) Antenna Coding Optimization: With a fixed beamformer, the binary coders are optimized using a Successive Exhaustive Boolean Optimization (SEBO) algorithm. A “warm-start” initialization strategy is crucial here to guide the SEBO search towards a good local optimum, mitigating the challenge of numerous poor local solutions.

Simulation results in a rich-scattering Rayleigh fading environment demonstrate the significant potential of the proposed scheme. The MIMO WPT system empowered by optimized pixel antennas achieves up to a 15 dB gain in average output DC power compared to a conventional system using antennas with fixed configurations. This substantial improvement stems from the synergistic exploitation of three key gains: the adaptability of radiation patterns via antenna coding, the spatial focusing of energy via transmit beamforming, and the efficient harvesting of RF power via the nonlinear rectifier model.

In conclusion, the paper successfully establishes antenna coding with pixel antennas as a powerful new paradigm for boosting WPT efficiency. It provides a comprehensive treatment spanning theoretical modeling (beamspace channel), practical problem formulation (incorporating rectifier nonlinearity and DC combining), algorithmic solution (alternating optimization with tailored methods), and performance validation, paving the way for more efficient and adaptable wireless energy delivery systems for future IoT and sensor networks.