Liesbet Van der Perre

Sam Boeckx, Pieterjan Polfliet, Lieven De Strycker et al.

Inductive wireless charging of implantable medical devices necessitates careful control of magnetic and electric field emissions to meet strict safety regulations while delivering sufficient power. When designing a comfortable wireless charger that can operate over distances ranging to 10cm or more, it is difficult not to exceed the most stringent E-field limit of 83~V/m. This paper investigates electric field attenuation techniques for mid-range wireless power transfer at 6.78~MHz. Using \newacronym{fea}{FEA}{finite element analysis}\acrfull{fea} like Ansys \textregistered{} HFSS \texttrademark{}, three mitigation strategies are evaluated; (1) a high-permittivity dielectric shielding layer to absorb and redistribute electric fields, (2) multiple resonant tuning capacitors distributed along the transmitter coil to lower the voltage swing and confine high E-field regions, and (3) alternative coil-array transmitter topologies to spatially localize more confined E-fields. The results show that each technique significantly reduces the E-field magnitude without substantially affecting the H-field. Shielding the transmit coil attenuates the peak E-field from its initial 1416~V/m to 496~V/m, approximately a 65\% reduction. Distributing the tuning capacitance into sixteen smaller capacitors yields a drop from the 1416~V/m to 231~V/m, approximately a 84\% reduction. Both techniques preserve the required 8~A/m magnetic field. The third technique, a two-by-two coil array transmitter reduced the E-field from its 1416~V/m to 990~V/m (around 30\% reduction), though with a slight magnetic field redistribution. All three methods combined, the E-field was successfully attenuated to 82~V/m, just below the strictest limit, without compromising power transfer efficiency. This research demonstrates a feasible approach and framework to safely extend the application of wireless charging for medical implants.

Thomas Feys, Liesbet Van der Perre, François Rottenberg

Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximizes the sum rate affected by non-linear distortion, using a high-order polynomial PA model. In the distortion-limited regime, this GNN-based precoder outperforms zero forcing (ZF), ZF plus digital pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from the state-of-the-art. At an input back-off of -3 dB the proposed precoder compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two and four users respectively. Radiation patterns show that these gains are achieved by transmitting the non-linear distortion in non-user directions. In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and ZF plus DPD respectively. A complexity analysis shows six orders of magnitude reduction compared to DAB precoding. This opens perspectives to operate PAs closer to saturation, which drastically increases their energy efficiency.

Thomas Feys, Liesbet Van der Perre, François Rottenberg

Massive MIMO systems are moving toward increased numbers of radio frequency chains, higher carrier frequencies and larger bandwidths. As such, digital-to-analog converters (DACs) are becoming a bottleneck in terms of hardware complexity and power consumption. In this work, non-linear precoding for coarsely quantized downlink massive MIMO is studied. Given the NP-hard nature of this problem, a graph neural network (GNN) is proposed that directly outputs the precoded quantized vector based on the channel matrix and the intended transmit symbols. The model is trained in a self-supervised manner, by directly maximizing the achievable rate. To overcome the non-differentiability of the objective function, introduced due to the non-differentiable DAC functions, a straight-through Gumbel-softmax estimation of the gradient is proposed. The proposed method achieves a significant increase in achievable sum rate under coarse quantization. For instance, in the single-user case, the proposed method can achieve the same sum rate as maximum ratio transmission (MRT) by using one-bit DAC's as compared to 3 bits for MRT. This reduces the DAC's power consumption by a factor 4-7 and 3 for baseband and RF DACs respectively. This, however, comes at the cost of increased digital signal processing power consumption. When accounting for this, the reduction in overall power consumption holds for a system bandwidth up to 3.5 MHz for baseband DACs, while the RF DACs can maintain a power reduction of 2.9 for higher bandwidths. Notably, indirect effects, which further reduce the power consumption, such as a reduced fronthaul consumption and reduction in other components, are not considered in this analysis.