Microwave

This work presents a fully integrated, two-stage, deep class-AB power amplifier (PA) operating at a center frequency of 60 GHz. High efficiency and suppression of third-order intermodulation products are targeted, achieving improved linearity compared to reported state-of-the-art designs. A current combining architecture is also employed to enhance the output power capability. The PA is designed in a 22 nm FD-SOI CMOS technology and is optimized through a complete schematic-to-layout design flow. Post-layout simulations indicate that the PA achieves a peak power-added efficiency (PAE) of 28%, a saturated output power ($P_{sat}$) of 20.2 dBm, and a maximum large-signal gain ($G_{max}$) of 19.6 dB at 60 GHz, evaluated at an operating temperature of 60 °C. The design maintains high linearity across the targeted output power range, exhibiting effective suppression of third-order intermodulation distortion (IMD3), which enhances its suitability for spectrally efficient modulation schemes.

Accurate user positioning in 6G networks is essential for next-generation mobile services. However, classical approaches such as time-difference-of-arrival (TDoA) remain vulnerable to dense multipath and NLoS conditions commonly found in indoor and industrial environments. This paper proposes an AI-driven RF fingerprinting framework that leverages uplink channel state information (CSI) to achieve robust and privacy-preserving 2D localization. A lightweight convolutional neural network (CNN) extracts location-specific spectral–spatial fingerprints from CSI tensors, while a federated learning (FL) scheme enables distributed training across multiple gNBs without sharing raw channel data. The proposed integration of CSI tensor processing with FL and structured pruning is introduced as a novel solution for practical 6G edge positioning. To further reduce latency and communication costs, a structured pruning mechanism compresses the model by 40–60%, lowering the memory footprint with negligible accuracy loss. A performance evaluation in 3GPP-compliant indoor factory scenarios indicates a median positioning error below 1 m for over 90% of cases, significantly outperforming TDoA. Moreover, the compressed FL model reduces the FL communication load by ~38% and accelerates local training, establishing an efficient, secure, and deployment-ready positioning solution for 6G networks.

Microwave heating technology has gained extensive application in various fields, including food processing and drying, material synthesis, and waste treatment, due to its advantages of high efficiency, selectivity, and rapid response. However, the inherent non-uniform electromagnetic field distribution within metallic microwave cavities leads to uneven heating. This issue severely constrains the large-scale industrial application of microwave heating, particularly its extension into high-precision, high-value-added industrial sectors like 3D printing, curing, and microwave plasma micro/nano-fabrication. To address this challenge, extensive research efforts have been undertaken in both academia and industry, focusing on improving microwave heating uniformity, resulting in the proposal of various methods. This review summarizes these methods for enhancing microwave heating uniformity and specifically outlines recent research progress on novel techniques based on artificial electromagnetic surfaces. It aims to offer valuable references and guidance for the broader application of microwave heating technology.