Microwave

Continuous-wave magnetrons continue to offer the highest efficiency, lowest cost per watt, and greatest compactness among high-power microwave sources, making them attractive for industrial, scientific, and defense applications. Emerging missions, particularly space solar power systems, industrial microwave heating, and accelerators, demand significantly enhanced performance metrics, including high DC-to-RF efficiency, thermal stability, ultra-low phase noise, and precise phase controllability for coherent operation. To satisfy the critical requirement for high power, low-cost microwave sources with high spectral purity, extensive research has focused on injection-locking techniques, external phase/frequency modulation methods, and large-scale coherent power combining. This paper reviews the fundamental characteristics of CW magnetrons, recent advances in injection-locked magnetron transmitters, power-combining systems employing multiple injection-locked magnetrons, magnetron-based phased-array systems, and emerging applications. Finally, the challenges and promising development directions for next-generation CW magnetrons are discussed.

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.