Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase.

This paper introduces new functionalities for industrial safety applications using the wireless infrastructure of the IEEE 802.11 standard. The new amendments (n/ac/ad) for the 2.4, 5, and 60-GHz-band support additional high data rate modes, for e.g. video applications and device-to-device (D2D) communication. The huge bandwidth in the 60-GHz-band enables precise localization. However, Listen-Before-Talk (LBT) has to be used in order to guarantee fair channel access possibilities and coexistence of many devices. The access requirements are regulated (e.g. by ETSI for the European region). To cope with the uncertainty of coexistence and the special channel conditions in an industrial environment a multi-band PHY and MAC solution with multi-link-connectivity (MLC) support is introduced. This enables highly reliable low-latency wireless communication links for industrial safety applications. The simultaneous Round-Trip-Time-of-Flight (RTToF) ranging measurements enable position based features, like the introduced virtual safe zone.

The application of radar in the medical field has attracted a lot of attention recently. Vital sign detection and monitoring are essential for certain use cases. Due to the limitations and discomfort during contact-based vital sign monitoring, the non-contact approach becomes preferred. In this work, a signal processing approach for multi-target breathing rate and heart rate monitoring is proposed. The proposed approach uses a frequency modulated continuous wave (FMCW) radar for contact-less vital sign monitoring. Using the proposed approach, the vital signs of multiple persons can be monitored simultaneously using a single mmWave radar. The results obtained using the proposed approach show an excellent match compared to a medical-grade instrument. This is especially notable for the heart rate monitoring, where the achieved accuracy error is only 7.77 %, outperforming other similar works.

This article presents a 220-260 GHz fully integrated phased-array wireless system featuring direct conversion RF beam-forming. The system is constructed using fully integrated transmitter (Tx) and receiver (Rx) chips with on-chip antenna array. A 4-channel Tx and Rx are designed and fabricated in a 130 nm SiGe BiCMOS process with f$_{\mathbf{T}}$ / f$_{\mathbf{max}}$ = 300 / 500 GHz. A modular design approach enables the chips as building units for 2×N phased arrays and multiple-input multiple-output (MIMO) systems. A comprehensive design approach for the Tx and Rx chips focusing on key design decisions is presented in this work. The transmitter is equipped with a local oscillator (LO) multiplication chain, IQ up $-$ conversion mixer, active RF splitting network, vector modulator phase shifter (VMPS), temperature sensors, and high output power amplifiers (PA). The PA with power $-$ combining boost the effective isotropic radiated power (EIRP) and reduces the need for external lenses. The receiver is equipped with an LO chain, IQ down $-$ conversion mixer, active RF combining network, VMPS, and low noise amplifiers (LNA). In both Tx and Rx the antenna array is composed of four differential double-folded dipole antennas with local backside etching (LBE). The Tx and Rx chips consume 4.4 W and 1.84 W of power respectively from a 3.5 V supply while each occupying 25 mm $^\rm{2}$ of silicon area. With a measured Tx array effective isotropic radiated power (EIRP) of 24 dBm, a beamforming wireless link is demonstrated supporting up to 50 Gbps of data rates across 85 cm of link distance with no need for focusing lenses and $\pm 30^\circ$ of scanning capability. With these capabilities, the presented modular chips enable future scaling for 2× N antenna arrays for sensing and communication applications.

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync&loc) problem in ultra-dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs’ clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by the exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packets, DePF draws on the multiple signal classification algorithm that is fed with the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and, thus, jointly estimates the position and clock parameters of the MUs. The simulation results verify the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of the extended Kalman filter- and linearized BRF-based joint sync&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs from a single AP, for 90% of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.