Human Machine Interaction based on air gestures finds an increasing number of applications in consumer electronics. The availability of mmWave technology, combined with machine learning, allows the detection and classification of gestures, avoiding high-resolution LIDAR or video sensors Nevertheless, in most of the existing studies, the processing takes place offline, takes into account only the velocity and distance of the moving arm, and can handle only gestures that are conducted very close to the sensor device, which limits the range of possible applications. Here, we use an experimental multi-channel mmWave- based system that can detect small targets, like a moving arm, up to a few meters away from the sensor. As our pipeline can estimate and take into account the angle of arrival in azimuth and elevation, it has the ability to classify a greater variety of dynamic gestures. Furthermore, the digital signal processing chain we present here, runs in real-time, incorporating an event detector. Whenever an event is detected, a novel empirical feature extraction takes place and a Multi-Layer Perceptron is deployed to infer the type of the gesture. To evaluate our setup and signal processing pipeline, a dataset with ten subjects, performing nine gestures was recorded. Our method yielded 94.3% accuracy on the test set, indicating a successful combination of our proposed sensor and signal processing pipeline for real time applications.

Massive multiple-input multiple-output (MIMO) systems operating in the centimeter-wave (cmWave) and millimeter-wave (mmWave) region offer huge spectral efficiencies, which enable to satisfy the urgent need for higher data rates in mobile communication networks. However, the proper design of those massive MIMO systems first requires a deep understanding of the underlying wireless propagation channel. Therefore, we present a fully-digital MIMO measurement system operating around 28 GHz. The system enables to take fast subsequent snapshots of the complex MIMO channel matrix. Based on this method we statistically analyze the time-dependent channel behavior, the achievable signal quality and spectral efficiency, as well as the channel eigenvalue profile. Furthermore, the presented calibration approach for the receiver enables an estimation of the dominant absolute angle of arrival (AoA) and allows us to draw conclusions about the line-of-sight (LOS) dominance of the scenario. In total, 159 uplink communication measurements over 20 seconds are conducted in three different small cell site scenarios to investigate the wireless propagation behavior. The measurements reveal the existence of several spatial propagation paths between the mobile transmitter and the base station. Furthermore, an insight into their likelihood in different propagation scenarios is also given.

Subarray-based hybrid beamforming (SBHB) systems represent an efficient architectural solution to realize massive multiple-input multiple-output (MIMO) systems. Thereby, the beamforming is no longer realized exclusively in the digital domain, but in coordination with an analog beamforming network by connecting each digital channel to a dedicated number of antenna elements via phase shifters. Most channel estimation and beamforming algorithms for SBHB systems are based on beam steering approaches, demanding a precise calibration of the analog beamforming network. In this letter, the SBHB system is calibrated by over-the-air (OTA) scattering parameter measurements and two criteria to optimize the beam pattern are formulated exploiting the ambiguities of the phase shifters. Both optimization criteria are evaluated using a designed SBHB receiver system operating at 27.8 GHz.

Hybrid beamforming systems represent an efficient architectural solution to realize massive multiple-input multiple-output (MIMO) communication systems in the centimeter wave (cmW) and millimeter wave (mmW) region. These hybrid beamforming systems separate the beamforming process into a digital and analog beamforming network. The analog beamforming networks can be realized by different architectural solutions, which demand dedicated algorithms to determine the complex weighting factors in the digital and analog domain. To date, novel hybrid beamforming architectures and algorithms are solely compared in numerical simulations based on statistical channel models. These abstract channel models simplify the complicated electromagnetic propagation process, thereby not exactly reconstructing the wireless channel. Within this work, we present a measurement-based evaluation of hybrid beamforming algorithms and compare them with numerical results gained from a statistical path-based MIMO channel model. The results show that by adjustment of the channel model parameter the simulation achieves a good match with the measured maximum achievable spectral efficiencies.