Adaptive and Robust Beam Selection in Millimeter-Wave Massive MIMO Systems

Future 6G wireless communications network will increase the data capacity to unprecedented numbers and thus empower the deployment of new real-time applications. Millimeter-Wave (mmWave) band and Massive MIMO are considered as two of the main pillars of 6G to handle the gigantic influx in data traffic and number of mobile users and IoT devices. The small wavelengths at these frequencies mean that more antenna elements can be placed in the same area. Thereby, high spatial processing gains are achievable that can theoretically compensate for the higher isotropic path loss. The propagation characteristics at mmWave band, create sparse channels in typical scenarios, where only few paths convey significant power. Considering this feature, Hybrid (analog-digital) Beamforming introduces a new signal processing framework which enables energy and cost-efficient implementation of massive MIMO with innovative smart arrays. In this setup, the analog beamalignment via beam selection in link access phase, is the critical performance limiting step. Considering the variable operating condition in mmWave channels, a desirable solution should have the following features: efficiency in training (limited coherence time, delay constraints), adaptivity to channel conditions (large SNR range) and robustness to realized channels (LOS, NLOS, Multipath, non-ideal beam patterns). For the link access task, we present a new energy-detection framework based on variable length channel measurements with (orthogonal) beam codebooks. The proposed beam selection technique denoted as composite M-ary Sequential Competition Test (SCT) solves the beam selection problem when knowledge about the SNR operating point is not available. It adaptively changes the test length when the SNR varies to achieve an essentially constant performance level. In addition, it is robust to non-ideal beam patterns and different types of the realized channel. Compared to the conventional fixed length energy-detection techniques, the SCT can increase the training efficiency up to two times while reducing the delay if the channel condition is good. Having the flexibility to allocate resources for channel measurements through different beams adaptively in time, we improve the SCT to eliminate unpromising beams from the remaining candidate set as soon as possible. In this way, the Sequential Competition and Elimination Test (SCET) significantly further reduces training time by increasing the efficiency. The developed ideas can be applied with different codebook types considered for practical applications. The reliable performance of the beam selection technique is evident through experimental evaluation done using the state-of-the-art test-bed developed at the Vodafone Chair that combines a Universal Software Radio Peripheral (USRP) based platform with mmWave frontends

RealTimeChess: Lessons from a Participatory Design Process for a Collaborative Multi-Touch, Multi-User Game

We report on a long-term participatory design process during which we designed and improved RealTimeChess, a collaborative but competitive game that is played using touch input by multiple people on a tabletop display. During the design process we integrated concurrent input from all players and pace control, allowing us to steer the interaction along a continuum between high-paced simultaneous and low-paced turn-based gameplay. In addition, we integrated tutorials for teaching interaction techniques, mechanisms to control territoriality, remote interaction, and alert feedback. Integrating these mechanism during the participatory design process allowed us to examine their effects in detail, revealing for instance effects of the competitive setting on the perception of awareness as well as territoriality. More generally, the resulting application provided us with a testbed to study interaction on shared tabletop surfaces and yielded insights important for other time-critical or attention-demanding applications.

Chatbots with Personality Using Deep Learning

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, what they lack and what can be improved