Despite long effort, few tangible interfaces are presently accessible in reproducible, generalizable form; accompanied by paths for hybrid physical and virtual realization; or coupled with bindings to extended content of widespread relevance. We present hybrid tangible interfaces that engage each of these, oriented both toward the ACM TEI community itself and broader generalizations. These include representations of all papers of the first 20 TEI conferences, structured by year; and of all papers by AI-assisted thematics. These include steps toward tangible “allomorphs” – interaction elements co-designed for both tangible and intangible realization; and fabrication and sensing via multiple low-cost paths.

Shape-changing interfaces offer transformative potential for user interaction across numerous sectors, facilitating more intuitive and immersive experiences. In parallel, their significant design complexity and cost constrain the widespread adoption of these interfaces. To tackle these challenges, MorphMatrix is a toolkit that lowers the entry barrier for designing and implementing shape-changing interfaces. MorphMatrix includes a software system with customized GUI and simulation capabilities; a flexible physical framework adaptable for various application scenarios; and a kinetic system that governs interface transformations. We illustrate the potential of MorphMatrix through five diverse application scenarios, complemented by a user study validating its effectiveness and usability. MorphMatrix streamlines the design and construction of shape-changing interfaces, thus facilitating broader applicability and potential impacts across interactive computational systems.

We describe iterations on the design of a hexagonal token and constraint tangible and cyberphysical interface. Our system has been co-designed both for use in tangible, embedded, embodied interaction and extended reality classroom contexts, and within broader applied generalizations. We survey some of the related literature in hexagonal, rectangular, and triangular tangible interfaces. We express a design space characterizing several aspects of these prior systems; and apply these to a number of additional virtual and physical iterations. We accompany our text with 3D printable, circuit board, and virtual models for a number of these interactors, usable in both tangible and purely virtual forms, including versions engaging four different embedded computing platforms (Raspberry Pi Zero, Raspberry Pi Pico, Adafruit Circuit Playground, and Blinks). We discuss experiences from use of these in classroom settings, and steps toward broader applications.

Thanks to its slow-varying characteristic and relatively low requirement for estimation overhead, the covariance matrix has been extensively researched in sixth-generation (6G) wireless systems. Nevertheless, user mobility in practice will cause a gradual change in the covariance matrix, thereby deteriorating the system's performance if no update of the covariance matrix is applied. In this paper, we study the problem of efficient detection of gradual changes in the covariance matrix. We first introduce four change-point detectors that directly map the observations to change in our target KPI. Then, we propose a low-overhead detection algorithm that omits unnecessary channel estimations by adapting an AoA-based estimation trigger. Simulation results show that our proposed scheme can provide near-optimal performance while drastically reducing the estimation and computation overhead.

In a realistic fifth generation (5G) massive multiple-input multiple-output (MIMO) system, hardware constraints often pose challenges towards network design that are not sufficiently considered in the literature. In this work, we consider a time division duplex (TDD) network where user equipments (UEs) are equipped with N> 1 antennas for receiving in the downlink (DL) but only with a single antenna for transmitting in the uplink (UL). Thus it is not possible to learn the complete downlink channel in a single timeslot from the uplink utilizing channel reciprocity. In this paper, we propose a novel solution based on deep learning with auxiliary input of the estimated single antenna channel in the uplink to accomplish the downlink channel completion for full rank transmission from the base station (BS). We use synthetic data for deep learning training and testing provided by the stochastic quasi-deterministic radio channel generator (QuaDRiGa). Evaluation results show that our work outperforms existing deep learning based algorithms and can provide highly effective recovered channels even with complex channel data and low compression ratio.

In this paper, the spatial consistency of wireless massive single- input-multiple-output channels in a cellular small cell scenario is evaluated based on measurements taken in Berlin city. The evaluation is done by computing the similarity of covariance matrices over the distance. As similarity measure the correlation matrix distance is used. A classification of the measurements tracks based on the shape of the curves into four different categories is done. The results in this paper indicate that spatial consistency is a highly deterministic property in the sense that it depends strongly on the individual environment and not so much on large scale parameters. Therefore, we conclude that spatial consistency is not sufficiently modelled by the current 3rd Generation Partnership Project feature.

Spatial consistency, meaning the spatial correlation of small scale fading, is a recently added feature in the geometry-based stochastic channel model used by 3rd Generation Partnership Project (3GPP) standardization. This feature is relevant for schemes that utilize channel correlation among users, e.g. clustering according to second order channel statistics. This paper provides a simulation based evaluation of the spatial consistency feature implemented in the open source quasi-deterministic radio channel generator (QuaDRiGa) channel model in terms of angle difference of multi-path components over distance as well as similarity of transmit covariance matrices. This evaluation helps future work that uses the spatial consistency feature in setting their parameters.

In this paper, massive MIMO amplify-and-forward multi-pair relaying is considered to assist device-to-device (D2D) communications. The direct D2D communication is underlaid with the source-to-relay transmission in the first hop, hence the D2D signals are also received at the relay, which are then forwarded in the second hop, overlapping with the relay-to-destination transmission. Moreover, D2D users are assumed to be able to harvest energy from the relay signal. Therefore, the D2D receivers, who failed in the direct D2D, can perform information detection (ID) with an opportunistic energy harvesting (EH) in the second hop. Closed-form deterministic equivalents of the associated signal to interference-plus-noise ratio in the ID and the harvested energy in the EH are derived, for both the maximum ratio combining/transmission and zero-forcing based relaying. A particular result of the asymptotic analysis indicates that the transmit power of source users can be made inversely proportional to the number of relay antennas, such that non-vanishing relay assisted D2D rate and harvested energy of D2D users exist. Simulations validate the analytical results, and illustrate the capability of massive MIMO relaying in assisting D2D via the so-called rate-energy region.