The continuous proliferation of applications requiring wireless connectivity will eventually result in latency and reliability requirements beyond what is achievable with current technologies. Such applications can for example include industrial control at the sensor-actuator level, intra-vehicle communication, fast closed loop control in intra-body networks and intra-avionics communication. In this article, we present the design of short range Wireless Isochronous Real Time (WIRT) in-X subnetworks aimed at life-critical applications with communication cycles shorter than 0.1 ms and outage probability below 10 −6. Such targets are clearly beyond what is supported by the 5th Generation (5G) radio technology, and position WIRT as a possible 6th Generation (6G) system. WIRT subnetworks are envisioned to be deployed for instance in industrial production modules, robots, or inside vehicles. We identify technology components as well as spectrum bands for WIRT subnetworks and present major design aspects including frame structure and transmission techniques. The performance evaluation considering a dense scenario with up to 2 devices per m 2 reveal that a multi-GHz spectrum may be required for ensuring high spatial service availability. The possibility of running WIRT as an ultra-wideband underlay system in the centimeter-wave spectrum region is also discussed. Aspects related to design of techniques for the control plane as well as enhancements to the presented design is the focus of our ongoing research.

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Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data.

This paper generalizes a propagation graph model to polarized indoor wireless channels. In the original contribution, the channel is modelled as a propagation graph in which vertices represent transmitters, receivers and scatterers while edges represents the propagation conditions between vertices. Each edge is characterized by an edge transfer function accounting for the attenuation, delay spread and phase shift on the edge. In this contribution, we extend this modelling formalism to polarized channels by incorporating depolarization effects into the edge transfer functions and hence, the channel transfer matrix. We derive closed form expressions for the polarimetric power delay spectrum and cross-polarization ratio of the indoor channel. The expressions are derived considering average signal propagation in a graph and relate these statistics to model parameters, thereby providing a useful approach to investigate the averaged effect of these parameters on the channel statistics. Furthermore, we present a procedure for calibrating the model based on method of moments. Simulations were performed to validate the proposed model and the derived approximate expressions using both synthetic data and channel measurements at 15 GHz and 60 GHz. We observe that the model and approximate expressions provide good fits to the measurement data.

In this paper, we investigate dynamic channel selection in short-range Wireless Isochronous Real Time (WIRT) in-X subnetworks aimed at supporting fast closed-loop control with super-short communication cycle (below 0.1 ms) and extreme reliability (>99.999999%). We consider fully distributed approaches in which each subnetwork selects a channel group for transmission in order to guarantee the requirements based solely on its local sensing measurements without the possibility for exchange of information between subnetworks. We present three fully distributed schemes: ϵ-greedy channel allocation, minimum SINR guarantee (minSINR) and Nearest Neighbor Conflict Avoidance (NNCA) based on measurements of the minimum SINR and interference power. We further apply a centralized graph coloring scheme as a baseline for evaluating performance of the proposed distributed algorithms. Performance evaluation considering subnetwork mobility and spatio-temporal correlated channel models shows that the dynamic allocation schemes results in significant performance improvement and a reduction in the bandwidth required for supporting such extreme connectivity by up to a factor larger than 2 relative to static channel assignment.