This article presents an overview of current Industry 4.0 applied research topics, addressed from both the industrial production and wireless communication points view. A roadmap toward achieving more advanced manufacturing visions concepts, such as "swarm production" (nonlinear fully decentralized production) is defined, highlighting relevant use cases, their associated requirements, well integrated technological solutions applicable to each them. Further, introduces Aalborg University 5G...

The continuous proliferation of applications requiring wireless connectivity will eventually result in latency and reliability requirements beyond what is achievable with current technologies. Such can for example include industrial control at the sensor-actuator level, intra-vehicle communication, fast closed loop intra-body networks intra-avionics communication. In this article, we present design short range Wireless Isochronous Real Time (WIRT) in-X subnetworks aimed life-critical...

The 6th Generation (6G) radio access technology is expected to support extreme communication requirements in terms of throughput, latency and reliability, which can only be achieved by providing capillary wireless coverage. In this paper, we present our vision for short-range low power 6G 'in-X' subnetworks, with the 'X' standing entity cell deployed, e.g., a production module, robot, vehicle, house or even human body. Such cells services that life-critical traditionally relied on wired...

Short range low power 6th Generation (6G) wireless subnetworks can support life critical services like engine and break control in intra-vehicle scenarios, or intra-body heart-rate control. Such may target communication cycles below 0.1 ms a wired-like reliability, translating to multi-GHz spectrum demand case of dense deployments (e.g., up 40000 per km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). We foresee the possibility for 6G...

The deployment of relays between Internet Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, have the potential to reduce base station overload. energy expended in single-hop long-range communication be reduced if listen transmissions forward these observations gateways. However, incorporating into IoT networks faces some challenges. are designed primarily for uplink small-sized toward network; hence, opportunistically using as needs a redesign both medium...

This paper investigates the prediction of multiple-input-multiple-output (MIMO) narrow-band multipath fading channels for mobile-to-mobile (M-to-M) wireless communication systems. Using a statistical model M-to-M in urban and suburban environments, we derive parameterized double directional utilize multidimensional extension ESPRIT algorithm to jointly estimate angles departure (AoD), arrival (AoA), effective Doppler frequencies. A simple method is also proposed mobile velocity estimation....

In this paper, we present results on the application of machine learning to detection human presence and estimation number occupants in our offices using data from an IoT LoRa-based indoor environment monitoring system at Aalborg University, Denmark. We cast problem as either binary or multi-class classification apply a two-layer feed forward neural network data. The used for training, validation testing comprises environmental sensors manual recordings door window states. Results show that...

This letter proposes a machine learning-based method for the calibration of stochastic radio propagation models. Model is cast as regression problem involving mapping channel transfer function or impulse response to model parameters. A multilayer perceptron trained with summary statistics computed from synthetically generated realizations using model. To calibrate model, network used estimate parameters obtained measurements. The performance proposed evaluated graph and Saleh-Valenzuela...

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 approximate Bayesian computation (ABC) the other deep learning, for fitting to directly. The proposed methods make use easy-to-compute summary statistics measured instead relying extracted components. Moreover, need post-processing components omitted....

Short-range low-power 6th generation (6G) in-X subnetworks are proposed as a viable radio concept for supporting extreme communication requirements in emerging applications such wireless control of robotic arms and critical on-body devices, e.g. heart pacemaker. For these applications, ultra-high reliability (e.g., above 6 nines) with sub-ms latency must be guaranteed at all spatio-temporal instants. To meet requirements, systems that robust against fading interference crucial. In this...

In-X subnetworks are expected to be located at the very edge of 6G 'network networks' and provide localized wireless connectivity for in-vehicle, in-robot, in-body communication. By nature in-X can lead dense crowds, calling efficient radio resource management techniques. In this article, we introduce a hybrid framework where decision capabilities either happen global agent operating on an umbrella network, or locally each subnetwork, depending quality backhaul link, crowd density local...

6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power can improve spectral efficiency dense deployments of subnetworks. However, existing solutions may require full channel state information (CSI) all the desired interfering links, which be cumbersome time-consuming obtain deployments. This paper presents a novel solution based on Graph...

Transmit power control (PC) will become increasingly crucial in alleviating interference as the densification of wireless networks continues towards 6G. However, practicality most PC methods suffers from high complexity, including sensing and signalling overhead needed to obtain channel state information. In a highly dense scenario such deployment short-range cells installed within production entities, termed in-factory subnetworks (InF-S), major limitation. this paper, we represent InF-S...

This paper generalizes a propagation graph model to polarized indoor wireless channels. In the original contribution, channel is modeled as in which vertices represent transmitters, receivers, and scatterers, while edges conditions between vertices. Each edge characterized by an transfer function accounting for attenuation, delay spread, phase shift on edge. this we extend modeling formalism channels incorporating depolarization effects into functions hence, matrix. We derive closed form...

This paper investigates efficient deep learning based methods for interference mitigation in independent wireless subnetworks via dynamic allocation of radio resources. Resource is cast as a mapping from power measurements at each subnetwork to class shared frequency channels. A neural network (DNN) then trained approximate this using data obtained application centralized graph coloring (CGC). The deployed distributed channel selection. Simulation results an environment with mobile have...

In this paper, we propose an ESPRIT-based parametric prediction scheme for narrowband MIMO systems that fully exploits both temporal and spatial correlations in realistic channels. The proposed predictor uses a vector transmit signature model two-dimensional ESPRIT the estimation of channel parameters. outperforms existing algorithms is well suited to two dimensional azimuth only three models.

In this paper, we investigate dynamic channel se- lection 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 which each subnetwork selects a group for transmission order to guarantee the requirements based solely on its local sensing measurements without possibility exchange of information between...

A novel prediction scheme for polarized narrowband MIMO channels is proposed in this paper. The based on estimation of the parameters a double directional propagation model. algorithm transforms channel impulse response matrix such manner that multidimensional extension ESPRIT can be utilized to jointly estimate angles arrival, departure, Doppler shifts and complex polarimetric weights dominant multipath components. Simulation results show outperforms repeated application one dimensional...

This paper presents the results of wireless channel measurement campaign in 3 GHz to 8 frequency range. The measurements were performed with focus on short-range a transmitter-receiver separation distance less than 9 m two typical industrial environments: low clutter density manufacturing space, and high one. We analyzed statistical properties most important temporal large-scale propagation characteristics including total received energy, path loss exponent, maximum excess delay (MED) root...

This paper proposes a method to infer on the parameters of stochastic channel model from observations temporal moments without multipath extraction. The distribution is approximated be Gaussian, and sampling carried out approximate posterior. are found informative about parameters, as can recovered samples.

Stochastic channel models are usually calibrated after extracting the parameters of multipath components from measurements. This paper proposes a method to infer on underlying stochastic model, in particular Turin without resolving components. Channel measurements summarised into temporal moments instead parameters. The model then estimated observations using approach. estimator is tested real data obtained in-room It concluded that calibration can be done extraction, and informative summary...