Abstract Identity management of devices in the Internet of Things (IoT) has become an essential part of a secure IoT infrastructure. Enrollment and authentication is performed based on public key infrastructure (PKI) in state-of-the-art deployments. This ensures device authenticity based on certificates. To mitigate the risk of compromised certificates and to strengthen the security, the certificates can be created on the device during enrollment based on physical properties, such as a physical unclonable function (PUF) of the device or a connected secure element. Identity management of devices in the Internet of Things (IoT) has become an essential part of a secure IoT infrastructure. Enrollment and authentication is performed based on public key infrastructure (PKI) in state-of-the-art deployments. This ensures device authenticity based on certificates. To mitigate the risk of compromised certificates and to strengthen the security, the certificates can be created on the device during enrollment based on physical properties, such as a physical unclonable function (PUF) of the device or a connected secure element. To add further physical properties to the device identity, we propose AARC-FE: Electrical A ssemblie A uthentication with R andom C onvoultion Kernels and F uzzy E xtractors. This is a new approach for hardware fingerprint creation of a serial communication interface. The proposed solution uses characteristics of the analog signal introduced by manufacturing variances, which are made detectable by using random convolutional kernels. The features produced by the convolution are transformed in a two-step approach to suit the requirements of a fuzzy extractor that is used to create strong keys for the observed communication interface. We create a stochastic model for evaluating the analog domain and transfer this model to a SPICE-based simulation of a common communication bus: $${\textrm{I}^{2}}\textrm{C}$$ I 2 C . To characterize the approach, metrics for Physical Unclonable Functions (PUFs) and authentication systems are applied. The evaluation shows, that AARC-FE is a feasible approach for key generation of an electrical assembly. Depending on the chosen parameters, the authentication system achieves an equal error rate (EER) as low as 0.09. With this approach, it is possible to detect attacks such as device swapping or sniffing attacks on a serial communication bus. The system derives asymmetric keys from the analog values of the communication bus and can thus participate in standard public key authentication schemes.

To address the demand for near real-time applications in 6G networks, we propose FLINT, a context-aware task scheduling system for in-network computing. Unlike hardwaredependent approaches, FLINT utilizes dynamic compute nodes connected to routers, enabling flexible hardware utilization. By employing static code embeddings from a large transformer model and small metric estimation models on each compute blade, FLINT efficiently assigns unseen tasks based on their binaries and arguments. This cooperative scheduling approach between blades and the central router allows for dynamic deployments and blade exchangeability. Evaluation of diverse WebAssembly tasks and hardware demonstrates FLINT's effectiveness, achieving an F1 score and accuracy of over 0.9 on our taskset while maintaining low response time overhead.

Reliable vehicle-to-vehicle communication is essential for maintaining stability in cooperative platooning. While prior work has explored control under communication constraints, few have quantitatively linked communication quality—specifically delay and packet loss—to control performance. This paper addresses that gap through a co-design framework that connects Quality of Service (QoS), defined by the average packet success probability, to Quality of Control (QoC), measured by the Downstream Propagation Factor (DPF). The model incorporates delay, packet loss, and fallback behavior under a Cooperative Adaptive Cruise Control (CACC) policy.Simulations reveal a nonlinear coupling between sampling interval, communication reliability, and platoon stability. In particular, larger sampling intervals increase sensitivity to communication loss. We observe a continuous, sharply descending relationship between average success rate and DPF, well-approximated by an exponential decay. This enables a simple yet effective model for predicting control performance under varying network conditions, supporting the design of robust and scalable platoons.

In IoT applications, embedded devices acquire and transmit data to control and optimize industrial processes. To trust this data, the data acquisition system, such as the sensors and the integrated signal processing components itself must be trusted. Approaches like hardware fingerprinting try to improve the overall security of such systems.

Subnetworks with different levels of autonomy are an integral part of 6G, helping to improve local connectivity, resilience, and security. These are especially interesting for cooperative robotics on shop floors, where multiple robots can come together and directly exchange task-specific data in near real-time without stressing the parent network. The same sub-network capabilities are currently implemented into Automated Guided Vehicles (AGVs), allowing non-time-critical data from near monitoring equipment to be offloaded via AGVs instead of streaming them directly to the shop floor's parent network, relieving the parent network in the process. However, to do so, an efficient approach is needed, which (1) can predict future encounters between AGVs and monitoring nodes on a node-to-node basis with minimal traffic overhead, (2) does not require heavy computation like machine learning on highly constraint nodes, and (3) does not need access to AGVs' subnetwork controllers and routing tables.Introducing ConTST (CONnection forecasting with Time Series Transformers), a semi-passive, probabilistic approach for predicting future data offloading possibilities to autonomous mobile subnetworks on a node-to-node basis from only successfully received bundles in the network edge. We utilize a single, small probabilistic time series transformer model in the network edge, minimal additional metadata per bundle, and some tracing bundles to successfully estimate future encounters from past encounters. Nodes optimize their offloading strategy greedily by only utilizing precomputed encounter tables from the edge services, making this approach compatible even with the most constrained IoT devices. We extensively evaluate our approach on our shop floor simulator and show that it is resilient against outliers, capable of predicting future encounters, and can reliably optimize overall offloading Quality of Service (QoS) metrics like bundle latency or lost bundle ratio.

As energy consumption of cellular networks continues to rise, its impact on the environment and operating costs have become a pressing concern. With the goal of improving energy efficiency by reducing the energy consumption, Network Energy Savings (NES) is considered by 3GPP as one of the key enhancements of the 5G-Advanced and 6G standardization. This paper addresses a key time-domain NES technique being standardized by 3GPP, namely, adaptation of downlink common signals, providing a comprehensive evaluation based on system-level simulations. We propose a novel approach to dynamically adapt the periodicity of downlink common signals based on network demand. The results demonstrate great benefits of the dynamic adaptation of downlink common signals in terms of energy saving gains.

Joint communications and radar sensing (JCRS) is regarded as a key application of the future 6G technologies. The phase-modulated continuous waveform (PMCW) has become popular for JCRS in recent years due to its strong anti-interference properties and low peak-to-average power ratio. Conventional PMCW systems suffer from several common problems, like low data rates and susceptibility to the Doppler effect. To address these shortcomings, this paper provides a novel PMCW approach that implements a parallel pilot and data transmission, where the pilot and data are divided by orthogonal codes. Compared to the conventional PMCW systems, the proposed method benefits from a flexible configuration of communications parameters and higher resource efficiency, improving communications performance while not influencing the radar function. Besides, the presented approach also reduces the impact of the Doppler effect on the reliability of communications. At last, the feasibility of this new method is verified, and its performance and application scenarios are analyzed.

The fifth-generation (5G) and beyond standards are being challenged by the diverse requirements of modern use cases. Multicarrier modulation techniques are one of the key components of the physical layer (PHY) design, which has immense potential to improve efficiency and reliability. In current state-of-the-art wireless technologies (i.e., NR and IEEE 802.11ax) orthogonal frequency division multiplexing (OFDM) is used which has many disadvantages such as peak-to-average power ratio (PAPR), out-of-band emission (OOBE), and sensitivity to carrier frequency offset (CFO). To overcome these drawbacks several alternate multicarrier modulation techniques are being considered, such as discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM), and orthogonal time-frequency space (OTFS). In this paper, we develop the physical layer abstraction (PLA) of these candidate multicarrier techniques to evaluate their performance under various use cases and scenarios. The PLA is a commonly used technique to avoid time-consuming PHY simulations in system-level simulators. To improve the accuracy of PLA in different fading conditions, we derive a fitting parameter as a function of the received signal-to-interference-plus-noise ratio (SINR) variance. The validation results show that performance can be accurately estimated through the proposed multicarrier PLA. Moreover, PLA techniques are at least thousands of times faster compared to PHY simulations.

The multi-connectivity is a key enabler for ultra-reliable low-latency communications. To evaluate its performance and suitability to various use cases system-level studies are essential, where the physical layer (PHY) plays an important role. Therefore, PHY modeling is required which is time-intensive and requires highly complex computations. For this purpose, the PHY performance is usually abstracted in terms of signal-to-interference-plus-noise ratio (SINR), also known as physical layer abstraction (PLA). However, due to fading, the symbols inside a packet could have different SINRs which require effective SINR mapping to compute an equivalent SINR. In the context of multi-connectivity, the received SINR depends on all connected links where each link experiences independent fading and on the used link combining technique. As a result, the computation of effective SINR also depends on the combining technique and fading experienced by individual links. To model PHY performance by considering all these effects, we develop PLA for multi-connectivity communications. This includes the computation of received symbols SINR for various link combining techniques and mapping them to effective SINR using enhanced exponential effective SINR mapping (eEESM). Furthermore, a new optimization method is introduced for eEESM to reduce its optimization complexity. Simulation results show that the proposed PLA accurately estimate the performance of different order of multi-connectivity communications under various fading conditions.

The ultra-high reliability is an essential requirement for enhanced vehicle-to-everything (eV2X) use cases. In order to ensure the desired reliability in a time-varying channel, link adaptation is required. Therefore, the current and upcoming technologies use adaptive modulation and coding (AMC) schemes. By using AM C, the reliability and data rates could be adapted according to the channel conditions. To further improve reliability, concepts like multi-connectivity could also be used. In multi-connectivity, redundant data can be transmitted using multiple simultaneous links and combined at the receiver to improve reliability. However, this requires link adaption in terms of both numbers of multiple links and AMC. In this paper, we evaluate different link adaptation schemes for IEEE 802.11bd based on single-link and multi-connectivity communications. For single-link communications, we generate channel quality indicators (CQI) based on various signal-to-interference-plus-noise ratio (SINR) mapping techniques, e.g., exponential effective SINR mapping (EESM), received bit information rate (RBIR), and recently proposed enhanced EESM (eEESM). The performance of these schemes is evaluated in terms of achieved reliability and data rates. Results show that eEESM achieves close to optimal performance. In the case of multi-connectivity, different MCS and link adaptation schemes are evaluated. It is shown that joint adaptation of MCS and the number of links deliver better performance in terms of data rates and link utilization.

Wireless closed-loop control is of major significance for future industrial manufacturing. However, control applications pose stringent quality of service requirements for reliable operation. Contrary to traditional ultra-reliable low-latency communications design goals such as low packet loss rates and low latency, research results in the domain of networked control systems (NCS) state that depending on the sampling period, control applications inherently tolerate a few consecutive packet losses. This translates into a better-suited metric to capture control application requirements and therefore a more conclusive design goal for wireless networks: ensuring a maximum age of information (AoI). With a Markov modeling approach, we propose to exploit the tolerance through a novel dynamic multi-connectivity scheme that we term state-aware resource allocation (SARA), which temporally negatively correlates packet losses, thus avoiding long packet loss sequences. Through statistical multiplexing, SARA enables a mean time to failure (MTTF) in the order of years while keeping the per-agent average channel usage close to one, also in a multi-agent setting with competition for resources. Compared with static dual-connectivity, the MTTF can be increased 100-fold whereas the number of required channels reduces by 40%. Our approach also statistically guarantees system-wide AoI distributions, which aid to ensure control performance.

Wireless closed-loop control is of major significance for different application areas, such as future industrial manufacturing, and ultra-reliable low-latency communications (URLLC) are designed to enable such systems. Static multi-connectivity, in which a number of independent parallel channels are allocated for each service, is a possible solution to achieve URLLC requirements, but this increases resource usage significantly, which becomes an issue particularly in multi-user systems. Building upon a control-communications codesign (CoCoCo) approach, a control-application optimized state-aware resource allocation (SARA) scheme was developed, which exploits the control cycle's inherent capability of tolerating a limited number of consecutive packet losses before ultimately failing. In essence, SARA negatively correlates packet losses through dynamic channel allocation in order to yield extraordinary availability values while keeping the average resource consumption low. This article develops a multi-user system representation of SARA with competition for limited resources using a Markov chain approach and subsequently evaluates the mean time to failure, demonstrating that SARA scales better than static multi-connectivity, fully supporting the maximum system availability at fewer channels per agent.

Control communications co-design enables robust and scalable wireless closed-loop control system design. We study the metric “control-communications availability” that allows consecutive errors until the control application is deemed dysfunctional. Multi-connectivity helps increasing the network availability, but there is a lack of dynamic and resource efficient link management. Thus, we propose the “state-aware resource allocation” scheme, whereby parallel links can be assigned adaptively to a given connection, depending on the number of previously, consecutively lost packets. We develop a Markov chain that captures the novel resource allocation approach suited for closed-loop wireless control applications. Our approach outperforms static dual connectivity by two orders of magnitude in terms of control-communications availability while reducing the amount of required resources to approximately half.

In [1] the upper-bound on the communications interval required to achieve string stability for a Cooperative Autonomous Cruise Control (CACC) system, i.e. the Maximum Allowable Transmission Interval (MATI), was found for the one-vehicle look ahead topology (vehicles communicating only with their immediate predecessors). Obtaining the MATI permitted to exploit the CACC dynamics as degrees of freedom for radio resource allocation while guaranteeing the proper functioning of CACC [2]. By transmitting only at the necessary update rate dictated by the system dynamics, the demands on the communication network are relaxed compared to an agnostic static allocation. This paper considers additional information from N preceding vehicles for further relaxation of the communications requirements. The hypothesis is that by over-hearing transmissions of additional predecessors, the update rate between vehicles can be reduced while maintaining string stability. Since overhearing state information of additional previous predecessors requires to process their information coherently, the design of N feedforward filters is necessary when considering N previous vehicles. In this work, we propose a methodology to design the feedforward filters that optimizes the MATI within the platoon based on a frequency domain analysis by posing an optimization problem. We show that transmission intervals on the wireless links can be relaxed up to 8x when a two-vehicle look ahead (2VLA) network topology is considered and up to 9x for a three-vehicle look ahead (3VLA) topology.

5G and beyond communications systems need to cope with a high degree of heterogeneity in terms of services and requirements. Specially, vehicle-to-everything (V2X) use cases require ultra-reliable and low latency communications (URLLC) under harsh channel conditions. To design an optimal waveform and coding scheme for such use cases is a key challenge. Therefore, new waveforms and coding techniques are need to be investigated. In this paper, we present a comparison of several waveform candidates (orthogonal frequency-division multiplexing (OFDM), discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM) and orthogonal time frequency space (OTFS)) and coding schemes (convolution, turbo, low-density priority-check (LDPC) and polar) under a common framework. We consider two metrics, i.e. maximum data rates and packet error rate, to evaluate their performance under various fading conditions. The simulation results show that OTFS outperforms all other waveforms in both frequency selective and doubly selective channels. Regarding the coding schemes, turbo codes outperforms all other coding schemes, even though difference with LDPC codes is marginal.

Realizing wireless mission-critical applications in industry, such as closed-loop control, necessitates ultra-reliable low latency communications (URLLC) to achieve error-free message transmission with hard real-time requirements. Recently, multi-connectivity (MC) has been introduced as a promising scheme to ensure URLLC in Industry 4.0. However, implementing MC in mobile industrial communications rises multiple technical challenges, such as avoiding degradation in reliability due to fading and the shadowing effect, and managing multiple links in parallel which increases signaling overhead dramatically. To deal with these challenges, this paper investigates the gains and limitations of implementing MC in industrial wireless communications. It studies conflicting optimization problems using MC based on different radio parameters. Also, a link management scheme is introduced for MC to reduce the signaling overhead based on different radio parameters. The simulation results demonstrate gains and limitations of using MC and the selected parameters (frequency reuse factor, number of users, and frequency band) on the reliability and the signaling overhead in industrial communications.

Physical layer abstraction (PLA) is commonly used in system level evaluations to speed up simulations. The accuracy of such evaluations highly depends on the modelling of PLA. Therefore, the main objective of this paper is to model and evaluate the performance of 5G and beyond (5GB) waveforms using PLA techniques. In the existing literature, PLA techniques were mainly used for orthogonal frequency division multiplexing (OFDM), which is commonly used to improve the performance under frequency selective fading. However, it suffers from high peak to average power ratio (PAPR). Therefore, to overcome this, DFT-spread- OFDM (DFT-s-OFDM) is used for uplink communication in the LTE/NR. In future use cases such as vehicle-to-everything (V2X), where high mobility is involved, inter-carrier interference becomes a bottleneck for above waveforms. Therefore, new waveforms are being proposed such as orthogonal time frequency space (OTFS). In the OTFS, data symbols are localized in delay-Doppler domain, and hence delay-Doppler channel impairments (due to mobility) can be easily compensated. To evaluate and compare the performance of above waveforms, we proposed waveforms specific PLA techniques. The proposed PLA techniques are validated though full PHY simulations, and used to compare the performance of different waveforms under frequency selective and doubly selective channels.

Ultra-reliable communications enable various advanced use cases, such as autonomous driving and safety critical applications. However, state-of-the-art vehicular communications technologies, such as IEEE 802.11p and LTE-V2X, cannot meet the reliability requirement of all time-critical use cases. Therefore, the next generation of these technologies are being developed to enhance vehicular support for ultra-reliable use cases. In this paper, the reliability of these upcoming vehicular communications technologies (i.e., IEEE 802.11bd and NR-V2X) is analyzed. Even though physical layer standardizations are not yet available, proposed candidate settings are used for investigations. We use Monte Carlo simulations to evaluate the physical layer performance of these technologies in various vehicle-to-vehicle (V2V) scenarios. High Doppler shifts in V2V scenarios is one of the main challenges to enable ultra-reliable communications. It is shown that NR-V2X can be expected to outperform IEEE 802.11bd in terms of reliability due to better handling of Doppler shifts. In case of IEEE 802.11bd, high Doppler shifts cause packet errors even at high signal-to-noise ratios (SNRs). Therefore, different measures to improve the performance of IEEE 802.11bd are discussed and evaluated.

An analytical method for computing the channel access delay budget between consecutive kinematic updates among a vehicle pair in a cooperative adaptive cruise control (CACC) enabled platoon under string stability conditions is presented. It is shown that even first-order approximations of the complex exponential delay term yield good numerical results. The method also indicates when communication in CACC is not required. The calculated channel access delay budget is used to obtain the semipersistent scheduling period for string stable CACC under LTE-V2X Mode 3 communication. Furthermore, an efficient semi-persistent scheduling algorithm capable of allocating heterogeneous periods and supporting multiple platoons is presented and validated with two examples.

Wireless system design on the physical layer is usually evaluated using comprehensive channel models. However, there is still a lack of publicly available stochastic channel models tailored to industrial use cases, which are recently considered more frequently. This paper presents the derivation of such a channel model for the 5 GHz ISM band and its parametrization. The frequency-selective behaviour is modeled by the Saleh-Valenzuela model. Based on a measurement campaign, the parameters of this model for a factory environment are determined and published the first time for the 5 GHz ISM band. Spatial correlation is modeled by the Kronecker model. The temporal variation of the channel is based on a theoretically derived Doppler spectrum assuming Laplacian distributed angle of arrivals. In addition to the description of the model components, key issues and common mistakes while constructing a channel model for industrial applications are discussed in order to advance the design and the deployment of future wireless industrial communications systems. The derived channel model is used in IEEE 802.11ax link layer simulations. It is shown that for industrial use cases specially tailored channel models are needed.

Vehicular communications and connected cars have an eminence potential to improve road safety and reduce the number of accidents by sharing information with their surrounding. The state of the art technologies to enable vehicular communications are IEEE 802.11p and LTE-V2X. A number of studies and field trials are carried out to evaluate their performance and suitability in various scenarios. On one hand 3GPP (3rd Generation Partnership Project) is working on the next generation V2X technology 5G NR-V2X to address new use cases and improve the performance. On the other hand, an IEEE 802.11 study group NGV (next generation V2X) is identifying new use cases and requirements to define a possible amendment IEEE 802.11bd. In this paper, we evaluate and compare the PHY layer performance of these upcoming technologies for vehicle-to-vehicle (V2V) communications. The purpose of this study is to identify which technology is more suitable for V2V communications. Our results show that NR-V2V is expected to outperform all other standards (even IEEE 802.11bd) in terms of reliability, range, latency and data rates. However, IEEE 802.11bd is expected to be more reliable with improved range and throughput compared to IEEE 802.11p.

Mission-critical applications in future vehicular networks require highly reliable wireless communications. Multi-connectivity is a potential solution to meet the desired quality-of-services of these applications. In this work, we consider a highway scenario with multi-connectivity where a vehicle can combine packets transmitted by multiple remote-radio-heads. For given inter-site- distances, number of links, and frequency reuse factors, we derive the expression for outage probability considering communication links affected by shadowing. We investigate the effect of network deployment parameters on the achievable outage reliability under different radio parameters. The simulation results show trade-offs between inter-site distance and control parameters (frequency reuse factor and number of links), as well as network and radio channel parameters (number of satisfied users, effect of shadowing, and path loss exponent) on communications reliability.

Multi-connectivity is a key enabler for realtime applications demanding high reliability such as connected vehicles. Employing macro-diversity with distributed transceivers has the advantage of mitigating large-scale losses such as shadowing, but may incur time offsets between packets, requiring the receiver to synchronize to each packet individually. Since packet detection is prerequisite for any downstream receiver processing, synchronization can become a bottleneck to achieving high reliability. In this paper, we propose a concept to improve receiver performance in macro-diversity multi-connectivity networks in case of time offsets between packets, for instance, due to loose synchronization of distributed transmitters. By buffering the inputs of parallel receiver paths and allowing for iterative synchronization, successfully detected packets can serve as extended correlation sequence to detect previously undetected packets which thereby become available to diversity combining. Taking link-level simulations of IEEE 802.11 (WLAN) as an example, we demonstrate the efficacy of such Joint Synchronization (JS) and provide first numerical results. We see an SNR gain of about 1 dB in the mid-SNR range, which is equivalent to a packet error rate reduction by an order of magnitude for four-fold diversity. With power consumption in mind, we consider the trade-off between implementation complexity and the gain of JS. We conclude that JS is a viable backwards-compatible approach to improve diversity combining of delayed packets in multi-connectivity networks.

Emerging wireless communication use-cases demand ultra-reliable and low-latency communications. In order to meet these requirements, multi-connectivity (MC) approaches are being considered as a possible solution. To enable multi-connectivity with efficient use of resources, link adaptation requires not only to adopt modulation and coding scheme but also the number of links. This can be achieved by using an effective link quality metric (LQM) and mapping it to packet error rate or throughput, a process known as physical layer abstraction (PLA). In this paper, a new PLA method enhanced received bit information rate (eRBIR) is presented for OFDM based MC networks. The performance of proposed method is evaluated and compared against existing methods such as exponential effective SINR mapping (EESM) and received bit information rate (RBIR). Simulation results show that the proposed method enable an accurate and reliable link adaptation as compared to state of the art PLAs. Finally, the application of PLAs, to adapt the MCS in varying channel conditions is illustrated to ensure a certain target quality of service.

A study on feasibility regarding enabling cooperative adaptive cruise control (CACC) through LTE-V2X Semi-Persistent Scheduling (SPS) is considered. A linear time invariant (LTI) CACC model, previously presented in [1] is revisited. The effect of communication delay on the platoon stability, i.e. string stability, in relation to the CACC parameters is then explained. We study the effects on the SPS period limitations imposed by the LTE-V2X standard by modelling it as a communication delay. The performance of CACC under such considerations is presented and further improved by considering multiple SPS parallel sessions. We show that by following simple conditions on the configuration of multiple SPS parallel sessions, the CACC performance can be further improved. Moreover, by considering multiple SPS parallel sessions following these conditions, the SPS period set is refined in granularity. A refined granularity in the SPS period leads to less redundant transmissions from a stability perspective, which effectively translates to a higher efficiency in radio resource usage when compared to single session SPS.

Locating active users/transmitters and predicting interference patterns in real-time is a key challenge in next generation radio networks to enable robust wireless communication and highly dynamic radio resource allocation. We consider a network of collaborating spectrum sensing units (SUs) and propose a two-phase approach for localization and tracking of transmitters using received signal strength at SUs. The first phase involves estimation of locations of multiple transmitters using compressed sensing based method. In the second phase, the location estimates are improved and velocities of mobile transmitters are estimated using extended Kalman filter. The proposed approach is evaluated through simulations considering an industrial channel model with correlated shadowing. We investigate the effect of SU placement, transmitter density, mobility and fading on the localization performance.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industrie 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

The fifth generation (5G) of mobile communication will enable new use-cases such as self driving cars, smart automation and mission critical applications, which require ultra-reliable communications. Multi-Connectivity (MC) is a promising approach to achieve high reliability in wireless networks. In order to enable MC and efficiently utilize radio resources, dynamic link adaptation is required for choosing appropriate modulation schemes and number of links. This can be achieved by using an effective link quality metric such as effective signal-to-noise ratio (SNR) and mapping it to the packet error rate, a process referred to as physical layer abstraction (PLA). In this paper, we investigate and compare the performance of existing PLA methods especially exponential effective SNR mapping (EESM) and received bit information rate (RBIR), for OFDM-based MC systems. Furthermore, we propose a new robust PLA method, called enhanced EESM (eEESM). The eEESM minimizes the efforts of optimizing tuning parameter by fitting the variations in tuning parameter to known curves as a function of channel and diversity order. Simulation results show that eEESM outperforms the state of the art PLAs for different channel conditions, modulation and diversity orders.

Exploiting the full potential of automated driving systems requires reliable wireless communication enabling network connectivity and cooperation among vehicles. Multiple V2X technologies are addressing the requirements of connected autonomous driving applications. Recent investigations have shown that none of the technologies is flexible and reliable enough to serve the diverse requirements in terms of delay, reliability and throughput under the various circumstances observed by vehicles. Hybrid V2X communications enables the coordination of multiple communication technologies to efficiently adapt to the time-varying channel and road traffic conditions. Further it allows to increase reliability and throughput of transmissions by combining multiple RATs in parallel. This work gives an overview of the potential, challenges and main design aspects of hybrid V2X communications considering the latest technological developments.

Enhancing the connectivity reliability is one of the most challenging requirements for the design of future wireless communications systems. The scope of this paper is to leverage the existing tool set of reliability theory for enabling reliable communication in wireless systems. Definitions, concepts, and methods of reliability theory are applied and extended to wireless communications networks, which are modeled as a repairable system. The steady-state and transient system behaviour are considered. Two new key performance indicators (KPIs) for the reliability analysis of wireless communications systems are introduced, namely mean time to first failure (MTTFF) and interval reliability (IR), and a closed form expression is derived for the MTTFF. By evaluating an exemplary scenario, the trade-off between availability, reliability and throughput is discussed.