3GPP standardized conditional handover (CHO) in Release 17 as a mechanism to enhance mobility robustness in non-terrestrial networks (NTNs). In addition to the traditional terrestrial NR triggering events (e.g., Event A3), two new triggers, location-based and time-based, were introduced. These enhancements address the limitations of relying solely on signal strength, which can be insufficient for distinguishing between satellites at similar altitudes due to comparable path-loss characteristics. In this paper, a time-to-exit (TTE) criterion, integrated with a location-based trigger, is proposed to select candidate cells for which the network estimates how long the D1 condition will remain valid after handover. A candidate cell is admitted if (i) it has the longest D1-based TTE and (ii) its signal quality exceeds a configured threshold; otherwise, it is discarded, with ties broken based on signal strength. This design arms only targets that are both strong enough and stable long enough to finish the handover, preventing late or unstable executions. Simulation results show that the proposed TTE-aware CHO significantly reduces handover failures and ping-pong events, and increases post-handover time-of-stay, compared with a baseline location-based CHO.

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design andopennessparadigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network.We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.

The space-air-ground integrated network (SAGIN) represents a major breakthrough in the advancement of Beyond 5G/6G, seamlessly combining terrestrial network (TN) and nonterrestrial networks (NTN) into a unified multi-layered system by creating a more cohesive and efficient communication infrastructure. However, the high mobility of NTN platforms creates significant challenges in managing user transitions across these interconnected layers. This article offers a concise overview of the unique demands of mobility management (MM) in SAGIN and the architectural developments required to meet the needs of various service categories, including enhanced mobile broadband (eMBB), massive machine type Communications (mMTC), and ultra reliable low latency Communications (URLLC). Additionally, the article details specific strategies for vertical handovers in SAGIN, as described in the literature, with a focus on the roles of software defined network (SDN), network function virtualization (NFV), and network slicing (NS).

Wireless time synchronization of mobile devices is a key enabler for numerous Industry 4.0 applications, such as coordinated and synchronized tasks or the generation of high-precision timestamps for machine learning or artificial intelligence algorithms. Traditional wireline clock synchronization protocols, however, cannot achieve the performance in wireless environments without significant modifications. To address this challenge, we make use of the Reference Broadcast Infrastructure Synchronization protocol, which leverages the broadcast nature of wireless communications and remains both non-invasive and standard-compliant. We implement and validate this protocol on a low-cost testbed using ESP32 modules and a commercial Wi-Fi access point. To support further research and development, we release our implementation as open-source software under the GNU General Public License Version 3 license via the OpenWifiSync project on GitHub1Our results demonstrate that synchronization accuracies within ±30 microseconds are achievable using energy-efficient and affordable hardware, making this approach suitable for a wide range of use cases.

Mobile communication technologies are considered fundamental to the implementation of Industry 4.0, leading to a growing deployment of non-public networks. To effectively manage and monitor these networks, digital twins (DTs) have emerged as a promising solution. The Asset Administration Shell (AAS), as a standardized framework for DTs, offers a structured approach to network monitoring and management. While previous research has demonstrated the feasibility of using AAS in simulated environments, real-world validation remains limited. In this work, we present a technical case study that implements a testbed combining a 5G system with the AAS to monitor license requirements in a non-public network. We evaluate the system’s performance by analyzing responsiveness and demonstrate that the AAS can effectively support real-time monitoring tasks. This study bridges the gap between simulation and practical deployment, highlighting the potential of the AAS for managing 5G network assets in Industry 4.0 contexts.

This paper proposes a dynamic epidemic model for successive opinion diffusion in social networks, extending the SHIMR model. It incorporates dynamic decision-making influenced by social distances and captures accumulative opinion diffusion caused by interrelated rumors. The model reflects the impact of rumor spread on social network structures. Simulations validate its effectiveness in explaining phenomena like the echo chamber effect and provide insights into opinion diffusion dynamics, with implications for understanding social polarization and network evolution.

Cell-free (CF) massive MIMO offers uniform service via distributed access points (APs), which impose high deployment costs. A novel design called hierarchical cell-free (HCF) addresses this problem by replacing some APs with a central base station, thereby lowering the costs of fronthaul network (wireless sites and fiber cables) while preserving performance. To identify the optimal uplink configuration in HCF massive MIMO, this paper provides the first comprehensive analysis, benchmarking it against cellular and CF systems. We develop a unified analytical framework for spectral efficiency that supports arbitrary combining schemes and introduce a novel hierarchical combining approach tailored to HCF’s two-tier architecture. Through analysis and evaluation of user fairness, system capacity, fronthaul requirements, and computational complexity, this paper identifies that HCF using centralized zero-forcing combining achieves the optimal balance between performance and cost-efficiency.

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

This paper presents a comprehensive Queuing Theory-based model of a Publish/Subscribe Internet of Things (IoT) communication system. Using a Markovian ($\mathrm{M} / \mathrm{M} / 1$) queuing process, we model, analyze, and optimize the message delivery of a Middleware Message Queuing Protocol (MMQP)-broker. We derive key performance metrics such as average waiting time, system utilization, and the effects of multi-threading on performance. Our model aims to optimize the throughput and the efficiency of the system by determining the optimal service rate of the broker in relation to the overall arrival rate of messages. Through experimental evaluation, we demonstrate the model's accuracy and its relevance in providing valuable insights for improving IoT communication.

Telecom base stations rely on a continuous and stable power supply for efficient operation. Batteries are the preferred choice to provide backup power during outages. With recent advances in the Internet of Things (IoT) and communication networks, telecom base stations have become complicated cyber-physical systems. However, the sharing of control signals and security-critical information over communication channels makes battery energy storage systems (BESS) vulnerable to cyberattacks. To solve this vulnerability, this study explores the role of digital twins (DT) in detecting and mitigating cyberattacks. To this end, we propose a data-driven battery digital twin architecture to characterize battery performance over a wide range of operating temperatures and state-of-charge (SOC) levels. The digital twin leverages a feedforward neural network to estimate SOC from current, voltage, and temperature measurements. Stealthy attacks are formulated using a Soft Actor-Critic (SAC) reinforcement learning agent to corrupt current and voltage sensors, which affect the estimation of SOC. The attacks are detected by comparing the SOC estimated by the data-driven model and the Coulomb counting method.

In electric vehicles and stationary storage applications, the increasing demand for Lithium-ion battery (LIB) systems highlights the need for precise state-of-health (SOH) estimation. Traditional approaches often rely on resource-heavy computations or impractical setups, such as controlled charging or high-frequency sampling, misaligned with the constraints of large-scale battery management systems (BMS) that operate with limited computational power and low-frequency data collected over seconds. This paper proposes a computationally efficient algorithm, using a nonlinear input-output neural network (NIONN) for state-of-charge (SOC) estimation and internal resistance derived from voltage drops during current transients for SOH estimation. Furthermore, temperature compensation through polynomial fitting enhances the accuracy of the estimate. Validated on the NASA battery aging dataset, the proposed approach achieves an average error below 3%. With only 41 arithmetic operations and adaptability to diverse conditions. The proposed method with real-time SOH monitoring for electric vehicles and large-scale energy storage offers a practical, robust solution tailored to BMS limitations.

Networks of honeypots, similar to network telescopes, enable the monitoring of Internet threat activity. Honeypots can collect service-specific information about threat behavior and capabilities, depending on their configuration. This demonstration paper presents SweetsPot, a distributed honeypot data collection system. SweetsPot aims to facilitate threat research to model attacker behavior and characterize emerging threats. Additionally, SweetsPot supports live-streaming of event data, enabling rapid analysis, visualization, and timely threat response.

To improve business efficiency and minimize costs, Artificial Intelligence (AI) practitioners have adopted a shift from formulating models from scratch towards sharing pretrained models. The pretrained models are then aggregated into a global model with higher generalization capabilities, which is afterwards distributed to the client devices. This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model. However, in the case of communication systems, the approach grapples with challenges arising from the existential diversity in device profiles. The multiplicity in profiles motivates our conceptual assessment of a metaheuristic algorithm (FedAvgen), which relates each pretrained model with its weight space as metadata, to a phenotype and genotype, respectively. This parent-child genetic evolution characterizes the global averaging step in federated learning. We then compare the results of our approach to two widely adopted baseline federated learning algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD).

Unauthorized sensing activities pose an increasing threat to individual privacy, yet effective countermeasures remain underdeveloped. This paper presents a novel methodology to characterize and counter such unauthorized surveillance. We model pedestrian trajectories as a random process and leverage the Cramér-Rao bound (CRB) to evaluate sensing performance, interpreting it as sampling error within this random process. Through simulation, we verify our method’s accuracy in monitoring unauthorized sensing activities in urban environments and validate the effectiveness of our proposed mitigation strategies.

Integrating terrestrial (TN) and non-terrestrial (NTN) networks into 3D user-centric cell-free massive multiple-input multiple-output (UC-CFmMIMO) architectures poses significant challenges, including extreme Doppler shifts, spatially diverse delay spreads and dynamic topology changes. This paper provides the first comprehensive comparison of four waveforms — Orthogonal Time-Frequency-Space (OTFS), Affine Frequency-Division-Multiplexing (AFDM), Orthogonal-Delay-Doppler-Multiplexing (ODDM) and Orthogonal-Frequency-Division-Multiplexing (OFDM) — across three deployment scenarios: ground-based UC-CFmMIMO, NTN-dominant UC-CFmMIMO and 3D UC-CFmMIMO. By integrating 3GPP TN (TR 38.901) and NTN (TR 38.811) channel models within a unified framework, we demonstrate the superiority of AFDM in NTN scenarios and the robustness of OTFS in unified space-air-ground networks. ODDM’s staggered pulse shaping reduces ground-level intersymbol interference and OFDM remains optimal for low-mobility deployments. We discuss the need for adaptive waveform switching to balance Doppler/delay robustness, latency and complexity, offering actionable guidelines for 6G system designers.

This paper seeks to determine the most efficient uplink technique for cell-free massive MIMO systems. Despite offering great advances, existing works suffer from fragmented methodologies and inconsistent assumptions (e.g., single- vs. multi-antenna access points, ideal vs. spatially correlated channels). To address these limitations, we: (1) establish a unified analytical framework compatible with centralized/distributed processing and diverse combining schemes; (2) develop a universal optimization strategy for max-min power control; and (3) conduct a holistic study among four critical metrics: worst-case user spectral efficiency (fairness), system capacity, fronthaul signaling, and computational complexity. Through analyses and evaluation, this work ultimately identifies the optimal uplink technique for practical cell-free deployments.

With the evolution of Vehicle-to-Everything (V2X) technology and increased deployment of 5G networks and edge computing, Predictive Quality of Service (PQoS) is seen as an enabler for resilient and adaptive V2X communication systems. PQoS incorporates data-driven techniques, such as Machine Learning (ML), to forecast/predict Key Performing Indicators (KPIs) such as throughput, latency, etc. In this paper, we aim to predict downlink throughput in an urban environment using the Berlin V2X cellular dataset. We select features from the ego and lead vehicles to train different ML models to help improve the predicted throughput for the ego vehicle. We identify these features based on an indepth exploratory data analysis. Results show an improvement in model performance when adding features from the lead vehicle. Moreover, we show that the improvement in model performance is model-agnostic.

Robotics and teleoperation have become pivotal in modern industrial settings, offering robust solutions for enhancing operational flexibility and safety. By leveraging telerobotics, the presented real-world test environment investigates the intersection of resilient wireless communications and collaborative robotics, emphasizing the necessity of reliable, low-latency data transmission for real-time control and feedback. The test environment features two collaborative robots (cobots), a high-precision Motion Capture (MoCap) system for position tracking and localization, and a Digital Twin (DT) for collision prevention and visualization. Based on Robot Operating System 2 (ROS 2) and Docker, the system is highly customizable, providing a foundation for comprehensive digital twinning, Artificial Intelligence (AI) training, and evaluation of flexible multipath networking. The inherent demands of robotic teleoperation make it particularly suited for exploring resilience, security, and adaptability of wireless communication technologies and advanced (multipath) protocols in real-time and safety-critical applications. Integration and extensive testing of 5 G and emerging 6 G approaches, such as a decentralized Ultra-Reliable Low Latency Communication (dURLLC) system, demonstrate the environment’s capability to evaluate advanced wireless communication strategies under realworld conditions.

Telerobotics has the potential to transform robotic operations across various domains, including industrial automation, medical robotics, and the chemical sector, where accurate and timely interventions are paramount. Wireless communication is pivotal in expanding the practical applications of telerobotic systems, enabling untethered, and flexible operation. To achieve precise teleoperation, low-latency motion controllers are required that enable seamless and intuitive human-machine interaction. This work presents the design and implementation of a wireless motion controller device for accurate telerobotic operation within the Robot Operating System (ROS) framework. By combining optical-active position tracking using a high-precision Motion Capture (MoCap) system and low-latency wireless communication, the setup facilitates a responsive and interactive human-robot interface. Experimental evaluations of the integrated system demonstrate accurate teleoperation control of a 7 -axis collaborative robot (cobot). Additionally the impact of the different system components on the end-to-end latency is analyzed with robotic actuation and processing time of the MoCap system identified as the primary contributors. The results confirm the viability of wireless teleoperation and highlight opportunities for future research and optimization in telerobotic systems.

Cryptographic agility is becoming increasingly essential in mobile networks due to the rapid evolution of network architectures, the growing complexity of security threats, and the anticipated arrival of quantum computing. As mobile networks transition from 5G to 6G, ensuring the adaptability of cryptographic systems to emerging standards and threats is paramount. This paper introduces a novel Cryptographic Management Function (CMF), a centralized software-defined cryptography framework designed to achieve seamless cryptographic agility in mobile networks. The CMF is introduced to enable dynamic, seamless updates to cryptographic algorithms and protocols without disrupting ongoing operations, making it ideal for environments with varying device capabilities and evolving security requirements. It operates by decoupling cryptographic functions from the other network components, centralizing the enforcement of cryptographic policies, and supporting hybrid cryptographic schemes, including Post-Quantum Cryptography (PQC).

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks.

In communication, network traffic prediction is the process of forecasting future network traffic based on historical data. This information can be used to improve network quality, optimize resource allocation, and prevent attacks. With the advent of Federated Learning (FL), the ability to harness insights from distributed data sources while preserving data privacy has revolutionized this field. However, the presence of malicious or unreliable data sources can impact the performance and reliability of the collaboratively trained Machine Learning (ML) models. One such situation is the presence of Distributed Denial of Service (DDoS) traffic data in FL training. In this work, the analysis and mitigation of clients with DDoS traffic data in FL for network traffic prediction are performed. The negative impact of such data on the performance of the global model is presented. Several mitigation techniques, such as client selection, data filtering, and model aggregation, are discussed. The results indicate that the Median aggregation technique is able to mitigate the effect of DDoS data. It is also suited to real-time scenarios as there is no need for client selection or data filtering, making it fast.

Precise clock synchronization is an important requirement for distributed and networked industrial use cases. As more and more use cases contain mobile devices, clock synchronization has to be performed over wireless communication links. As wireless communication links are currently not as deterministic and reliable as wireline communication systems, novel clock synchronization algorithms have to be investigated. Here, the so-called Reference Broadcast Infrastructure Synchronization Protocol is a well suited solution as it brings up multiple advantages. Most important is the non-invasiveness, meaning it can be used with commercially available components. As a considerably high amount of factories use Wi-Fi as wireless communication system for their mobile use cases, the aforementioned protocol is implemented using Wi-Fi. Furthermore, the usage of Open-Source Software can be seen as driver for highly efficient and interoperable applications. Consequently, the implementation is accessible under the GNU General Public License on GitHub under the designation OpenWiFiSync. Besides the details on concept, its implementation, and the used testbed, first results are outlined within this paper. Additionally, future work and the estimated timeline are presented.

Cell-free massive multi-input multi-output (CFmMIMO) offers uniform service quality through distributed access points (APs), yet unresolved issues remain. This paper proposes a heterogeneous system design that goes beyond the original CFmMIMO architecture by exploiting the synergy of a base station (BS) and distributed APs. Users are categorized as near users (NUs) and far users (FUs) depending on their proximity to the BS. The BS serves the NUs, while the APs cater to the FUs. Through activating only the closest AP of each FU, the use of downlink pilots is enabled, thereby enhancing performance. This heterogeneous design outperforms other homogeneous massive MIMO configurations, demonstrating superior sum capacity while maintaining comparable user-experienced rates. Moreover, it lowers the costs associated with AP installations and reduces signaling overhead for the fronthaul network.

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.

Cell-free massive multi-input multi-output (MIMO) has recently gained much attention for its potential in shaping the landscape of sixth-generation (6G) wireless systems. This paper proposes a hierarchical network architecture tailored for cell-free massive MIMO, seamlessly integrating co-located and distributed antennas. A central base station (CBS), equipped with an antenna array, positions itself near the center of the coverage area, complemented by distributed access points spanning the periphery. The proposed architecture remarkably outperforms conventional cell-free networks, demonstrating superior sum throughput while maintaining a comparable worst-case peruser spectral efficiency. Meanwhile, the implementation cost associated with the fronthaul network is substantially diminished.

3D Network convergence across ground, air and space wireless communications holds immense promise, offering disruptive use cases and addressing the challenges of ubiquitous connectivity. Next-generation mobile networks may need to transcend the current ground-focused paradigm and fully embracing aerial and spaceborne communications becomes essential. The unified 3D network requires adaptations at the radio as well as at the network level. This paper presents an analysis of the dynamic placement of Radio Access Network (RAN) functions, exploring the specific challenges associated with the links between Distributed Units (DUs) and Radio Units (RUs), as well as between DUs and Centralized Units (CUs), within the context of 3D unified network. The dynamic placement of RAN functions introduces significant complexities, including latency management, synchronization, resource allocation, etc. The DU-RU link faces challenges such as high bandwidth requirements, real-time processing needs and efficient beamforming techniques to maintain robust connectivity. Meanwhile, the DU-CU link must address issues related to mobility, handover processes and maintaining low-latency communication despite the physical separation of network components. By analyzing these challenges, this paper provides insights into the necessary technological advancements and strategic approaches required to optimize the dynamic placement of RAN functions, ensuring the seamless operation and high performance of 6G networks.

Cell-free massive multi-input multi-output (MIMO) has recently attracted much attention, attributed to its potential to deliver uniform service quality. However, the adoption of a cell-free architecture raises concerns about the high implementation costs associated with deploying numerous distributed access points (APs) and the need for fronthaul network installation. To ensure the sustainability of next-generation wireless networks, it is crucial to improve cost-effectiveness, alongside achieving high performance. To address this, we conduct a cost analysis of cellfree massive MIMO and build a unified model with varying numbers of antennas per AP. Our objective is to explore whether employing multi-antenna APs could reduce system costs while maintaining performance. The analysis and evaluation result in the identification of a cost-effective design for cell-free massive MIMO, providing valuable insights for practical implementation.

Wireless communication has profoundly transformed the way we experience the world. For instance, at most events, attendees commonly utilize their smartphones to document and share their experiences. This shift in user behavior largely stems from the cellular network’s capacity for communication. However, as networks become increasingly sophisticated, new opportunities arise to leverage the network for services beyond mere communication, collectively termed Beyond Communication Services (BCS). These services encompass joint communications and sensing, network as a service, and distributed computing. This paper presents examples of BCS and identifies the enablers necessary to facilitate their realization in sixth generation (6 G). These enablers encompass exposing data and network capabilities, optimizing protocols and procedures for BCS, optimizing compute offloading protocols and signalling, and employing application and device-driven optimization strategies.

Industrial Ethernet networks are well-established communication systems in industrial production facilities. They are used in particular in applications with high demands on real-time capability and transmission reliability. In the context of applications with mobility requirements, such as mobile robots or rotating machine parts, however, they reach their practicable limits. In these cases, wireless communication systems are necessary. In addition to enabling the aforementioned applications, they promise further advantages, such as cost savings through simplified installation. However, the same requirements are placed on wireless systems as on their wired counterparts. This paper structures these requirements’ implications on industrial communication systems by deriving four mandatory properties that need to be fulfilled by any communication system for industrial applications. Current commercially available technologies are reviewed with respect to the mandatory properties. Addressing their shortcomings, an overview of current research approaches aiming to improve industrial wireless systems in the automation applications is given.

Long Range Wide Area Network (LoRaWAN) is a promising technology for enabling 6G massive Machine Type Communication (mMTC). However, it lacks in terms of key management schemes (e.g. key updates). We therefore propose the usage of Physical Layer Security (PLS) based Secret Key Generation (SKG) within LoRaWANs in this work in order to establish efficient session key management. Further, we introduce the utilization of Autoencoders (AEs) for Reciprocity Enhancement (RE) in order to optimize the SKG performance and study the impact of multi-bit quantization. To assess the effectiveness of our proposed approach, we conducted experiments using an experimental testbed. The obtained results highlight the potential benefits of AE based key management and multi-bit quantization for LoRaWAN in terms of improved Bit Disagreement Rate (BDR) and Key Generation Rate (KGR) utilizing Received Signal Strength Indicator (RSSI) measurements and respective preprocessing.

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low computational complexity.

Realistic vehicle traffic simulations have been a subject of great interest in many fields, particularly in the Intelligent Transport Systems (ITS), as they can help improve transportation efficiency, safety, and sustainability. Simulation of Urban MObility (SUMO) is a powerful tool for vehicular traffic simulations; however, traditional methods of generating traffic demand are usually based on random trips or the use of real-world datasets, which can be difficult to obtain. More advanced approaches create algorithms to generate trips based on multimodal activities. However, these approaches require either a large amount of computational resources or a great deal of effort in deploying measurement hardware. Therefore, we proposed a new scheme to generate large-scale traffic demands, where a real-world dataset is fed into an ML model to predict traffic flows. We then calibrate the traffic flows to match the predicted ones, resulting in realistic traffic flows for urban scenarios. The approach is highly computationally efficient and can be applied to any city scenario. Furthermore, the training data set and the ML model can be updated for more precise predictions. In the end, we create the whole map of Berlin and generate around 1.2 million vehicles as a 24-hour trial, and compared the predicted model with the real-world measured dataset. As a result, more than 85 percent of the monitored lanes show a mismatch of less than 5 vehicles in 24 hours, and a general traffic pattern of Berlin is revealed and matched.

For Visible Light Communication (VLC) between vehicles, maintaining Line of Sight (LoS) is a fundamental requirement. Especially when a sequentially communicating convoy (platoon) is turning at an intersection within a city, the direct line-of-sight connection can be interrupted, leading to a disruption of VLC communication. Particularly in urban locations with obstructed view, it is crucial for future autonomous driving to convey critical information, such as crossing pedestrians and other obstacles like partial road closures due to construction sites, to following vehicles. Within the development of Sixth-Generation (6G) wireless networks, the use of Reconfigurable Intelligent Surfaces (RIS) is proposed as one option to address the LoS issue. In this work, a conceivable use case is investigated to examine through ray-optical simulations the requirements for the update rate of such systems when RIS elements are reconfigured discontinuously. Additionally, the influence of the size of the device surface area on the resulting signal strength in the described traffic situation is provided. The findings suggest that delays in updating array elements significantly impact situations where the transmitter is far from the intersection, offering crucial insights for autonomous driving system development.

This paper presents an innovative approach for Channel State Information (CSI) prediction using Long-Short Term Memory (LSTM) networks in the frequency domain, addressing selective fading in mobile scenarios. Unlike models that assume perfect CSI, our method relies on realistic, estimated CSI and introduces strategies to mitigate estimation errors. We propose an adaptive prediction technique leveraging LSTM for precise 3D Downlink (DL) transmission control, alternating between previously predicted and high-accuracy estimates. Our system supports both open-loop and closed-loop predictions, enhancing resource allocation and signal reliability in Non-terrestrial Network (NTN). Experimental results demonstrate significant improvements in prediction accuracy, meeting the demands of complex Geometry-based Stochastic Channel Modeling (GSCM) over simpler 2D stochastic channel models with predefined temporal correlation, in compliance with the 3GPP 38.901 standard.

Correctly predicting the channel is crucial for tackling the issue of channel aging in mobile communications, where channels change quickly over time. Current methods for predicting channels mostly rely on processing signals in sequence, meaning the channel for the upcoming frame is predicted one at a time. Consequently, as frames progress, the precision of these predictions tends to decline because errors tend to accumulate during the sequential prediction process. For preliminary research aimed at optimizing unmanned aerial vehicle (UAV) communication channels by integrating intelligent channel predictor to the UAV communication system, this paper evaluates the performance of a channel predictor based on a Transformer mechanism in comparison with traditional serial data processing methods for Rayleigh fading channels in non-line-of-sight cellular communication scenarios. The study explores the effectiveness of machine learning models in predicting channel state Information. The Transformer model, recognized for its ability to handle sequential data and mitigate prediction error propagation, is analyzed for its computational efficiency and predictive accuracy against gated recurrent unit (GRU)-based and Kalman filter-based models. The study highlights the Transformer’s superior performance in long-term prediction due to its attention mechanism, offering a promising approach for real-time communication applications.

Recently, intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) and terahertz (THz) communications are considered in the wireless community. This paper aims to design a beam-based multiple-access strategy for this new paradigm. Its key idea is to make use of multiple sub-arrays over a hybrid digital-analog array to form independent beams, each of which is steered towards the desired direction to mitigate inter-user interference and suppress unwanted signal reflection. The proposed scheme combines the advantages of both orthogonal multiple access (i.e., no inter-user interference) and non-orthogonal multiple access (i.e., full time-frequency resource use). Consequently, it can substantially boost the system capacity, as verified by Monte-Carlo simulations.

Unmanned aerial vehicle (UAV) is becoming increasingly important in modern civilian and military applications. However, its novel use cases is bottlenecked by conventional satellite and terrestrial localization technologies, and calling for complementary solutions. Multi-UAV mutual positioning can be a potential answer, but its accuracy and security are challenged by inaccurate and/or malicious measurements. This paper proposes a novel, robust, and secure approach to address these issues.

With the increasing level of connectivity to the internet, especially wireless system, network traffic monitoring has become an active field of research. Network traffic analysis has many applications, including in resource allocation or management. However, the growing concern regarding privacy makes it difficult for different entities to share network traffic information. Federated learning and homomorphic encryption have been proposed in previous research as a solution to a secure collaborative analysis, but the practicality as well as a thorough evaluation of the approach have to be explored. This article aims to provide a practical study that could be implemented in real life. Aspects like secure multi-party computation are investigated, which allows organization to use different private keys. In addition, data used for the evaluation are generated in totally different environments. These new features are considered since in practice, companies will not use the same private keys and also network traffic data often come from different type of companies. A detailed evaluation of the approach is also presented.

This paper focuses on the design of transmission methods and reflection optimization for a wireless system assisted by a single or multiple reconfigurable intelligent surfaces (RISs). The existing techniques are either too complex to implement in practical systems or too inefficient to achieve high performance. To overcome the shortcomings of the existing schemes, we propose a simple but efficient approach based on opportunistic reflection and non-orthogonal transmission. The key idea is opportunistically selecting the best user that can reap the maximal gain from the optimally reflected signals via RIS. That is to say, only the channel state information of the best user is used for RIS reflection optimization, which can in turn lower complexity substantially. In addition, the second user is selected to superpose its signal on that of the primary user, where the benefits of non-orthogonal transmission, i.e., high system capacity and improved user fairness, are obtained. Additionally, a simplified variant exploiting random phase shifts is proposed to avoid the high overhead of RIS channel estimation.

Enabled by the emerging industrial agent (IA) technology, swarm intelligence (SI) is envisaged to play an important role in future industrial Internet of Things (IIoT) that is shaped by Sixth Generation (6G) mobile communications and digital twin (DT). However, its fragility against data injection attack may halt it from practical deployment. In this paper we propose an efficient trust approach to address this security concern for SI.

In order to enable all types of use cases that are proposed within 5G and B5G factory scenarios, an accurate and precise time synchronization is required. Here, the so-called RBIS protocol is a possible solution since it was specifically designed for infrastructure-based wireless communications. While the use of RBIS protocol has a lot of benfits, a strong weakness is its restriction to the range of a single gNB. Thus, a concept for the extension of the RBIS protocol is proposed within this work, as well as a mapping to 5G and B5G systems.In order to prove the suitability of this approach, a simulation that is based on a reference scenario that was specified by NGMN, 3GPP, and IEEE, is carried out. Moreover, as the performance is dependent on the number of users and their mobility, either a tradeoff between minimum number of UEs and performance has to be done, or the usage of dedicated synchronization UEs that are able to guarantee the time synchronization quality.

This paper proposes a modular and scalable approach for implementing an Internet of Things (IoT) broker using the Entity-Component-System (ECS) architecture. The broker is designed to handle numerous sessions and topics without performance degradation, utilizing a publish/subscribe messaging model similar to Message Queue Telemetry Transport (MQTT). The approach simplifies the implementation of privacy, security, and trust measures in data exchange and offers a notable improvement in energy efficiency over a conventional open-source implementation.

Biometric systems to measure inherent human characteristics in combination with methods of Artificial Intelligence (AI), can result in innovative applications, especially in the fields of remote access systems and human-machine interfaces. One of these involves the use of Electroencephalography (EEG) equipment to monitor and visualize brain activity. Besides traditional EEG applications, such as medical aspects and neuroscience purposes, it can be used to distinguish individuals based on unique and characteristic signals. The ability to recognize a person without their physical attendance is the first step for future remote access to Brain-Computer Interface (BCI) applications. Therefore, in this work, a novel Deep Neural Network (DNN), based on inception modules with a kernel-adapted modification, is developed. Inception-based DNNs recently gained much interest for Time Series Classification (TSC), since they provide comparable performance to very deep Convolutional Neural Networks (CNN) with much less computational complexity. To the best of knowledge, this is the first inception-based DNN developed for authentication purposes using EEG. To validate the proposed network's performance it is compared to three other inception-based DNNs, namely: InceptionTime, EEG-Inception, and EEG-ITNet. Using an anonymized dataset of 22 participants with a length of 1 s per epoch, the proposed network achieves an average recall and precision of 99.1 % and 99.4 %, respectively, outperforming the other DNNs, with EEG-Inception achieving the best results with an average recall and precision of 96.8 % and 97.1 %, respectively.

Within the development of Beyond 5G and Sixth Generation (6G) wireless systems new communication technologies keep pushing toward high frequencies up to visible light to reduce interference and increase data rates. Many different modulation methods are available, and it is not clear yet which technique will be represented most frequently in future Visible Light Communication (VLC). As more and more demanding tasks are left to Artificial Intelligence and machines in the industry as well as in healthcare, it is essential to add further layers of resilience and security to off-the-shelf state-of-the-art VLC systems, regardless of the underlying modulation method. Therefore, in this work, two methods to modulate the physical beam path of a narrow laser beam utilizing piezoelectric mirror actuators, which can be implemented at a later stage onto existing VLC systems are proposed. While piezoelectric systems are established for specific tasks like beam stabilization in laser physics, the application in a modulation scheme for data transmission is new. The proposed concept is evaluated in the scope of upcoming wireless solutions including challenges and performance predictions. Piezoelectric beam path modulation is identified as a flexible method to enhance existing VLC systems with an additional layer of resilience and, when paired with appropriate intrusion detection mechanisms, improving security likewise.

Diminishment of dead zones, global coverage of the Earth and access to the Internet anywhere and anytime are expected with the development of the next-generation wireless networks. The latest technologies such as cloud computing, artificial intelligence and machine learning, augmented and virtual reality, the Internet of Things (IoT) and billions of connected devices are revolutionising the expansion of capabilities of wireless communication systems. 6G technology promises a reliable high-speed and almost instantaneous connection for any subscriber, and it allows us to create a personalised environment for each user using smart devices and artificial intelligence. The integration of airborne and spaceborne platforms into 6G Terrestrial Network (TN) will provide coverage across land, sea and sky, and connection whether users walk, drive or fly and supply services even if one of the services cannot be provided. In this study, we analyze the concept of developing a three-dimensional coverage of 6G communication services, where a Non-Terrestrial Network (NTN) is listed as a new network segment. The integration of NTN will lead to new research questions and an extension of 6G NTN application scenarios. Possible application scenarios, challenges and key technologies of the unified 6G NTN communication link are considered. In particular, we discuss (i) the effects of atmosphere, altitude, and speed of NTN platforms on signal quality; (ii) the architecture design supporting user-centric scalable Cell-Free Massive Multiple-Input Multiple-Output (CFmMIMO) for NTN, with the proposal of a 3D model of scalable user-centric cell-free massive MIMO with multiple interconnected Central Processing Units (CPUs); and (iii) briefly discuss the potential use of Reconfigurable Intelligent Surfaces (RIS) and Artificial Intelligence (AI).

This paper focuses on multiple-access protocol design in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs). By extending the existing approaches in single-user or single-RIS cases, we present two benchmark schemes for this multi-user multi-RIS scenario. Inspecting their shortcomings, a simple but efficient method coined opportunistic multi-user reflection (OMUR) is proposed. The key idea is to opportunistically select the best user as the anchor for optimizing the RISs, and non-orthogonally transmitting all users’ signals simultaneously. A simplified version of OMUR exploiting random phase shifts is also proposed to avoid the complexity of RIS channel estimation.

This paper presents a robust and secure framework for achieving accurate and reliable mutual localization in multiple unmanned aerial vehicle (UAV) systems. Challenges of accurate localization and security threats are addressed and corresponding solutions are brought forth and accessed in our paper with numerical simulations. The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detectio (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in the mutual localization system, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying UAVs with malicious data, enhancing the security and resilience of the mutual localization.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Most prior works on intelligent reflecting surface (IRS) merely consider point-to-point communications, including a single user, for ease of analysis. Nevertheless, a practical wireless system needs to accommodate multiple users simultaneously. Due to the lack of frequency-selective reflection, namely the set of phase shifts cannot be different across frequency subchannels, the integration of IRS imposes a fundamental challenge to frequency-multiplexing approaches such as frequency-division multiple access (FDMA) and the widely adopted technique called orthogonal FDMA (OFDMA). It motivates us to study (O)FDMA-based multi-user IRS communications to clarify which user scheduling and passive beamforming are favorable under this non-frequency-selective reflection environment. Theoretical analysis and numerical evaluation reveal that (O)FDMA does not need user scheduling when there are a few users. If the number of users becomes large, neither user scheduling nor IRS reflection optimization is necessary. These findings help substantially simplify the design of (O)FDMA-based IRS communications.

This paper focuses on multi-user downlink signal transmission in a wireless system aided by multiple reconfigurable intelligent surfaces (RISs). In such a multi-RIS, multi-user, multi-antenna scenario, determining a set of RIS phase shifts to maximize the sum throughput becomes intractable. Hence, we propose a novel scheme that can substantially simplify the optimization of passive beamforming. By opportunistically selecting a user with the best channel condition as the only active transmitter in the system, it degrades to single-user passive beamforming, where two methods, i.e., joint optimization based on the semidefinite relaxation approach and alternating optimization, are applicable. The superiority of the proposed scheme is demonstrated through Monte-Carlo simulations.

Mobile communication is in the deployment phase of its fifth generation (5G) technology standards and the research activities have begun toward the sixth generation (6G) of mobile communication systems. Private campus networks (PCNs) serving a specific use case of the vertical industries (e.g., smart factory) are commonplace in the 5G and will continue to be the same in the future. Facilitating secure operations in such a PCN is of the utmost priority. Physical layer security (PLS) is one of the most promising security paradigms for the present and future generations of mobile communication systems, which works on the principle of channel reciprocity. In this paper, PLS concepts are applied to design authentication schemes for the PCN. Measurements of the physical layer are carried out in a live PCN and a grid-wise radio environment map is constructed. A lookup table-based as well as Machine learning (Decision trees, Random Forest) based PHY packet authentication schemes are designed and evaluated to showcase their applicability in providing efficient authentication in the PCN environment.

Mobile communication systems are in the deployment phase of their fifth generation (5G) and research activities toward the sixth generation (6G) of standards are ongoing around the globe. Integration of artificial intelligence and machine learning for the optimization of the network (e.g, radio access network functionalities) is one of the key technological enablers in modern mobile networks. Further, private campus networks (PCNs) are the local networks customised to serve certain vertical industry use-cases (e.g., smart warehouse and logistics). These PCNs are the key to the vertical market penetration of 5G standards and their proliferation. Optimizing the functioning of such PCNs is of key importance for both network operators and enterprises owning the PCNs. This paper presents machine learning based methods to predict signal-to-interference-plus-noise ratio (SINR) in the controlled environment of a PCN. The network measurements are collected from a live 5G SA PCN and a grid-wise radio environment map is generated. Subsequently, machine learning methods namely Vanilla long short-term memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), and Random Forest are used in tandem with these measurements to predict SINR in advance based on mobility attributes of the user. The results show fair performance and demonstrate the applicability of such methods in PCNs.

Due to significant climate change in the past decades, the reduction of Greenhouse Gas (GHG) has been set as the top priority. In recent years, many Connected, Cooperative and Automated Mobility (CCAM) applications have been proposed and developed to improve the network and increase traffic efficiency. By following the net-zero emission reduction by 2050 in European Union (EU), a slowly increased percentage of connected vehicles (penetration rate) should be considered, and yet, most studies only show benefits of CCAM at 100% penetration rate, i.e., under assumption that all vehicles are connected. Therefore, we study the effect of different penetration rates for CCAM applications in the urban scenario. In this work, some representative German cities are chosen and classified into three Tiers based on the population. Then the urban scenarios is built in Simulation of Urban MObility (SUMO), and the CCAM applications control the vehicles via Traffic Control Interface (TraCI). Simulation results show a significant reduction in CO2emissions and an increase in vehicle speed. Then, these results are fed into a Machine Learning (ML)-based estimator, called Random Forest Regressor (RFR), to predict further improvements of CCAM at the 27 European Union Countries (EU27)-level. The predicted results can act as references or guidelines for developing or deploying connected vehicles to reduce GHG and improve traffic efficiency.

In 5G wireless communication, Intelligent Transportation Systems (ITS) and automobile applications, such as autonomous driving, are widely examined. These applications have strict requirements and often require high Quality of Service (QoS). In an urban setting, Ultra-Dense Networks (UDNs) have the potential to not only provide optimal QoS but also increase system capacity and frequency reuse. However, the current architecture of 5G UDN of dense Small Cell Nodes (SCNs) deployment prompts increased delay, handover times, and handover failures. In this paper, we propose a Machine Learning (ML) supported Mobility Prediction (MP) strategy to predict future Vehicle User Equipment (VUE) mobility and handover locations. The primary aim of the proposed methodology is to minimize Unnecessary Handover (UHO) to ensure reliable communication in vehicular communication systems. We evaluate and validate our approach on a downlink system-level simulator. We predict mobility using Support Vector Machine (SVM), Decision Tree Classifier (DTC), and Random Forest Classifier (RFC). Simulation results demonstrate a notable 30% decrease in handover times and a considerable enhancement in QoS, maintaining the advantage of 5G UDN networks.

In this work, non-linear supervised learning classifiers are proposed for Physical Layer Authentication (PLA). Their performance is evaluated using a Universal Software Radio Peripheral (USRP) based testbed under randomly distributed attacks and burst attacks within a mobile Ultra Reliable Low Latency Communication (URLLC) campus network scenario. It is shown, that in specific cases, non-linear classifiers can achieve promising results in terms of authentication accuracy and Receiver Operating Characteristics (ROCs) curve performance.

The paper proposes a framework to identify and avoid the coverage hole in an indoor industry environment. We assume an edge cloud co-located controller that followers the Automated Guided Vehicle (AGV) movement on a factory floor over a wireless channel. The coverage holes are caused due to blockage, path-loss, and fading effects. An AGV in the coverage hole may lose connectivity to the edge-cloud and become unstable. To avoid connectivity loss, we proposed a framework that identifies the position of coverage hole using a Support-Vector Machine (SVM) classifier model and constructs a binary coverage hole map incorporating the AGV trajectory re-planning to avoid the identified coverage hole. The AGV’s re-planned trajectory is optimized and selected to avoid coverage hole the shortest coverage-hole-free trajectory. We further investigated the look-ahead time’s impact on the AGV’s re-planned trajectory performance. The results reveal that an AGV’s re-planned trajectory can be shorter and further optimized if the coverage hole position is known ahead of time.

Intelligent reflecting surface (IRS) is envisioned to become a key technology for the upcoming six-generation (6G) wireless system due to its potential of reaping high performance in a power-efficient and cost-efficient way. With its disruptive capability and hardware constraint, the integration of IRS imposes some fundamental particularities on the coordination of multi-user signal transmission. Consequently, the conventional orthogonal and non-orthogonal multiple-access schemes are hard to directly apply because of the joint optimization of active beamforming at the base station and passive reflection at the IRS. Relying on an alternating optimization method, we develop novel schemes for efficient multiple access in IRS-aided multi-user multi-antenna systems in this paper. Achievable performance in terms of the sum spectral efficiency is theoretically analyzed. A comprehensive comparison of different schemes and configurations is conducted through Monte-Carlo simulations to clarify which scheme is favorable for this emerging 6G paradigm.

Reconfigurable intelligent surface (RIS) has recently drawn intensive attention due to its potential of simultaneously realizing high spectral and energy efficiency in a sustainable way. This paper focuses on the design of efficient transmission methods to maximize the uplink sum throughput in a RIS-aided multi-user multi-input multi-output (MU-MIMO) system. To provide an insightful basis, the channel capacity of RIS-aided MU-MIMO is theoretically analyzed. Then, the conventional transmission schemes based on orthogonal multiple access are presented as the baseline. From the information-theoretic perspective, we propose two novel schemes, i.e., joint transmission based on the semidefinite relaxation of quadratic optimization problems and opportunistic transmission relying on the best user selection. The superiority of the proposed schemes over the conventional ones in terms of achievable rates is justified through simulation results.

Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It adds computational power towards the edge of cellular networks, much closer to energy-constrained user devices, and therewith allows the users to offload tasks to the edge computing nodes for low-latency applications with very-limited battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes resulting in long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions. In this article, we study a latency-outage critical scenario, where users intend to limit the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations to be efficient for generic service model, when a perfect queue status information is available. For the practical case where the users obtain only imperfect queue status information, we design an optimal online learning strategy to enable its application in Poisson service scenarios.

Driven by the goal of maintaining competitiveness within the shift toward personalized production, manufacturing companies are increasingly adopting Artificial Intelligence (AI) in their manufacturing facilities, empowered by the digital transformation, Big Data, and Cyber-Physical Systems (CPS). However, the complexity caused by the continuously growing variety of AI implementation frameworks, the initial high investment in resources, and the lack of industry-wide AI standards are hindering the rapid adoption of industrial AI. To improve asset interoperability for AI assets and thereby ensure sustainable reliability by reducing investment risks, previous work has proposed an information model to wrap AI assets into the Industry 4.0 framework. Therefore the approach utilizes the Asset Administration Shell (AAS) as a realization standard of the Digital Twin (DT) in the manufacturing Industry 4.0 context. This paper provides additional practical experience from implementing the AI AAS concept in a real use case as a basis for initiating a standardization process of the resulting AI AAS information model.

In order to achieve terabits-per-second (Tbps) data rates in the sixth-generation (6G) mobile system, wireless communications are required to exploit the abundant spectrum in the millimeter-wave (mmWave) and terahertz (THz) bands. However, high-frequency transmission heavily relies on high beamforming gain to compensate for severe propagation loss. A beam-based system faces a barrier in the process of initial access, where a base station must broadcast synchronization signals and system information to all users within its coverage. Hence, this paper proposes a novel omnidirectional broadcasting scheme for mmWave and THz systems with hybrid beamforming. It provides an instantaneously equal gain over all directions by forming complementary beams over sub-arrays. Numerical results verify that it can achieve omnidirectional coverage with a performance that remarkably outperforms the previous scheme.

Modular reconfigurable batteries, also known as smart batteries, are gaining significant traction, mainly due to the large environmental incentives and falling price of electronic components. Although they have many advantages compared to a hard-wired battery pack, complex monitoring circuit and numerous sensor requirement make it harder to compete with conventional systems in a cost-driven application. This paper proposes a novel approach to estimate parameters of each individual battery module without any direct measurement at their terminals. The proposed algorithm uses the output voltage and current of the load combined with the exact knowledge of the modules’ states to estimate the open-circuit voltage, ohmic resistance, and polarization resistance in the electric circuit model for each battery module. The method combined with Kalman filter demonstrates the feasibility of this method through simulations, where the proposed method achieves above 98% and 96% accuracies for estimation of the open-circuit voltage and equivalent resistance of the battery, respectively. Additionally, the method can decouple the two resistances with

The next revolution of industry will turn the industries as well as the entire society into a human-centric shape. The human presence in industrial environment and the human participation in industrial processes will be magnified more than ever before. To cope with the emerging challenges raised by this revolution, 6G ambitions to bridge the three domains of digital information, physical assets and humans into one merged cyber-physical-human world. This proposes not only an unprecedented demand for digital twin solutions, but also new technical requirements. Especially, aiming at a human-centric industrial DT system, novel multi-sensory human-machine interfaces will play a key role in this paradigm shift.

As a complement to conventional AI solutions, emergent intelligence (EI) exhibits competitiveness in 6G IIoT scenario for its various outstanding features including robustness, protection to privacy, and scalability. However, despite the low computational complexity, EI is challenged by its high demand of data traffic in massive deployment. We propose to leverage massive twinning, which 6G is envisaged to support, to reduce the data traffic in EI and therewith enhance its performance.

Intelligent reflecting surface (IRS) is a cost-efficient technique to improve power efficiency and spectral efficiency. However, IRS-aided multi-antenna transmission needs to jointly optimize the passive and active beamforming, imposing a high computational burden and high latency due to its iterative optimization process. Making use of hybrid analog-digital beamforming in high-frequency transmission systems, a novel technique, coined dual-beam IRS, is proposed in this paper. The key idea is to form a pair of beams towards the IRS and user, respectively. Then, the optimization of passive and active beamforming can be decoupled, resulting in a simplified system design. Simulation results corroborate that it achieves a good balance between the cell-edge and cell-center performance. Compared with the performance bound, the gap is moderate, but it remarkably outperforms other sub-optimal schemes.

Exploiting the degree of freedom in the frequency domain and the near-far effect among different access points (APs), this paper proposes an opportunistic transmission scheme in cell-free massive MIMO-OFDM systems. The key idea is to orthogonally assign subcarriers among different users, so that there is only one user on each subcarrier. Then, a user is only served by its near APs through opportunistic selection, while the far APs are deactivated to avoid wasting power over their channels with severe propagation losses. Moreover, the number of active APs per subcarrier becomes small due to the opportunistic selection, making the use of downlink pilots and coherent detection feasible. As corroborated by numerical results, the proposed scheme can bring a significant performance boost in terms of both power efficiency and spectral efficiency.

In the context of cell-free massive multi-input multi-output (CFmMIMO), zero-forcing precoding (ZFP) is superior in terms of spectral efficiency. However, it suffers from channel aging owing to fronthaul and processing delays. In this paper, we propose a robust scheme coined delay-tolerant zero-forcing precoding (DTZFP), which exploits deep learning-aided channel prediction to alleviate the effect of outdated channel state information (CSI). A predictor consisting of a bank of user-specific predictive modules is specifically designed for such a multi-user scenario. Leveraging the degree of freedom brought by the prediction horizon, the delivery of CSI and precoded data through a fronthaul network and the transmission of user data and pilots over an air interface can be parallelized. Therefore, DT-ZFP not only effectively combats channel aging but also avoids the inefficient “Stop-and-Wait” mechanism of the canonical ZFP in CFmMIMO.

Technological trends in different domains have en-abled operators to integrate large numbers of sensors within their wireless communication networks. These sensors can provide data for many different fields of applications, e.g. process monitoring, eHealth or position feedback for closed loop control of autonomous vehicles. However, such systems often face the challenge to aggregate and process information from many distinct sources in a homogeneous and coherent manner. We propose a software architecture design that is based on any context management and message handling system. The general objective is to design a common interface for different information sources. We assume that by processing this data in conjunction it can be possible to obtain enhanced quality of information. The presented abstract architecture can enable the user to flexibly integrate different Information Anchors - active sensors or passive tags - within a comprehensive multi-modal, multi-domain data aggregation platform. By applying a system as proposed, it is possible to generate a pool of homogeneous data sets that can be used as a sound foundation for further analysis. We demonstrate the feasibility of the concept by presenting a possible realization of a system as described in form of an inter-operable multi-technology indoor localization system.

With the climate change high on the priority list, reduction of Greenhouse Gas (GHG) emissions with new and intelligent technologies in the transportation sector has been a widely discussed research topic. Conventional combustion engines of today will not achieve the long-term goal of zero emissions by 2050 in the European Union (EU). In this era of automation and digitization, the transportation sector is introducing a new dimension - connectivity. With the release of Vehicle-to-everything (V2X) technology, the number of devices that can potentially connect to the automobile is increasing at a rapid pace. Thus, with connected mobility, vehicles can talk to other vehicles, the infrastructure and pedestrians, thereby making the cities not only safer, but also potentially greener and more efficient. In this regard, it is important to study and analyze the benefits of Connected, Cooperative and Automated Mobility (CCAM). Therefore, in this paper, we provide a methodology to quantitatively assess the different CCAM applications and study its effects on the GHG emissions. This study shows that introduction and usage of CCAM applications, both individually and as a group, helps in reducing GHG emissions in different cities of Germany.

Artificial intelligence (AI) based channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems have attracted growing attention recently. Accurate channel prediction can effectively improve the quality of CSI and can help optimize system transmission schemes, such as the throughput and transmission efficiency. The aim of this paper is to propose an efficient deep learning algorithm for signal-to-noise ratio (SNR) prediction in the real world, and a method for measuring SNR data from a universal software radio peripheral (USRP)based software-defined radio platform. The results verify that the proposed channel measurement method is efficient for getting real-world channel data, and the deep learning-based algorithm has a strong ability on the real-world channel prediction.

With the development of smart connected automated guided vehicles (AGVs) and robots, many new services and applications occur, which require flexible wireless end-to-end communication, high data transmission, and intensive computation. To achieve such a high demanding communication system, it is very important to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance, such as throughput and transmission efficiency. In this paper, we present our efforts towards proposing a deep learning-based channel prediction algorithm, which is then evaluated on the data set measured with different system state report frequencies from our implemented software-defined radio platform in different indoor environments. Results showed that the proposed channel predictor has a convincing ability on the real-world channel prediction.

To meet the demand of supreme data rates in terabits-per-second, the next-generation mobile system needs to exploit the abundant spectrum in the millimeter-wave and terahertz bands. However, high-frequency transmission heavily relies on large-scale antenna arrays to reap high beamforming gain, used to compensate for severe propagation loss. It raises a problem of omni-directional beamforming during the phase of initial access, where a base station is required to broadcast synchronization signals and system information to all users within its coverage. This paper proposes a novel initial beamforming scheme, which provides instantaneous gain equally in all directions by forming a pair of complementary beams. Numerical results verify that it can achieve omni-directional coverage with the optimal performance that remarkably outperforms the previous scheme called random beamforming. It is applicable for any form of large-scale arrays, and all three architecture, i.e., digital, analog, and hybrid beamforming.

The increasing demand for road freight has raised tremendous attention to vehicle platooning, which reduces air resistance and improves fuel economy. To achieve a small and safe spacing between vehicles while ensuring platoon stability, wireless communication assistance is indispensable. However, the imperfection of communication brings degradation of platooning performance (i.e., spacing error) and more possibilities for adversarial attacks. This article proposes a prediction-assisted platooning mechanism from the perspective of performance optimization, wherein each vehicle establishes its local platoon model based on the information received from the communication network, thereby reducing information latency. Then, to secure the system against malicious vehicles, we carry out analysis and design of a detection algorithm for a typical attack type, i.e., the data-injection attack. The detection is based on three indicators: 1) absolute spacing error; 2) spacing prediction error; and 3) acceleration prediction error. The advantages of the novel platooning mechanism and detection algorithm are ultimately demonstrated on a road-traffic simulation platform that considers the imperfection of realistic vehicle perceptions and Cellular-Vehicle-to-Everything (C-V2X) communication.

Smart manufacturing has the objective of creating highly flexible and resource optimized industrial plants. Furthermore, the improvement of product quality is another important target. These requirements implicate more complex control algorithms. Processing these algorithms may exceed the capabilities of resource constrained devices, such as programmable logic controllers (PLCs). In this case, the necessity for computation offloading is given. Due to the fact that industrial plants are currently designed for a life-cycle-time of more than ten years, in a realistic smart manufacturing scenario, these devices have to be considered. Therefore, we investigate the impact of complex algorithms on conventional PLCs by simulating them with a load generator. In addition, we propose a realistic factory scenario including benchmarks for both wireline and wireless communication systems. Thus, their round-trip time (RTT) is measured with and without additional load on the network. With the help of these investigations, break-even points for the application of computation offloading of two typical PLCs of Siemens S7 series can be calculated.

Databases have a huge impact on the functionality of a running system, especially in the context of the Internet of Things (IoT). In the IoT, smart products are created with the usage of neural networks (NN) that require huge amount of stored data for training. The programming language Python is the common way for creating and working with NNs and its data preparation. Therefore, it is useful to use the same language for both, database driver and NN. For this reason, four well-known databases (DBs) are examined for handling different operations. In this paper, the performance of different not only Structured Query Language (NoSQL) DBs (Redis, Cassandra, mongoDB) and MySQL are compared to each other, using most popular Python clients. Important for the performance measurements are not only the actual operation time but also the preparation time that Python or the DB requires to access the data. The results of the paper show that Redis is efficient with small data sets, whereas mongoDB and MySQL are usable for all kinds of data sets, whereas Cassandra handles reading operations well.

Deterministic communication across integrated wired and wireless networks is currently one of the big topics in research and standardization. 5G and TSN integration efforts are at the forefront of enabling the convergence of wired and wireless networks for Industry 4.0.In this paper, we investigate how synchronization and syntonization errors affect the achievable end-to-end time synchronization accuracy in integrated 5G and TSN networks. We specifically focus on the impact of the 5G System modeling a TSN transparent clock according to 3GPP Release 17.

Machine learning (ML) grows to be an inseparable part of future networks and an enabler for internet of things use-cases. Input components of machine learning units (MLU) have different levels of relevancy for determining accurate output. Assuming that attributes of each MLU are transmitted from multiple terminals, we revisit the resource allocation problem for a ML-based centralized control system, considering relevancy in order to provide best effort MLU outcome as key performance indicator. To achieve this goal, we propose a heuristic greedy resource allocation algorithm in combination with lookup tables of various payload requirements. These tables are generated by a Kullback-Leibler divergence (KLD)-based approach for each MLU. The KLD assigns number of quantization bits for input attributes of each terminal considering their relevancy.The proposed resource allocation is applied to a network of inverted pendulums on carts with MLUs as controllers. Simulation results demonstrate significant gains in MLU performance and efficiency in resource utilization when employing the proposed technique comparing with two benchmarks: the greedy maximum sum rate (GMSR) algorithm allocating resources once with equal payload requirements, and once KLD lookup table.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.

A key aspect of 5G is the facilitation of network convergence. It enables the support of a broad range of use cases including real-time applications over the same shared network infrastructure. One major challenge in addressing the various latency requirements is the efficient sharing of resources for heterogeneous services under the presence of different scheduling timescales. State-of-the-art solutions propose preemptive approaches to satisfy instantaneous time-critical demands, which however can deteriorate the communication for preempted users as well as the overall rate efficiency in the network. We propose a slicing method tailored for deterministic traffic, as commonly found in industrial applications. By exploiting deterministic properties of resource demands, we determine an optimal share of reserved and preempted resources on a per-slot basis to efficiently satisfy all time-critical demands. Additionally, we introduce a preemption restricted slice to protect users with higher reliability requirements. Under this proposed model, we derive an online scheduling algorithm which maintains a better fairness and higher throughput compared to state-of-the-art solutions under high demand of deterministic traffic.

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.

Wireless control applications will play a fundamental role in future factory environments to enable novel mobility use cases and achieve the desired flexibility in production. The standardized integration of 5G into a time-sensitive networking (TSN) environment defines the joint architecture and communication interfaces between the different networks to support time-critical applications. Managing and scheduling such a network in an end-to-end fashion to guarantee the stringent QoS requirements is however a complex task. In this work, we present an efficient window-based resource allocation method for the end-to-end network. A big challenge in scheduling periodic and time-critical control applications over a wireless system is the uncertainty of the channel. We hence introduce a fading correlation-aware optimization to find robust schedules. Our simulation study shows that this approach is able to reduce peak resource demands and hence improve the schedulability compared to standard channel-unaware TSN scheduling.

Programmable logic controllers (PLCs) are the core element of industrial plants in todays deployments. They read sensor values, execute control algorithms, and write output values. Furthermore, industrial plants have lifetimes of one or more decades. Thus, in a realistic Industry 4.0 scenario, these devices have to be integrated in novel systems. In order to apply advanced concepts and technologies, such as computation offloading, which requires data exchange between PLCs and edge cloud, we investigate open communication interfaces of two typical PLCs of Siemens S7 series. Hence, each of the interfaces is analyzed based on plug & play capability, if metadata is provided, protocol efficiency, and performance. For the latter, the smallest possible update time for each of the interfaces will be measured.

Operational Technology (OT) networks and devices, i.e., all components used in industrial environments, were not designed with security in mind. Efficiency and ease of use were the most important design characteristics. However, due to the digitization of industry, an increasing number of devices and industrial networks are opened up to public networks. This is beneficial for the administration and organization of the industrial environments. However, it also increases the attack surface, providing possible points of entry for an attacker. Originally, breaking into production networks meant to break an information technology (IT)-perimeter first, such as a public Website, and then to move laterally to industrial control systems (ICSs) to influence the production environment. However, many OT-devices are connected directly to the Internet, which drastically increases the threat of compromise, especially since OT-devices contain several vulnerabilities. In this work, the presence of OT-devices in the Internet is analyzed from an attacker’s perspective. Publicly available tools, such as the search engineShodanand vulnerability databases, are employed to find commonly used OT-devices and map vulnerabilities to them. These findings are grouped according to the country of origin, manufacturer, and number as well as severity of vulnerability. More than 13000 devices were found, almost all contained at least one vulnerability. European and Northern American countries are by far the most affected ones.

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI.

Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vs-exploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using off-the-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.

Outdated channel state information (CSI) has a severely negative impact on the performance of a wide variety of adaptive transmission systems. Channel prediction is an effective method that can directly improve the quality of CSI. To realize the full potential of adaptive systems, the prediction horizon should be long enough to at least compensate for the time delay. In this paper, therefore, we focus on the problem of long-range prediction (LRP), i.e., how to forecast fading channels as far ahead as possible. Two different LRP approaches - Multi-Step Prediction and Fading Signal Processing - are proposed for the predictors based on classical Kalman filter and recently proposed recurrent neural networks. As an application example, we present an LRP-aided transmit antenna selection system, whose performance in noisy and correlated channels is evaluated. Numerical results reveal that the RNN predictor can achieve a comparable performance with respect to the classical predictor, while avoiding its drawbacks in parameter estimation and multi-step processing.

While the industry is continuously evolving towards more flexible and modular manufacturing environments, simultaneously requirements on the industrial network infrastructure are changing. Network convergence, flexibility in configuration with support of hard real-time end-to-end guarantees are playing a key role. A technology supporting these features is Time-sensitive Networking (TSN). The extension with TSN-over-5G to additionally support mobile use cases brings us one step closer towards a unified and converged wireless and wired network architecture, as needed for future manufacturing environments. At the same time, management and configuration of such large converged networks to guarantee real-time capabilities becomes inherently complex. In this work, we present a joint configuration approach that enables end-to-end scheduling optimization over a bridged network consisting of TSN and logical 5G bridges. We derive suitable network constraints and optimization strategies and apply constraint programming to determine feasible scheduling solutions. By providing numerical results from a simulative study, we show the effectiveness of our proposed end-to-end framework in terms of resource efficiency and support of concurrent users in the network.

The term Industry 4.0, which refers to the fourth industrial revolution, aims at the digitalization of industries, including all kinds of production assets. With the help of these, so-called "industrial cyber-physical systems", which form the industrial Internet of Things, numerous novel use cases that are key enabler for a smart manufacturing, can be realized. However, existing facilities mainly consist of legacy equipment and technologies that do not offer these kind of flexibility. To address this issue, we introduce an architecture that allows a flexible reconfiguration and redeployment of future process control systems. Moreover, a high system availability and reliability, as required by industrial applications, has been taken into account. The architectural design follows the 4+1 model approach as it is available in the literature. This ensures, that the design results in a holistic architecture. Additionally, we provide insights into first results and outline future development steps.

Industry 4.0 brings up new types of use cases, whereby mobile use cases play a significant role. These use cases have stringent requirements on both automation and communication systems that cannot be achieved with recent shop floor technologies. Therefore, novel technologies such as IEEE time-sensitive networking (TSN) and Open Platform Communications Unified Architecture (OPC UA) are being introduced. In addition, for the realization of mobile use cases, wireline technologies cannot be used and have to be replaced by wireless connections, which have to meet the high demands of the industrial landscape. Here, 5th generation wireless communication system (5G) is seen as a promising candidate.Especially encouraging and similarly challenging is the cooperative work of mobile robots, where particularly high demands on time synchronization arise. Therefore, this paper introduces a concept for the integration of TSN time synchronization (IEEE 802.1AS) conform with 5G to fulfill the requirements of these use cases. Furthermore, the paper describes a testbed for discrete manufacturing, consisting pre-dominantly of industrial equipment, in order to evaluate the presented approach.

Industry 4.0 describes an adaptive and changeable production, where its factory cells have to be reconfigured at very short intervals, e.g. after each workpiece. Furthermore, this scenario cannot be realized with traditional devices, such as programmable logic controllers. Here the use of well-proven technologies of the information technology are conquering the production hall (IT-OT convergence). Therefore, both virtualization and novel communication technologies are being introduced in the field of industrial automation. In addition, these technologies are seen as the key for facilitating various emerging use cases. However, it is not yet clear whether each of the dedicated hardware and software components, which have been developed for specific control tasks and have performed well over decades, can be upgraded without major adjustments. In this paper, we examine the opportunities and challenges of hardware and operating system-level virtualization based on the stringent requirements imposed by industrial applications. For that purpose, benchmarks for different virtualization technologies are set by determining their computational and networking overhead, configuration effort, accessibility, scalability, and security.

With the aid of accurate channel state information (CSI) at the transmitter, a wireless system can receive great performance by adaptively selecting its transmission parameters. However, the CSI becomes outdated quickly due to the rapid channel variation caused by multi-path fading, leading to severe performance degradation. Such an impact is applicable on a wide variety of adaptive transmission systems, the fading channel prediction that can combat the outdated CSI is therefore of great significance. The aim of this paper is to propose two novel recurrent neural network (RNN)-based predictors, leveraging the strong time-series prediction capability of long short-term memory or gated recurrent unit. Performance evaluation is conducted and the results in terms of prediction accuracy verify that the proposed predictors notably outperform the conventional RNN predictor.

Channel state information (CSI) plays a vital role in adaptive transmission systems, which adapt their transmission parameters to instantaneous channel conditions. However, the CSI tends to become outdated due to the rapid channel variation caused by multi-path fading. The inaccuracy of outdated CSI imposes a severe impact on the performance of a wide range of wireless systems, highlighting the significance of channel prediction that can combat the outdated CSI effectively. The aim of this paper is to propose a novel predictor, leveraging the strong time-series prediction capability of deep learning, where a deep recurrent neural network incorporating long short-term memory or gated recurrent unit is applied. Performance evaluation is carried out upon multi-antenna fading channels, and the numerical results in terms of prediction accuracy unveil that deep learning can bring a notable performance gain compared with the conventional predictors built on shallow neural networks.

Vehicle-to-everything (V2X) communications plays a significant role in increasing traffic safety and efficiency by enabling vehicles to exchange their status information with other vehicles and traffic entities in their proximity. In this regard, two technologies emerged as the main contenders for enabling V2X communications which have stringent requirements in terms of latency and reliability due to their apparent safety criticality. The first one is the Dedicated Short Range Communications (DSRC) standard (referred to as ITS-G5 in Europe) that is well researched since 20 years and has attained enough technical maturity for current deployment. The second one is the relatively new Cellular-V2X (C-V2X) standard that is nevertheless, based on the 3rd Generation Partnership Project (3GPP) standard family that have successful deployments in almost every corner of the globe. In this work, we compare the link level performance of the Physical Layer (PHY) protocols for both the technologies for different vehicular fading channel models. To this end, we construct and simulate the PHY pipelines and show the performance results by means of Block Error Rate (BLER) versus Signal-to-Noise Ratio (SNR) graphs. Our investigations show that C-V2X performs better than ITS-G5 for almost all the considered channel models due to better channel coding and estimation schemes.

In recent years, the next generation of wireless communication (5G) plays a significant role in both industry and academy societies. Cellular Vehicle-to-Everything (C-V2X) communication technology has been one of the prominent services for 5G. For C-V2X transmission mode, there is a newly defined communication channel (sidelink) that can support direct C-V2X communication. Direct C-V2X communication is a technology that allows vehicles to communicate and share safety-related information with other traffic participates directly without going through the cellular network. The C-V2X data packet will be delivered to all traffic Users (UE) in the proximity of the Transmitter (Tx). Some UEs might not successfully receive the data packets during one transmission but the sidelink Tx is not able to check whether the Receivers (Rxs) get the information or not due to the lack of feedback channel. For enabling the strict requirements in terms of reliability and latency for C-V2X communication, we propose and evaluate one retransmission scheme and retransmission with different traffic speed scheme. These schemes try to improve the reliability of the safety-related data by one blind retransmission without requiring feedback. Although this retransmission scheme is essential to C-V2X communication, the scheme has a limitation in the performance aspect because of its redundant retransmission. Since radio resources for C-V2X communication are limited, we have to detect the effect of retransmission on the performance of the communication system. To the end, the simulator for evaluating the proposed schemes for the C-V2X communication has been implemented, and the performances of the different communication schemes are shown through the Packet Reception Ratio (PRR).

Increasing heterogeneity of industrial network systems is a fact and the chances that in the future one communication standard will be able to fulfill the requirements of all possible applications are utopian. With the increasing number of communication systems, their management, configuration, and maintenance become a significant issue. Additionally, due to the increasing amount of network services and traffic, the management of available network resources and the possibility of delivering certain levels of communication quality of service, especially across different network solutions becomes a big challenge. Therefore, in this paper a Controller of Controllers (CoC) concept for management of heterogeneous industrial networks is proposed. The concept is designed to support still widely spread legacy fieldbus systems, different Ethernetbased industrial solutions, and potentially upcoming network technologies. The goal is achieved by leveraging state-of-theart architectural concepts such as software-defined networks, which allows for integration of abstract models in network management. The concept is described and discussed by way of a demonstrator concept for a heterogeneous system of fieldbus and Ethernet-based time-sensitive networks.

The digitisation of industry provides a plethora of novel applications that increase flexibility and reduce setup and maintenance time as well as cost. Furthermore, novel use cases are created by the digitisation of industry, commonly known as Industry 4.0 or the Industrial Internet of Things, applications make use of communication and computation technology that is becoming available. This enables novel business use cases, such as the digital twin, customer individual production, and data market places. However, the inter-connectivity such use cases rely on also significantly increases the attack surface of industrial enterprises. Sabotage and espionage are aimed at data, which is becoming the most crucial asset of an enterprise. Since the requirements on security solutions in industrial networks are inherently different from office networks, novel approaches for intrusion detection need to be developed. In this work, process data of a real water treatment process that contains attacks is analysed. Analysis is performed by an extension of Matrix Profiles, a motif discovery algorithm for time series. By extending Matrix Profiles with a Hamming-distance metric, binary and tertiary actuators can be integrated into the analysis in a meaningful fashion. This algorithm requires low training effort while providing accurate results. Furthermore, it can be employed in a real-time fashion. Selected actuators in the data set are analysed to highlight the applicability of the extended Matrix Profiles.

Mass events represent one of the most challenging scenarios for mobile networks because, although their date and time are usually known in advance, the actual demand for resources is difficult to predict due to its dependency on many different factors. Based on data provided by a major European carrier during mass events in a football stadium comprising up to 30.000 people, 16 base station sectors and 1 Km2area, we performed a data-driven analysis of the radio access network infrastructure dynamics during such events. Given the insights obtained from the analysis, we developed ARENA, a model-free deep learning Radio Access Network (RAN) capacity forecasting solution that, taking as input past network monitoring data and events context information, provides guidance to mobile operators on the expected RAN capacity needed during a future event. Our results, validated against real events contained in the dataset, illustrate the effectiveness of our proposed solution.

Operation technology networks, i.e. hard- and software used for monitoring and controlling physical/industrial processes, have been considered immune to cyber attacks for a long time. A recent increase of attacks in these networks proves this assumption wrong. Several technical constraints lead to approaches to detect attacks on industrial processes using available sensor data. This setting differs fundamentally from anomaly detection in IT-network traffic and requires new visualization approaches adapted to the common periodical behavior in OT-network data. We present a tailored visualization system that utilizes inherent features of measurements from industrial processes to full capacity to provide insight into the data and support triage analysis by laymen and experts. The novel combination of spiral plots with results from anomaly detection was implemented in an interactive system. The capabilities of our system are demonstrated using sensor and actuator data from a real-world water treatment process with introduced attacks. Exemplary analysis strategies are presented. Finally, we evaluate effectiveness and usability of our system and perform an expert evaluation.

The combination of recent emerging technologies such as network function virtualization (NFV) and network programmability (SDN) gave birth to the novel Network Slicing paradigm. 5G networks consist of multi-tenant infrastructures capable of offering leased network “slices” to new customers (e.g., vertical industries) enabling a new telecom business model: Slice-as-a-Service (SlaaS). However, as the service demand gets increasingly dense, slice requests congestion may occur leading to undesired waiting periods. This may turn into impatient tenant behaviors that increase potential loss of the business attractiveness to customers. In this paper, we aim to: 1) study the slicing admission control problem by means of a multi-queuing system for heterogeneous tenant requests; 2) derive its statistical behavior model; 3) find out the rational strategy of impatient tenants waiting in queue-based slice admission control systems; 4) prove mathematically and empirically the benefits of allowing infrastructure providers to share its information with the upcoming tenants; and 5) provide a utility model for network slices admission optimization. Our results analyze the capability of the proposed SlaaS system to be approximately Markovian and evaluate its performance as compared to a baseline solution.

Until two decades ago, industrial networks were deemed secure due to physical separation from public networks. An abundance of successful attacks proved that assumption wrong. Intrusion detection solutions for industrial application need to meet certain requirements that differ from home- and office-environments, such as working without feedback to the process and compatibility with legacy systems. Industrial systems are commonly used for several decades, updates are often difficult and expensive. Furthermore, most industrial protocols do not have inherent authentication or encryption mechanisms, allowing for easy lateral movement of an intruder once the perimeter is breached. In this work, an algorithm for motif discovery in time series, Matrix Profiles, is used to detect outliers in the timing behaviour of an industrial process. This process was monitored in an experimental environment, containing ground truth labels after attacks were performed. Furthermore, the graph representations of a different industrial data set that has been emulated are used to detect malicious activities. These activities can be derived from anomalous communication patterns, represented as edges in the graph. Finally, an integration concept for both methods is proposed.

Accurate channel state information (CSI) is a prerequisite to reap the benefits of fading-adaptive wireless communications. In practice, however, the available CSI is generally outdated due to processing and feedback delays, which deteriorate system's performance severely. Channel prediction that is able to forecast future CSI provides a promising solution. In addition to statistical methods, namely modelling a time-varying channel as an autoregressive process and using a Kalman filter to predict, artificial intelligence techniques with the capability of time-series prediction are also being discussed recently. This paper compares performance and complexity of these two kinds of predictors. The numerical results on prediction accuracy measured by mean squared error in both noiseless and noisy Rayleigh fading channels, together with their achieved performance in a transmit antenna selection system, are comparatively illustrated.

The combination of recent emerging technologies such as network function virtualization (NFV) and network programmability (SDN) gave birth to the Network Slicing revolution. 5G networks consist of multi-tenant infrastructures capable of offering leased network “slices” to new customers (e.g., vertical industries) enabling a new telecom business model: Slice-as-a-Service (SlaaS). In this paper, we aim i) to study the slicing admission control problem by means of a multi-queuing system for heterogeneous tenant requests, ii) to derive its statistical behavior model, and iii) to provide a utility-based admission control optimization. Our results analyze the capability of the proposed SlaaS system to be approximately Markovian and evaluate its performance as compared to legacy solutions.

Outdated channel state information (CSI) severely degrades the performance of adaptive transmission systems that adapt their transmissions to channel fading. In contrast with mitigation methods that sacrifice scarce wireless resources to compensate for such a performance loss, channel prediction provides an efficient solution. A few predictors for frequency-flat channels were by far proposed, whereas those suited to frequency-selective channels are seldom explored. In this paper, therefore, we propose to apply a recurrent neural network to build a frequency-domain channel predictor for wideband communications. As an application example, integrating a predictor into a multi-input multi-output orthogonal frequency- division multiplexing system to improve the correctness of antenna selection is provided. Performance assessment is carried out in multi-path fading channels defined by 3GPP Extended Vehicular A and Extended Typical Urban models. Results reveal that this predictor is effective to combat the outdated CSI with reasonable computational complexity. It outperforms the Kalman filter-based predictor notably and has intrinsic flexibility to enable multi-step prediction.

Mobile communication has been evolving through various generations and is currently on the verge of its fifth generation (5G). The ubiquitous usage of this technology is reflected by its ever increasing number of subscribers and data traffic demand. The contemporary cellular networks are composed of different Radio Access Technologies (RAT) (e.g, LTE, HSPA, (soon) 5G) with different cell sizes, transmission frequencies etc., co- existing with each other forming heterogeneous networks (HetNets). Multipath-TCP (MPTCP) enables simultaneous use of multiple network interfaces (through multiple RATs) available at the UE to maximize resource usage. The application however is oblivious to the presence of multiple TCP connections (or subflows) and perceives it as a regular TCP interface. Though promising in HetNets, MPTCP faces several challenges among which handling of user handover (HO) is a key issue. This paper focuses on the effects of handovers and coverage loss on the performance of MPTCP and proposes Mobility Context Awareness (MCA) as a potential solution. The prediction of HOs in secondary RAT will be beneficial to take appropriate actions (e.g., Coordinated Multipoint (CoMP) transmission) in order to sustain scheduled subflows on such RATs. The simulation results show key improvements in goodput, assimilation time of ordered packets during handovers, which become crucial for delay sensitive applications.

Using V2X communication in platoons promises benefits regarding energy efficiency and fleet management. It is also a safety critical process with the potential to cause dangers to life and limb which needs to be secured against attackers. We propose two protocols for secure platoon communication and provide a comparative analysis of those protocols.

PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within Ultra Reliable Low Latency Communication (URLLC) scenarios in order to meet the requirement of secure user data transmissions in the sense of authenticity and integrity. In this work, we investigate the performance of supervised learning classifiers for discriminating legitimate transmitters from illegimate ones in such scenarios. We further present our methodology of data collection using Software Defined Radio (SDR) platforms and the data processing pipeline including e.g. necessary preprocessing steps. Finally, the performance of the considered supervised learning schemes under different side conditions is presented.

In the context of the Industrial Internet of Things, communication technology, originally used in home and office environments, is introduced into industrial applications. Commercial off-the-shelf products, as well as unified and well-established communication protocols make this technology easy to integrate and use. Furthermore, productivity is increased in comparison to classic industrial control by making systems easier to manage, set up and configure. Unfortunately, most attack surfaces of home and office environments are introduced into industrial applications as well, which usually have very few security mechanisms in place. Over the last years, several technologies tackling that issue have been researched. In this work, machine learning-based anomaly detection algorithms are employed to find malicious traffic in a synthetically generated data set of Modbus/TCP communication of a fictitious industrial scenario. The applied algorithms are Support Vector Machine (SVM), Random Forest, k-nearest neighbour and k-means clustering. Due to the synthetic data set, supervised learning is possible. Support Vector Machine and k-nearest neighbour perform well with different data sets, while k-nearest neighbour and k-means clustering do not perform satisfactorily.

The overall Industry 4.0 (I4.0) developments combined with the disruptive process of IT-based digitalisation create a vast amount of new opportunities but also challenges for the industrial automation domain. The combination of hybrid (wired & wireless) communication architectures, already widely installed legacy technologies, new approaches, such as Time-Sensitive Networking (TSN) or 5G, and the general heterogeneity of the industrial landscape results in a high configuration complexity. This creates the necessity for future-proof industrial communication network management systems. Therefore, this paper summarises the current state of the art in this area in order to identify the specific requirements towards future industrial network management systems. The most promising candidate is the Software-Defined Networking (SDN) concept. To evaluate SDN as a possible enabler, specified industrial requirements are compared with the current technological and conceptual capabilities of SDN. In addition, drawbacks resulting in future research questions are identified.

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).

Factory automation and production are currently undergoing massive changes, and 5G is considered being a key enabler. In this paper, we state uses cases for using 5G in the factory of the future, which are motivated by actual needs of the industry partners of the “5Gang” consortium. Based on these use cases and the ones by 3GPP, a 5G system architecture for the factory of the future is proposed. It is set in relation to existing architectural frameworks.

Impulsive noises widely existing in various channels can significantly degrade the performance and reliability of communication systems. The Bernoulli- Gaussian (BG) model is practical to characterize noises in this category. To estimate the BG model parameters from noise measurements, a precise impulse detection is essential. In this paper, we propose a novel blind impulse detector, which is proven to be fast and accurate for BG noise in underspread communication channels.

Recently, both the industry and academy societies are trying to develop the next generation of cellular network (5G) to support vehicle-to-everything (V2X) communication. V2X communication can be used for information exchange among traffic participants and enable a cooperative driving to avoid accidents and also improve traffic efficiency. However, there are certain technical challenges faced by cellular networks to meet the requirements of V2X communication. For instance, the fast movement of vehicles in highway scenarios requires a large V2X communication range which can not be guaranteed by a single-hop direct V2X communication. In this work, we propose a two-hop direct V2X communication scheme to improve packet transmission range and therefore enhance service reliability. In this scheme, a V2X packet will be broadcasted by its generator directly to all nearby users (UEs) over sidelink. Among these receivers that locate in the proximity of the transmitter, some receivers will be selected and act as relays to retransmit its received packet. In this way, receivers which locate inside the targeted communication range but far away from the transmitter have a possibility to receive the packet from two different transmission hops and therefore the packet reception ratio can be increased. In order to select the proper receivers as relays, it is proposed for the network to collect and exploit certain context information. In addition, an optimization problem is constructed to optimally adapt the resource allocation among different hops according to the real-time system conditions. Last but not least, a system-level simulation is implemented to evaluate the proposed two-hop direct V2X communication with different resource allocation options.

Outdated channel state information (CSI) has a severe impact on a wide variety of wireless techniques. Making use of artificial intelligence, a multi-antenna channel predictor is proposed in this paper. It can accurately forecast the future CSIs up to tens of symbols ahead in a fast fading channel. By tuning the number of neurons in the input and output layers of neural network (NN) in terms of the number of antennas, it congenitally suits a multi-antenna system. Relying on a NN with real-valued weights, this predictor is simpler but substantially outperforms existing complex-valued NN predictors.

Channel state information (CSI) plays a vital role in fading-adaptive wireless systems, whereas acquired CSI is generally inaccurate due to feedback delay. Channel prediction that is able to forecast upcoming CSI provides a promising approach to tackle this problem. Exploiting the capability of time-series prediction in neural network, a fading channel predictor is proposed in this paper. Since the number of neurons in the input and output layers is tunable to adapt the number of antennas, the predictor well suits multi- antenna systems. Numerical results in antenna selection system reveal that a substantial performance gain can be achieved by applying channel prediction.

Mobile communication has become one of the key technologies in contemporary world which is on the verge of its fifth generation (5G). In parallel, Intelligent Transportation System (ITS) is an emerging sector whose focus is safe, smart and efficient transportation. In recent years, various vehicular use cases are studied within the scope of cellular networks, to provide reliable and low latency services as required. With contemporary advancements such as Mobile Edge Clouds (MEC), ability of cellular networks to support vehicular use cases with low latency requirements (e.g., platooning of vehicles) has also increased. In this work, mobility context awareness is built in a cellular network and is subsequently exploited to assist migration of service or application that is being used by vehicular use case (e.g., vehicular platoon). Further, solutions based on Cooperative Multi-Point transmission (CoMP) and sidelink transmission are proposed to be used in tandem with service migration, to reduce data/service interruption that would arise due to high mobility of vehicles (e.g., handovers (HO)). Message sequence charts are also presented for aforementioned solutions and functional framework is presented for inspected vehicular scenario. Simulation and analysis show the potential of proposed mobility context awareness schemes in assisting service migration among MECs in timely fashion and reducing amount of data transfer and data interruption times, for considered vehicular use case.

As the next generation of wireless system targets at providing a wider range of services with divergent QoS requirements, new applications will be enabled by the fifth generation (5G) network. Among the emerging applications, vehicle-to-everything (V2X) communication is an important use case targeted by 5G to enable an improved traffic safety and traffic efficiency. Since the V2X communication requires a low end-to-end (E2E) latency and an ultra-high reliability, the legacy cellular networks can not meet the service requirement. In this work, we inspect on the system performance of applying the LTE-Uu and PC5 interfaces to enable the V2X communication. With the LTE-Uu interface, one V2X data packet is transmitted through the cellular network infrastructure, while the PC5 interface facilitates the direct V2X communication without involving the network infrastructure in user-plane. In addition, due to the high reliability requirement, the application of a single V2X transmission technology can not meet the targets in some scenarios. Therefore, we also propose a multi-radio access technologies (multi-RATs) scheme where the data packet travels through both the LTE-Uu and PC5 interfaces to obtain a diversity gain. Last but not least, in order to derive the system performance, a system level simulator is implemented in this work. The numerical results provide us insights on how the different technologies will perform in different scenarios and also validate the proposed multi-RATs scheme.

Time-Sensitive Networking (TSN) is becoming more important in automotive and industrial sectors. There is no clear strategy how to use TSN to improve and migrate the established real-time protocols without excluding legacy devices. This paper presents a realistic concept to integrate TSN into automation networks. We choose Sercos as a deterministic Industrial-Ethernet protocol to be extended with the TSN Standards IEEE 802.1AS-Rev and IEEE 802.1Qbv. By simulating the Sercos over TSN network, we investigate the impact of integrating a TSN-capable switch in a Sercos network to enable a topology extension from line/ring to star/tree. By comparing the impact of: increasing the data rate and topology extension, we demonstrate the improvement of Industrial-Ethernet timing performance.

Massive Machine-Type Communication (mMTC) is expected to be strongly supported by future 5G wireless networks. Its deployment, however, is seriously challenged by the high risk of random access (RA) collision. A popular concept is to reduce RA collisions by clustering mMTC devices into groups, and to connect group members with device-to-device (D2D) links. However, analytical models of this method and discussions about the reliability of D2D links are still absent. In this paper, existing grouping-based solutions are reviewed, an analytical model of grouped RA collision is proposed. Based on the analytical model, the impact of D2D reliability on the collision rate is also investigated. Afterwards, an efficient grouped RA procedure is designed to extend the state-of-the-art with an efficient local group update mechanism against D2D link exceptions.

A wireless system is considered, where, computationally complex algorithms are offloaded from user devices to an edge cloud server, for the purpose of efficient battery usage. The main focus of this paper is to characterize and analyze, the trade-off between the energy consumed for processing the data locally, and for offloading. An analytical framework is presented, that minimizes the in-device energy consumption, by providing an optimal offloading decision for multiple user devices. A closed form solution is obtained for the offloading decision. The solution also provides the amount of computational data that should be offloaded, for the given computational and communication resources. Consequently, reduction in the energy consumption is observed.

In context of Industry 4.0 Augmented Reality (AR) is frequently mentioned as the upcoming interface technology for human-machine communication and collaboration. Many prototypes have already arisen in both the consumer market and in the industrial sector. According to numerous experts it will take only few years until AR will reach the maturity level to be deployed in productive applications. Especially for industrial usage it is required to assess security risks and challenges this new technology implicates. Thereby we focus on plant operators, Original Equipment Manufacturers (OEMs) and component vendors as stakeholders. Starting from several industrial AR use cases and the structure of contemporary AR applications, in this paper we identify security assets worthy of protection and derive the corresponding security goals. Afterwards we elaborate the threats industrial AR applications are exposed to and develop an edge computing architecture for future AR applications which encompasses various measures to reduce security risks for our stakeholders.

The core innovation in future 5G cellular networks-network slicing, aims at providing a flexible and efficient framework of network organization and resource management. The revolutionary network architecture based on slices, makes most of the current network cost models obsolete, as they estimate the expenditures in a static manner. In this paper, a novel methodology is proposed, in which a value chain in sliced networks is presented. Based on the proposed value chain, the profits generated by different slices are analyzed, and the task of network resource management is modeled as a multi-objective optimization problem. Setting strong assumptions, this optimization problem is analyzed starting from a simple ideal scenario. By removing the assumptions step-by-step, realistic but complex use cases are approached. Through this progressive analysis, technical challenges in slice implementation and network optimization are investigated under different scenarios. For each challenge, some potentially available solutions are suggested, and likely applications are also discussed.

In today's world, mobile communication has become a ubiquitously used technology, currently on the verge of its fifth generation (5G). 5G anticipates higher traffic volume and connected device density than present. It further envisions variety of service types and use cases arising from them. Various 5G concepts are being developed in order to satiate the aforementioned challenges. One of the key tasks for 5G consortium however, is to present the developed technology and concepts to wider audience (academia, industry) and even convince decision makers from non-information and communications technology (ICT) industries. Thus, it is desired to have easy to understand illustrations of envisioned 5G use cases and proposed technical solutions, targeting the non-experts. The contemporary simulation framework has limited or no provision to dynamically visualize and interact with the simulation scenario, rendering it difficult to be used for marketing as well as academic purposes. In this paper, we propose a serious game engineering based 3D visualization and evaluation framework, which allows dynamic and interactive visualization of simulated scenario. The proposed framework is simulator agnostic which enables viewer to immerse oneself into the simulated scene, visualize key performance indicators (KPI) in almost real time and even interact with simulator by changing key simulation parameters. These features equip the proposed interactive visualization/evaluation platform (IVEP) to demonstrate the 5G use cases and developed concepts better, paving way for easier understanding and evaluation of the concepts even by non-experts.

Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.

Device-to-Device (D2D) communication is considered as one of the key technologies for the fifth generation wireless communication system (5G) due to certain benefits provided, e.g. traffic offload and low end-to-end latency. A D2D link can reuse resource of a cellular user for its own transmission, while mutual interference in between these two links is introduced. In this paper, we propose a smart radio resource management (RRM) algorithm which enables D2D communication to reuse cellular resource, by taking into account of context information. Besides, signaling schemes with high efficiency are also given in this work to enable the proposed RRM algorithm. Simulation results demonstrate the performance improvement of the proposed scheme in terms of the overall cell capacity.

Mobile communication is one of the most ubiquitously used technologies in contemporary world, evolving towards its fifth generation (5G). In day-to-day scenarios, many vehicular users avail broadband cellular services while traveling. The density of such vehicular users change dynamically in a cell and at certain sites (e.g. signal lights), traffic jams would arise frequently. Such conditions would pose high load situation to respective serving base station. As a consequence, the cell site would experience high dropping and blocking rates and subject its users to poor Quality of Experience (QoE). In this work, mobility behavior of vehicular users are analyzed and an algorithm is designed to predict traffic status of a cell. The proposed traffic prediction algorithm is a coalition strategy consisting of schemes to predict user cell transition, vehicular cluster/moving network detection, user velocity monitoring etc. The traffic status indication provided by the algorithm could be used to design efficient radio resource management (RRM) techniques. In the presented paper, this context information about traffic severity is used to pro-actively initiate load balancing at corresponding site and release resources. Further, appropriate small cells are activated/deactivated based on formation/dispersion of traffic jams respectively. The simulation results exhibit substantial reductions in dropping and blocking of users, demonstrating improved QoE of users.

The application of Mission Critical Machine Type Communication (MC-MTC) in wireless systems is currently a hot research topic. Wireless systems are considered to provide numerous advantages over wired systems in e.g. industrial applications such as closed loop control. However, due to the broadcast nature of the wireless channel, such systems are prone to a wide range of cyber attacks. These range from passive eavesdropping attacks to active attacks like data manipulation or masquerade attacks. Therefore it is necessary to provide reliable and efficient security mechanisms. Some of the most important security issues in such a system are to ensure integrity as well as authenticity of exchanged messages over the air between communicating devices. In the present work, an approach on how to achieve this goal in MC-MTC systems based on Physical Layer Security (PHYSEC) is presented. A new method that clusters channel estimates of different transmitters based on a Gaussian Mixture Model is applied for that purpose. Further, an experimental proof-of-concept evaluation is given and we compare the performance of our approach with a mean square error based detection method.

The design of wireless systems for Mission Critical Machine Type Communication (MC-MTC) is currently a hot research topic. Wireless systems are considered to provide numerous advantages over wired systems in industrial applications for example. However, due to the broadcast nature of the wireless channel, such systems are prone to a wide range of cyber attacks. These range from passive eavesdropping attacks to active attacks like data manipulation or masquerade attacks. Therefore it is necessary to provide reliable and efficient security mechanisms. One of the most important security issue in such a system is to ensure integrity as well as authenticity of exchanged messages over the air between communicating devices in order to prohibit active attacks. In the present work, an approach on how to achieve this goal in MC-MTC systems based on Physical Layer Security (PHYSEC), especially a new method based on keeping track of channel variations, will be presented and a proof-of-concept evaluation is given.

Mobile communication is arguably the most ubiquitously used technology in contemporary world, evolving towards its fifth generation (5G). The key challenges being faced by present day mobile communication are growing number of mobile users and subsequent high traffic volume posed by them. Providing uniform service quality and best quality of experience (QoE) in such dense scenarios is a major motive of 5G. Context awareness is a concept of extracting information from the user and his environment, and utilizing it to optimize user performance. Context awareness is recognized as one of the key pillars in enabling uniform quality of experience for mobile users. For instance, predicting the next cell for user transition, predicting the crowd formation in a cell etc., will assist the basestation to reserve or manage resources and prepare the cell well in advance for a future event, targeting to provide uninterrupted and uniform QoE. This paper investigates context aware procedures with a focus on user mobility, finds commonalities among different procedures and proposes a general framework to support mobility context awareness. The new information and interfaces which are required from various entities (e.g., vehicular infrastructure) are discussed. Further, a context aware resource allocation algorithm is designed, exploiting new interfaces and information arising from vehicular infrastructure. Simulation results demonstrate improvements in user throughput, thus validating the requirement of new interfaces.

Mobile communication is one of the most ubiquitously used technologies in today's world, evolving towards its fifth generation (5G). Amidst increasing number of devices and traffic volume, one of the key focuses of 5G is to provide uniform service quality despite high mobility. In real world scenarios, user mobility is not random but rather direction oriented, based on its origin and destination. Further, several users exhibit repeated mobility patterns on daily basis (e.g., office goers, commuters in public transport etc.). Such mobility is termed as Diurnal mobility. Information of such diurnal mobility can assist in improving prediction accuracy of future user location (e.g., cells, routes). Knowledge of future user location will enable the designing of efficient resource management algorithms, aiming to make great service quality follow the user. In the presented work, information of diurnal mobility is used to enhance the accuracy of mobility prediction (next cell prediction as well as route prediction) in a realistic urban scenario. Further, using this context information about future routes and possible coverage holes in them, efficient resource allocation is done to sustain streaming/full buffer services, even in coverage holes. The simulation results show substantial improvements in user throughput as a result of context aware resource allocation, enabled by diurnal user mobility prediction.

This paper proposes a conceptually novel, adaptive and future-proof 5G mobile network architecture. The proposed architecture enables unprecedented levels of network customisability, ensuring stringent performance, security, cost and energy requirements to be met; as well as providing an API-driven architectural openness, fuelling economic growth through over-the-top innovation. Not following the 'one system fits all services' paradigm of current architectures, the architecture allows for adapting the mechanisms executed for a given service to the specific service requirements, resulting in a novel service- and context-dependent adaptation of network functions paradigm. The technical approach is based on the innovative concept of adaptive (de)composition and allocation of mobile network functions, which flexibly decomposes the mobile network functions and places the resulting functions in the most appropriate location. By doing so, access and core functions no longer (necessarily) reside in different locations, which is exploited to jointly optimize their operation when possible. The adaptability of the architecture is further strengthened by the innovative software-defined mobile network control and mobile multi-tenancy concepts.

Device-to-Device (D2D) communications underlaying cellular network exploits physical proximity of the devices and aims at enhancing resource utilization, coverage, data rates and Quality of Service (QOS). Further, it enables the network operators to offload conventional network routed traffic to direct peer- to-peer links and could also support new application fields, such as Machine-to-Machine (M2M) and Vehicle-to-Vehicle (V2V), communication. In order to enable D2D communication, allocating resources (Physical resource blocks: PRBs) to D2D links by reusing cellular resources is a vital procedure. There are several resource allocation (RA) schemes that facilitate resource reuse of cellular PRBs for D2D communication. In most of these schemes, the crucial information for deciding upon PRB reuse is positions of D2D pairs and cellular users, so that mutual interference can be reduced. Further, there are certain schemes that rely on angular information of users to carry out resource allocation for D2D users. Thus, accuracy of position information plays an important role for employing these RA schemes in real world. This paper discusses RA schemes that are based on position/angular information and evaluates the robustness of location based RA schemes against positioning errors in real world deployment.

In today's world, mobile communication has become a ubiquitously used technology. In real world scenarios such as open air festivals, shopping malls, stadiums or public events, several mobile users gather to form a crowd. This poses high load situation to the respective serving base station at the site of crowd formation, thereby leading to congestion. As a consequence, mobile users are subjected to poor Quality of Experience (QoE) due to high dropping and blocking rates. This paper exploits mobility patterns of the users in neighboring cells to predict formation of crowd at the cell of interest. The work presented here combines concepts of next cell prediction, cluster detection and trajectory prediction to indicate the severity of crowd formation and to predict probable location of crowd formation. This information could be used to design smart and efficient resource management schemes. In the presented work, this context information about formation of crowd is used to proactively trigger load balancing at the predicted site and free up resources. Alternatively, a small cell is activated at the predicted site of crowd formation. The simulation results show substantial reductions in blocking and dropping of users, thereby improving the QoE of users in the crowd.

Device-to-Device (D2D) communication underlaying cellular communications takes advantage of physical proximity of devices to improve coverage, resource utilization, data rates, QoS,and offers network operators the possibility to offload normally network-routed traffic to direct P2P links. This paper presents a novel Resource Allocation (RA) concept for D2D User Equipment(D-UEs) reusing the Physical Resource Blocks (PRBs) of the Cellular UEs (C-UEs). The RA algorithm is based on a virtual sectoring concept that relies on network assisted positioning technologies. By means of system level simulations, we show that this novel RA scheme yields significant performance and efficiency gains. To this end, we show the gains when such a RA scheme is employed along with some potential future directions.

As of today, broadband connectivity in developing countries, particularly in rural areas, has not been realized in a satisfactory manner. Coordinated point-to-point radio networks constitute a reliable and cost-efficient technology for wireless backhauling to remote regions. In mountainous regions like the Alps, such networks are deployed to provide Internet connectivity to remote villages. In Sub-Saharan countries such as Tanzania, they serve as an increasingly popular solution to connect sparsely populated areas. However, due to the remote location of radio nodes, no power grid for reliable energy supply is available. Rather, backhaul nodes are equipped with photovoltaic (PV) modules that charge buffer batteries that in turn supply energy to the network equipment. Without sufficiently dimensioned batteries and solar panels, radio nodes are prone to failure which in turn result in network outages and limited service availability. Hence, the deployed equipment for autarkic power supply has to accommodate for periods of lull or overcast skies. In this context, this paper derives recommendations and general rules on how to dimension power supply components to allow for reliable point-to-point wireless backhauling.

We propose key management schemes for wireless ad-hoc networks by utilising the random and reciprocal variations of the wireless channel. For effective key generation, we propose three different methods of enhancing channel reciprocity with appropriate quantisation schemes. The proposed methods are evaluated using real-world channel measurements and validated for better performance in terms of bit disagreement rate, key generation rate, and test of randomness.

As one of the key technologies for next generation wireless communication systems, Device-to-Device (D2D) communication is able to offer several benefits, e.g. low end-to-end latency and traffic offload. In particular network controlled D2D operation underlaying the primary cellular system, e.g. LTE, and reusing its radio resources, is seen as an appealing option for increasing system capacity. However, a D2D link that reuses radio resources of another link introduces mutual interference in between these two links. In this paper, we firstly propose a network controlled radio resource management (RRM) algorithm that maximizes overall user satisfaction with the assistance of context information. The task of the algorithm is to decide on appropriate operation mode and allocate radio resources to both cellular and D2D links with respect to their channel state information (CSI) and service requirements. Furthermore, the RRM algorithm is extended to account for link priority information. Simulation results demonstrate the substantial performance improvement of our smart algorithm compared with a partial smart algorithm in terms of links satisfaction ratio.

In contrast to synthetic user mobility models, user movements in real-world scenarios are restricted and typically conform to location-specific street layouts. Further in urban areas, user movements depend on traffic laws and behavior of other users. For example, vehicular users are said to stop at red traffic lights or should brake, if a vehicle ahead suddenly stops. Moreover, cellular users moving in these urban environments may face severe and abrupt changes in receive signal levels, which in the worst case result in connection drops. In order to pro-actively prevent these situations, where link rate decreases, handover execution is triggered too late, or even the connection dropped, a context-enhanced user movement prediction scheme is presented in this paper. Further, achievable performance gains using user movement prediction and modeling network deployment, user mobility, and radio propagation in a more realistic manner as envisioned for the next generation of wireless networks 5G are presented.

Coordinated point-to-point radio networks constitute a reliable and cost-efficient technology for wireless backhauling that connect access networks with backbone infrastructures. In mountainous regions like the Alps, such networks are deployed to provide Internet connectivity to remote villages. In Sub-Saharan countries, such as Tanzania, they serve as an increasingly popular solution to connect sparsely populated areas. In these countries, climatic and weather conditions provide continuous solar irradiation, which facilitates power supply with photovoltaic (PV) modules and buffer batteries. During daytime, PV modules provide the power for network operation and charging batteries. At night, batteries supply power to a scaled-down network configuration. Moreover, intricacies such as remote or hardly accessible location of radio nodes or unreliable power grid are effectively dealt with. However, without stringent energy budget control policies, these kinds of networks are prone to outages and failures. As a further option, among others, the deployment of excess battery capacity and larger PV panels can handle periods of lull or overcast (dark) skies. Accounting for the solar irradiation patterns of typical deployment areas of these networks, this work investigates if solar-powered point-to-point radio networks are a feasible and reliable backhauling solution.

The future Fifth Generation (5G) mobile cellular networks that are currently in research phase today enable broad range of services/applications beyond classical mobile communications. One key enabler for Ultra-Reliable services to be integrated into mobile networks is the Reliability of transmission success of a given data packet. This is harder mainly owing to the time-dependent effective link qualities of the communicating devices. However, successful indication of the availability of the instantaneous link quality (e.g., by the device) would allow opportunistic access of ultra reliable services/applications when the link conditions are fair enough. This paper introduces a framework for modeling, predicting and analyzing the theoretical reliability of the wireless link based on factors such as fading, mobility, interference etc. The analysis and prediction is based on the part stress method [1] by assuming time dependent factors as elements/components and their respective Transmission Times To Failure (TTTF). The proposed framework also supports other reliability analysis techniques such as Fault Tree Analysis [2] and Accelerated testing [3] of wireless systems and to improve the components.

For a multi-hop wireless mesh network it is well known that network throughput as well as network energy consumption acts as two major performance metrics. Hence there is a real need to have a thorough study on how one can optimize both objectives simultaneously. In this paper, we take a evolutionary optimization approach to optimize the performance objectives. In order to stress on throughput and energy performance, we assume a simplified link layer scheduling by using orthogonal channels among the data links. We use the multiple reference point approach for the above multi-objective optimization problems which is intrinsically of discrete and combinatorial nature. During the course of approximating the Pareto Frontier, multiple reference points can be used instead of traditional methods. The multiple reference point approach has the benefit of parallelization by finitely partitioning the decision space.

The Link-to-System (L2S) interface concept plays an important role in the evaluation of wireless communication systems in terms of System Level Simulation (SLS). The L2S interface is an abstraction model to predict the Link Level Simulation (LLS) performance and transfers the prediction criterion to the SLS. L2S interface is based on a number of mapping models that predict and abstract the link level performance. It is called Effective SINR Mapping (ESM) model, which plays a major role in the L2S interface. A number of L2S interface models have been analyzed for Single Input Single Output (SISO) based on Orthogonal Frequency Division Multiplexing (OFDM) for various wireless communication systems such as the 3GPP Long Term Evolution [1] and WiMAX systems [2]. This paper is extending the analysis of L2S interface based on Multiple Input Multiple Output (MIMO OFDM) based on LTE system. Several models have been considered for the investigation such as Exponential ESM model (EESM), Mutual Information ESM model (MIESM), Capacity ESM model (CESM), and Logarithmic ESM model (LESM). The analysis has covered various MIMO transmission mode of LTE such as Transmit Diversity (TxD) and Spatial Multiplexing (SM). It has considered the different physical layer configuration for different Modulation and Coding Schemes MCS (from 1 to 15) and different bandwidth transmission mode such 1.4 MHz, 10 MHz, and 20 MHz. The results show that MIESM and EESM give a very high accuracy. Particularly, for small transmission bandwidth mode such as 1.4 MHz, the MIESM model outperform EESM model and gives an error range between ±0.2 and ±0.6 dB for SM and between ±0.1 and ±0.9 dB for TxD. However, the results decrease for large transmission bandwidth, in particular, with high modulation and coding scheme.

Autonomous wireless networks play an increasingly crucial role in today's society. Harsh environmental conditions, challenging deployment topologies, unreliable (or non-existing) power grids, as well as misguided control procedures of communications infrastructures by governmental authorities have made autonomous communication infrastructures a valuable asset for long-term development and wealth of societies. However, such wireless networks are in an early phase of their development and need to incorporate necessary equipment and algorithms for autonomous operation. This paper presents a concept and a concrete simulation scenario showing how a distributed context management system and the data collected and distributed by such a system can improve the performance of an autonomous, coordinated wireless mesh network (ACWMN). Research agrees that it is context awareness that will transform today's networks to incorporate autonomous, coordinated behavior. We further depict a concrete example simulation scenario where the access capacity of an ACWMN with WLAN radio access technology is significantly increased by coordinating user access with upper limits on the number of users that can concurrently request data services.

Coordinated wireless point-to-point networks constitute a reliable and cost-efficient technology for providing backhaul connectivity for access networks in remote or topologically challenging environments, e.g. mountainous regions. Their flexibility allows for joint operation with any kind of access networks (including cellular), temporary or mobile deployment (e.g., for sport or entertainment events), or for full-fledged alternative of wired backhaul infrastructure. This paper introduces a novel approach for optimizing the topology of such coordinated backhaul point-to-point networks based on a range of relevant parameters, among them total network capacity, current load in the access network, energy consumption of the considered topology, battery level of nodes without continuous power supply, as well as fairness of throughput allocation from user perspective. We extend our Backhaul Topology Optimization (BTO) algorithm by exploiting the given heterogeneous data to generate an optimal backhaul topology. We show that our algorithm extends the lifetime of such a backhaul network by up to 20% compared to a standard reference case while at the same time modestly improving the fairness of throughput distribution.

Coordinated wireless point-to-point networks constitute a reliable and cost-efficient technology for providing backhaul connectivity for access networks in remote or topologically challenging environments, e.g. mountainous regions. Their flexibility allows for joint operation with any kind of access networks (including cellular), temporary or mobile deployment (e.g., for sport or entertainment events), or for full-fledged alternative of wired backhaul infrastructure. This paper introduces a novel approach for optimizing the topology of such coordinated backhaul mesh networks based on a range of relevant parameters, among them total network capacity, current load in the access network, energy consumption of the considered topology, as well as battery level of nodes without continuous power supply. We present a Backhaul Topology Optimization (BTO) algorithm that, at its core, generates a pareto-optimal backhaul topology. We show that our algorithm extends the lifetime of such a backhaul network by up to 20% compared to a standard reference case while at the same time keeping network capacity, data throughput, and user outage at satisfactory levels.

In 2020, mobile and wireless traffic volume is expected to increase thousand-fold over 2010 figures. Moreover, an increase in the number of wirelessly-connected devices to counts in the tens of billions will have a profound impact on society. Massive machine communication, forming the basis for the Internet of Things, will make our everyday life more efficient, comfortable and safer, through a wide range of applications including traffic safety and medical services. The variety of applications and data traffic types will be significantly larger than today, and will result in more diverse requirements on services, devices and networks. METIS is set up by leading global players to prepare the migration towards tomorrow's multi-purpose global communication infrastructure, serving humans and things. The main objective of METIS is to lay the foundation for this future global mobile and wireless communications system, and to generate a global consensus here. In particular, METIS will provide new solutions which fit the needs beyond 2020.

The Link-to-System (L2S) Level Interface models play a crucial role in the wireless system evaluations, and the design of cross layer simulation. A number of novel approaches have been suggested for consideration of the next generation of mobile communications. However, there has been no agreement on a generic multi-functional approach as a link to system interface for future wireless systems. There are mainly two mapping metrics have been brought forward, which are Exponential Effective SINR Mapping (EESM) model and Mutual Information Effective SINR Mapping (MIESM) model. These algorithms are able to deal with instantaneous channel realizations as an averaging method, like when techniques such as OFDM multiplexing are considered. However, this paper investigates and elaborates these mapping models for the Long Term Evolution (LTE) system in term of their accuracy. In addition, the effect of different bandwidth schemes of the LTE system on the accuracy prediction of these mapping models are also evaluated. The results show that the accuracy of the models are very good for the small bandwidth schemes and the low modulation and coding schemes (MCS) and it decreases as long as large bandwidth schemes and high modulation and coding schemes are applied.

Mobile Cloud Computing is widely accepted as a concept that can significantly improve the user experience when accessing mobile services. By removing the limitations of mobile devices with respect to storage and computing capabilities and providing a new level of security by a centralized maintenance of security-critical software for e.g. mobile payment applications, it is expected that it will find broad acceptance on the business as well as consumer side. Research indicates that Mobile Cloud Computing will additionally help to make visions of context services become reality. However, Mobile Cloud Computing concepts rely on an always-on connectivity and will need to provide a scalable and - when requested - quality mobile access. ”Intelligent access” schemes meeting these requirements will be discussed in this paper. They exploit the specific information available by the ”Mobile Cloud Controller”, i.e., theusers' location, context, and requested services, and significantly evolve the Heterogeneous Access Management schemes developed for the traditional heterogeneous access scenarios. In order to evaluate the performance of these intelligent radio access management concepts in the framework of mobile cloud computing, a specialized radio network simulator will be introduced. It will be shown that a Mobile Cloud Controller establishing a Context Management Architecture can provide the requested availability and quality of mobile connectivity.

Time-discrete signals with good autocorrelation properties are used in various applications in communications engineering. From an implementation point of view, usually sequences with a small phase alphabet and a maximal energy efficiency, i.e., a uniform envelope, are most favorable. For this reason, known methods as well as several new construction techniques of binary and quadriphase sequences with optimal or best known autocorrelation properties are discussed in this correspondence. In many cases, the achievable correlation properties can be improved significantly if a single zero element per sequence is accepted. These "almost" binary and "almost" quadriphase sequences are considered as well. The optimality criteria used include the maximum absolute sidelobe and the merit factor of the periodic and the odd-periodic autocorrelation function, respectively. For sequence lengths of up to 44 in the binary case and up to 32 in the quadriphase case, the best known parameters obtained by computer search are compared with the constructed results.

This paper presents a multi-cell simulation approach with which the impact of selected link-level aspects on cellular system performance can be investigated. The investigations are carried out for downlink dedicated physical channels (DL-DPCH) of third-generation wideband CDMA (3G WCDMA), specified by the 3GPP standardization body. The simulator incorporates user equipment (UE) that suffers from intra-cell interference of the connected base station (BS) as well as inter-cell interference originating from explicitly, i.e. chip-exact, modeled surrounding BSs. The employed multi-cell environment, based on a radio geometry concept; is described. Coded error performance is shown for various mobile radio channel scenarios, radio geometries and different models for the interference. The question whether inter-cell interference of surrounding BSs can be modeled alternatively by additive white Gaussian noise (AWGN) is addressed. It turns out that in all cases the chip-exact modelling of adjacent BSs is necessary in order to obtain realistic performance results. This holds especially the more the UE moves towards the cell border and beyond. Comparisons with simulations employing the AWGN model for inter-cell interference show that those results are too optimistic at low speeds. Moreover, it is shown that a performance analysis based solely on uncoded (raw) bit error rate is not sufficient, but must be complemented by decoded bit and block error rate figures.

The decorrelating and the linear, minimum mean-squared error (MMSE) detectors for asynchronous code-division multiple-access communications ideally are infinite memory-length detectors. Finite memory approximations of these detectors require the inversion of a correlation matrix whose dimension is given by the product of the number of active users and the length of the processing window. With increasing number of active users or increasing length of the processing window, the calculation of the inverse may soon become numerically very expensive. In this paper, we prove that the decorrelating and the linear MMSE detector can both be realized by linear multi-stage interference cancellation algorithms with ideally an infinite number of stages. It is shown that for serial multi-stage interference cancellation, depending on the signal-to-noise ratio and the number of active users, only a few stages are necessary to obtain the same BER performance as the ideal detectors. Thus, the complexity can be reduced considerably.

In coded aperture imaging the attainable quality of the reconstructed images strongly depends on the choice of the aperture pattern. Optimum mask patterns can be designed from binary arrays with constant sidelobes of their periodic autocorrelation function, the so-called URAs. However, URAs exist for a restricted number of aperture sizes and open fractions only. Using a mismatched filter decoding scheme, artifact-free reconstructions can be obtained even if the aperture array violates the URA condition. A general expression and an upper bound for the signal-to-noise ratio as a function of the aperture array and the relative detector noise level are derived. Combinatorial optimization algorithms, such as the great deluge algorithm, are employed for the design of near-optimum aperture arrays. The signal-to-noise ratio of the reconstructions is predicted to be only slightly inferior to the URA case while no restrictions with respect to the aperture size or open fraction are imposed.

In the past, different multiuser detectors for asynchronous code-division multiple-access communications have been proposed, many of them may be characterized as zero-forcing detectors, e.g., the decorrelation detector. We show that linear interference cancellation schemes are asymptotically zero-forcing which means that they are equivalent to the decorrelating detector if the number of stages approaches infinity. These detectors have been found to be superior to the conventional matched filter detector. However, the design of spreading sequences optimized especially for these receivers has not been considered up to now. Usually, spreading sequences are designed to have a low peak correlation parameter. Pursley (1977) has shown that the average interference parameter (AIP) is an important design parameter since it is related to the average signal-to-interference ratio of the conventional receiver. In this paper, we consider the construction of spreading sequences for zero-forcing multiuser detectors that are optimal in the sense of performance and near-far resistance. It is shown that sequences with a low AIP are near-optimal. This, again, stresses the importance of the AIP for the design of spreading sequences for CDMA systems employing any kind of receiver. Numerical examples indicate that by using optimized sequences the average signal-to-noise ratio (SNR) can be improved by about 1-2 dB for lengths of interest in applications.

Perfect arrays are defined by having zero out-of-phase autocorrelation sidelobes. Such arrays find many applications, e.g. in two-dimensional synchronization techniques. With a known construction method, it is shown that perfect phase arrays can be obtained for every size. However, the phase alphabets of these constructed phase arrays increase by the size of the arrays. With further construction schemes the phase alphabet of perfect phase arrays can be reduced for particular sizes. Classes of perfect phase arrays are obtained whose phase alphabet does not increase with the size of the arrays.>