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Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC.

Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle-to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. Learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems., Comment: 6 pages, 2 figures, accepted for publication at IEEE ICMLA 2023

This paper provides an overview of the hardware and software components used in our test bed project the NET Playground. All source information is stored in the GitLab repository (https://gitlab.com/Paulteck/net-playground). In the Hardware section, we present sketches and 3D views of mechanical parts and technical drawings of printed boards. The Software section discusses relay control using shell scripts and the utilization of Ansible for automation. We also introduce a C++ framework for connecting with the INA231 energy sensor. This paper serves as a reference for understanding and replicating our project's hardware and software components.

Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction. Portable devices such as inertial measurement units (IMUs) are applicable to monitor human motions, while in practice often limited computational power is available locally. The human motion in task space coordinates requires not only the human joint motion but also the nonlinear coordinate transformation depending on the parameters such as human limb length. In most applications, measuring these kinematics parameters for each individual requires undesirably high effort. Therefore, it is desirable to estimate both, the human motion and kinematic parameters from IMUs. In this work, we propose a novel computational framework for dual estimation in real-time exploiting in-network computational resources. We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process. In order to further accelerate the convergence, the parameter estimation is progressively computed on multiple networked computational nodes. The superiority of our proposed method is demonstrated by a simulation of a human arm, where the estimation accuracy is shown to converge faster than with conventional approaches.

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments., Comment: 18 pages, 12 Figures. Accepted on IEEE Access

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 onboarding 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., Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2x

Robust, reliable, and deterministic networks are essential for a variety of applications. In order to provide guaranteed communication network services, Time-Sensitive Networking (TSN) unites a set of standards for time-synchronization, flow control, enhanced reliability, and management. We design the TSN-FlexTest testbed with generic commodity hardware and open-source software components to enable flexible TSN measurements. We have conducted extensive measurements to validate the TSN-FlexTest testbed and to examine TSN characteristics. The measurements provide insights into the effects of TSN configurations, such as increasing the number of synchronization messages for the Precision Time Protocol, indicating that a measurement accuracy of 15 ns can be achieved. The TSN measurements included extensive evaluations of the Time-aware Shaper (TAS) for sets of Tactile Internet (TI) packet traffic streams. The measurements elucidate the effects of different scheduling and shaping approaches, while revealing the need for pervasive network control that synchronizes the sending nodes with the network switches. We present the first measurements of distributed TAS with synchronized senders on a commodity hardware testbed, demonstrating the same Quality-of-Service as with dedicated wires for high-priority TI streams despite a 200% over-saturation cross traffic load. The testbed is provided as an open-source project to facilitate future TSN research., Comment: 30 pages, 18 figures, 6 tables, IEEE TNSM, in print, 2024. Shorter version in print in IEEE Trans. on Network and Service Management (see related DOI below)

In existing mobile network systems, the data plane (DP) is mainly considered a pipeline consisting of network elements end-to-end forwarding user data traffics. With the rapid maturity of programmable network devices, however, mobile network infrastructure mutates towards a programmable computing platform. Therefore, such a programmable DP can provide in-network computing capability for many application services. In this paper, we target to enhance the data plane with in-network deep learning (DL) capability. However, in-network intelligence can be a significant load for network devices. Then, the paradigm of the functional split is applied so that the deep neural network (DNN) is decomposed into sub-elements of the data plane for making machine learning inference jobs more efficient. As a proof-of-concept, we take a Blind Source Separation (BSS) problem as an example to exhibit the benefits of such an approach. We implement the proposed enhancement in a full-stack emulator and we provide a quantitative evaluation with professional datasets. As an initial trial, our study provides insightful guidelines for the design of the future mobile network system, employing in-network intelligence (e.g., 6G).

The advent of 5G and the design of its architecture has become possible because of the previous individual scientific works and standardization efforts on cloud computing and network softwarization. Software-defined Networking and Network Function Virtualization started separately to find their convolution into 5G network architecture. Then, the ongoing design of the future beyond 5G (B5G) and 6G network architecture cannot overlook the pivotal inputs of different independent standardization efforts about autonomic networking, service-based communication systems, and multi-access edge computing. This article provides the design and the characteristics of an agent-based, softwarized, and intelligent architecture, which coherently condenses and merges the independent proposed architectural works by different standardization working groups and bodies. This novel work is a helpful means for the design and standardization process of the futureB5G and 6G network architecture., Comment: 7 pages, 6 figures

We consider the problem of remotely tracking the state of and unstable linear time-invariant plant by means of data transmitted through a noisy communication channel from an algorithmic point of view. Assuming the dynamics of the plant are known, does there exist an algorithm that accepts a description of the channel's characteristics as input, and returns 'Yes' if the transmission capabilities permit the remote tracking of the plant's state, 'No' otherwise? Does there exist an algorithm that, in case of a positive answer, computes a suitable encoder/decoder-pair for the channel? Questions of this kind are becoming increasingly important with regards to future communication technologies that aim to solve control engineering tasks in a distributed manner. In particular, they play an essential role in digital twinning, an emerging information processing approach originally considered in the context of Industry 4.0. Yet, the abovementioned questions have been answered in the negative with respect to algorithms that can be implemented on idealized digital hardware, i.e., Turing machines. In this article, we investigate the remote state estimation problem in view of the Blum-Shub-Smale computability framework. In the broadest sense, the latter can be interpreted as a model for idealized analog computation. Especially in the context of neuromorphic computing, analog hardware has experienced a revival in the past view years. Hence, the contribution of this work may serve as a motivation for a theory of neuromorphic twins as a counterpart to digital twins for analog hardware.

As we are more and more dependent on the communication technologies, resilience against any attacks on communication networks is important to guarantee the digital sovereignty of our society. New developments of communication networks tackle the problem of resilience by in-network computing approaches for higher protocol layers, while the physical layer remains an open problem. This is particularly true for wireless communication systems which are inherently vulnerable to adversarial attacks due to the open nature of the wireless medium. In denial-of-service (DoS) attacks, an active adversary is able to completely disrupt the communication and it has been shown that Turing machines are incapable of detecting such attacks. As Turing machines provide the fundamental limits of digital information processing and therewith of digital twins, this implies that even the most powerful digital twins that preserve all information of the physical network error-free are not capable of detecting such attacks. This stimulates the question of how powerful the information processing hardware must be to enable the detection of DoS attacks. Therefore, in the paper the need of neuromorphic twins is advocated and by the use of Blum-Shub-Smale machines a first implementation that enables the detection of DoS attacks is shown. This result holds for both cases of with and without constraints on the input and jamming sequences of the adversary., Comment: submitted for publication

The trend of future communication systems is to aim for the steering and control of cyber physical systems. These systems can quickly become congested in environments like those presented in Industry 4.0. In these scenarios, a plethora of sensor data is transmitted wirelessly to multiple in network controllers that compute the control functions of the cyber physical systems. In this paper, we show an implementation of network Functional Compression (FC) as a proof of concept to drastically reduce the data traffic in these scenarios. FC is a form of goal-oriented communication scheme in which the objective of the sender receiver pair is to transmit the minimum amount of information to compute a function at the receiver end. In our scenario, the senders transmit an encoded and compressed version of the sensor data to a destination, an in-network controller interested in computing as its target function, a PID controller. We show that it is possible to achieve compression rates of over 50% in some cases by employing FC. We also show that using FC in a distributed cascade fashion can achieve more significant compression rates while reducing computational costs.

We consider the task of converting different digital descriptions of analog bandlimited signals and systems into each other, with a rigorous application of mathematical computability theory. Albeit very fundamental, the problem appears in the scope of digital twinning, an emerging concept in the field of digital processing of analog information that is regularly mentioned as one of the key enablers for next-generation cyber-physical systems and their areas of application. In this context, we prove that essential quantities such as the peak-to-average power ratio and the bounded-input/bounded-output norm, which determine the behavior of the real-world analog system, cannot generally be determined from the system's digital twin, depending on which of the above-mentioned descriptions is chosen. As a main result, we characterize the algorithmic strength of Shannon's sampling type representation as digital twin implementation and also introduce a new digital twin implementation of analog signals and systems. We show there exist two digital descriptions, both of which uniquely characterize a certain analog system, such that one description can be algorithmically converted into the other, but not vice versa.

As space agencies are planning manned missions to reach Mars, researchers need to pave the way for supporting astronauts during their sojourn. This will also be achieved by providing broadband and low-latency connectivity through wireless network infrastructures. In such a framework, we propose a Martian deployment of a 3-Dimensional (3D) network acting as Cloud Radio Access Network (C-RAN). The scenario consists, mostly, of unmanned aerial vehicles (UAVs) and nanosatellites. Thanks to the thin Martian atmosphere, CubeSats can stably orbit at very-low-altitude. This allows to meet strict delay requirements to split functions of the baseband processing between drones and CubeSats. The detailed analytical study, presented in this paper, confirmed the viability of the proposed 3D architecture, under some constraints and trade-off concerning the involved Space communication infrastructures, that are discussed in detail., Comment: Initial version submitted to IEEE Transactions on Aerospace and Electronic Systems

5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix low-volume application, in particular at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases., Comment: 10 pages; submitted to 6th International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production, 7-9 September Genova, Italy

Modern manufacturers are currently undertaking the integration of novel digital technologies - such as 5G-based wireless networks, the Internet of Things (IoT), and cloud computing - to elevate their production process to a brand new level, the level of smart factories. In the setting of a modern smart factory, time-critical applications are increasingly important to facilitate efficient and safe production. However, these applications suffer from delays in data transmission and processing due to the high density of wireless sensors and the large volumes of data that they generate. As the advent of next-generation networks has made network nodes intelligent and capable of handling multiple network functions, the increased computational power of the nodes makes it possible to offload some of the computational overhead. In this paper, we show for the first time our IA-Net-Lite industrial anomaly detection system with the novel capability of in-network data processing. IA-Net-Lite utilizes intelligent network devices to combine data transmission and processing, as well as to progressively filter redundant data in order to optimize service latency. By testing in a practical network emulator, we showed that the proposed approach can reduce the service latency by up to 40%. Moreover, the benefits of our approach could potentially be exploited in other large-volume and artificial intelligence applications.

Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level -- Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services.

This article summarises the current status of classical communication networks and identifies some critical open research challenges that can only be solved by leveraging quantum technologies. By now, the main goal of quantum communication networks has been security. However, quantum networks can do more than just exchange secure keys or serve the needs of quantum computers. In fact, the scientific community is still investigating on the possible use cases/benefits that quantum communication networks can bring. Thus, this article aims at pointing out and clearly describing how quantum communication networks can enhance in-network distributed computing and reduce the overall end-to-end latency, beyond the intrinsic limits of classical technologies. Furthermore, we also explain how entanglement can reduce the communication complexity (overhead) that future classical virtualised networks will experience., Comment: 7 pages, 4 figures

While 5G mobile communication systems are currently in deployment, researchers around the world have already started to discuss 6G technology and funding agencies started their first programs with a 6G label. Although it may seem like a good idea from a historical point of view with returning generations every decade, this contribution will show that there is a great risk of introducing 6G labels at this time. While the reasons to not talk about 6G yet are manifold, some of the more dominant ones are i.) there exists a lack of real technology advancements introduced by a potential 6G system; ii.) the flexibility of the 5G communication system introduced by softwarization concepts, such as in the Internet community, allows for daily updates; and iii.) introducing widespread 6G discussions can have a negative impact on the deployment and evolution of 5G with completely new business cases and customer ecosystems compared to its predecessors. Finally, as we do not believe that 5G is the end of our journey, we will provide an outlook on the future of mobile communication systems, independent of the current mainstream discussion.

Well-known error detection and correction solutions in wireless communications are slow or incur high transmission overhead. Recently, notable solutions like PRAC and DAPRAC, implementing partial packet recovery with network coding, could address these problems. However, they perform slowly when there are many errors. We propose S-PRAC, a fast scheme for partial packet recovery, particularly designed for very noisy wireless channels. S-PRAC improves on DAPRAC. It divides each packet into segments consisting of a fixed number of small RLNC encoded symbols and then attaches a CRC code to each segment and one to each coded packet. Extensive simulations show that S-PRAC can detect and correct errors quickly. It also outperforms DAPRAC significantly when the number of errors is high.

Novel use cases and verticals such as connected cars and human-robot cooperation in the areas of 5G and Tactile Internet can significantly benefit from the flexibility and reduced latency provided by Network Function Virtualization (NFV) and Multi-Access Edge Computing (MEC). Existing frameworks managing and orchestrating MEC and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework. Whereas, the latter leads to inefficient use of computation resources because information are not shared. We introduce APMEC, a dedicated framework for MEC while enabling the collaboration with the management and orchestration (MANO) frameworks for NFV. The new design allows to reuse allocated network services, thus maximizing resource utilization. Measurement results have shown that APMEC can allocate up to 60% more number of network services. Being developed on top of OpenStack, APMEC is an open source project, available for collaboration and facilitating further research activities.

We use random linear network coding (RLNC) based scheme for multipath communication in the presence of lossy links with different delay characteristics to obtain ultra-reliability and low latency. A sliding window version of RLNC is proposed where the coded packets are generated using packets in a window size and are inserted among systematic packets in different paths. The packets are scheduled in the paths in a round robin fashion proportional to the data rates. We use finite encoding and decoding window size and do not rely on feedback for closing the sliding window, unlike the previous work. Our implementation of two paths with LTE and WiFi characteristics shows that the proposed sliding window scheme achieves better latency compared to the block RLNC code. It is also shown that the proposed scheme achieves low latency communication through multiple paths compared to the individual paths for bursty traffic by translating the throughput on both the paths into latency gain.

Based on the impressive features that network coding and compressed sensing paradigms have separately brought, the idea of bringing them together in practice will result in major improvements and influence in the upcoming 5G networks. In this context, this paper aims to evaluate the effectiveness of these key techniques in a cluster-based wireless sensor network, in the presence of temporal and spatial correlations. Our goal is to achieve better compression gains by scaling down the total payload carried by applying temporal compression as well as reducing the total number of transmissions in the network using real field network coding. In order to further reduce the number of transmissions, the cluster-heads perform a low complexity spatial pre-coding consisting of sending the packets with a certain probability. Furthermore, we compare our approach with benchmark schemes. As expected, our numerical results run on NS3 simulator show that on overall our scheme dramatically drops the number of transmitted packets in the considered cluster topology with a very high reconstruction SNR., Comment: Submitted to VTC Spring 2018