Open radio access network (O-RAN) is a paradigm shift in telecommunications, facilitating interoperability and innovation through the disaggregation of traditional monolithic architecture, empowering operators to select equipment from diverse vendors. However, within the multi-vendor O-RAN ecosystem, individual xApps may pursue conflicting objectives. While fine-tuned coordination can alleviate conflicts, it often requires extensive information exchange, raising privacy concerns among competing vendors. This paper delves into these challenges, particularly focusing on the interplay between different xApps, such as energy efficiency (EE) and load balancing (LB), and highlights the tradeoff between performance and level of coordination. To address this, we propose novel algorithms to optimize performance across varying levels of coordination. Initial findings underscore the diminishing returns of coordination, with significant performance gains from zero to partial coordination, yet a more modest increase with full coordination.

This paper presents a dual-functional waveform design approach for integrated sensing and communication systems with a base station simultaneously communicating with multiple downlink users and illuminating directions of interest for radar sensing. The joint design goals include matching a desired beam-pattern for sensing and minimization of interference between users for communication, where the trade-off between the two goals is controlled by a tuning parameter. The proposed approach also efficiently integrates practical constraints on per-antenna powers and peak-to-average-power-ratio into the design. Through simulations, we show that the proposed approach achieves lower symbol error rate and better beampattern matching performance compared to the popular baseline state-of-the-art method.

This paper addresses load balancing in open radio access networks (O-RAN), which aims to enhance network avail-ability without overloading the network when accommodating new user equipment (UEs) while ensuring an efficient allocation of resources to meet the data rate requirement of existing UEs. More precisely, we propose a resource allocation framework that balances the utilization of resource blocks (PRBs) at the radio units (RU s) as well as the computational resources at the distributed units (DUs) while maintaining the quality of service (QoS) demands of UEs. Given the combinatorial nature of the optimization problems, we propose, 1) a supermodular algorithm to find UE-RU assignments and 2) a job scheduling-inspired method to assign RUs to respective DUs. Through comprehensive simulations, we validate the effectiveness of our approach by showcasing substantial enhancements in the network load con-ditions and highlighting the superiority of the provided resource allocation scheme in terms of key performance indicators such as the call block ratio (CBR).

The Open RAN paradigm offers data-driven, intelligent optimization of the radio access network (RAN). The disaggregated nature of the Open RAN combined with virtualization on general-purpose CPUs with limited computation capacity creates different load types at multiple levels, making it more challenging to balance the load within the network. This paper proposes a learning framework that learns the assignment of users (UEs) to network nodes to balance the communication and computation load in the network. The framework incorporates communication resources consumed by the users in the radio unit (RU), and computation resources needed for baseband processing in the virtualized distributed unit (DU). The goal is thus to balance the communication load between RUs and the computation load between DUs to avoid overloading network elements or to handle higher peak data rate demands when new users arrive in the network. We apply a novel utility-based approach to jointly optimize the UE-RU and RU-DU assignments taking into account the users' QoS (quality of service) requirements. Simulations demonstrate that the proposed method generates the assignments that significantly improve the network load conditions compared to baseline schemes, thereby enabling more available communication and computation resources for incoming peak data rate users in the network.

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.

Due to the lack of a feedback channel in the C-V2X sidelink, finding a suitable modulation and coding scheme (MCS) is a difficult task. However, recent use cases for vehicle-to-everything (V2X) communication with higher demands on data rate necessitate choosing the MCS adaptively. In this paper, we propose a machine learning approach to predict suitable MCS levels. Additionally, we propose the use of quantile prediction and evaluate it in combination with different algorithms for the task of predicting the MCS level with the highest achievable data rate. Thereby, we show significant improvements over conventional methods of choosing the MCS level. Using a machine learning approach, however, requires larger real-world data sets than are currently publicly available for research. For this reason, this paper presents a data set that was acquired in extensive drive tests, and that we make publicly available.

To enable safety-related applications for autonomous driving, Vehicle-to-Everything (V2X) networks are required to support the dissemination of real-time status updates among neighboring vehicles. In this scenario, the ‘freshness’ of information with respect to an application-specific threshold is crucial for the availability of the application. In this paper, we rely on the Age of Information (AoI) as a metric that quantifies the availability of safety-related applications in V2X. Under the assumption of a distributed reservation-based channel access, we derive a probabilistic model that allows us to characterize the statistical properties of AoI and, consequently, evaluate the availability of the application in question. The analytical derivations are validated via numerical simulations.

With the advent of 5G, network lifecycle operations such as service initial deployment, configuration changes, upgrades, optimization, and self-healing to name a few, should be fully automated processes to reduce capital expenditure (CAPEX) and operational expenditure (OPEX), and also to allow new players such as industry owners, to come into the scene as nontraditional network operators. To this end, self-organized networks functions (SF) have been proposed as a first attempt to provide self-adaptation capabilities to mobile networks on different fronts and to reduce the error-prone human intervention. Nevertheless, deploying multiple optimization functions in a network brings demanding challenges in terms of conflicting objectives in coordination. Automatically coordinating all those functions is paramount for industry owners in campus networks (CN) since they often do not have a deep expertise to carry out network optimization in an agile manner. Typically, each SF aim at individual goals modifying coupled network parameters, generally in dissonant directions with respect to other SF, jeopardizing the global stability of the system. This work presents an explicit formulation of the joint optimization problem when load balancing optimization (LBO) and coverage and capacity optimization (CCO) are instantiated in a CN.

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.

The possibility of predicting Quality of Service, and particularly data rates, in mobile networks will enable new applications for future automated and connected mobility, such as teleoperated driving. Since network data is difficult to acquire and usually of low granularity, robust prediction approaches are required that need to be trained with data sets and measurements generated by end devices. In this paper, we present uplink and downlink data sets, measured in extensive drive tests, that are made available for the evaluation of machine learning methods. Based on this data, we compare the data rate prediction performance in uplink and downlink for neural network, random forest and gradient boosting approaches. Our results show a significantly higher achievable accuracy in uplink than in downlink, and that, even with reduced feature sets, gradient boosting is particularly suited for the prediction task. Moreover, we investigate the use of quantile estimation methods for predicting bounds on the achievable data rate. We show that conformalized approaches, both based on neural networks and random forests, can predict quantiles with very high accuracy.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices are supposed to be delivered to the gateway with low latency and high reliability. However, most of industrial wireless standards are based on IEEE 802.15.4 and offer limited data rates, which prevents their adoption in critical scenarios. Based on IEEE 802.11, WIA-FA is proposed to address higher communication requirements in factory automation. In this paper, we first analyze the drawbacks of the default NACK-based retransmission scheme of WIA-FA, and then propose an automatic on-demand retransmission (AODR) scheme. Finally, we give a detailed reliability analysis of the proposed AODR scheme. Simulation results show that the proposed AODR scheme outperforms existing works in terms of reliability for different scenarios.

The centralized computing model in traditional Wireless Sensor/Actuator Networks (WSANs) can lead to large delays and unbalances, which severely restricts the adoption of WSANs in applications requiring high network performance. To address this limitation, the in-network computation model has been proposed, in which the computation capability is distributed among wireless nodes in WSANs, i.e., wireless nodes perform not only data communication but also data processing. Node placement is a primary step to build the underlaying topologies of WSANs. Nevertheless, the problem of node placement to design underlaying network topologies supporting in-network computation is still unexplored. To this end, we propose an In-network-oriented Node Placement Algorithm (INPA) to build WSANs supporting in-network computation. Moreover, we investigate the time complexity of INPA and verify the efficiency of INPA through extensive simulations.

Direct communication between vehicles using the sidelink is becoming increasingly important, but there is a lack of opportunities for performance evaluation, testing of new features and measurement data acquisition. In this paper, we present an open source implementation of the 3GPP C-V2X sidelink Mode 4. The implementation is standard-compliant and provides additional measurement and configuration interfaces that enable its use as an evaluation and measurement platform. We integrate our implementation in a software defined radio (SDR) based hardware platform that allows for practical outdoor use at different frequencies. In addition, we verify the performance of the SDR-based sidelink setup using a hardware channel emulator, and we compare the packet error rate performance against available performance curves in literature. Finally, we show the benefits of our platform over simulations by using the realistic SDR-based setup to generate exemplary reference curves for different 3GPP channels.

We propose a novel utility optimization algorithm for wireless networks modeled by systems of nonlinear equations based on the load at the base stations. Unlike previous studies, the algorithm solves a max-min utility optimization problem over the joint space of network load, transmit power, and rates. In more detail, our first main contribution is to show that, in the optimum, users operate at the same rate, base stations are fully loaded, and at least one base station transmits at the maximum power. This characterization of the optimal solution enables a reformulation of the optimization task as a conditional eigenvalue problem associated with a concave mapping that relates the transmit power to the network load. With this reformulation, an efficient iterative solver becomes readily available. Our second main contribution is the derivation of a simple lower bound for conditional eigenvalues of general positive concave mappings. These bounds are of particular interest to network designers, because conditional eigenvalues can often be related to the optimal rates (or the optimal signal-to-interference-noise ratio) of a large class of utility optimization problems, and, in this paper, these bounds are used to derive performance limits of load coupled networks.

LTE and LTE-Advanced have been optimized to deliver high bandwidth pipes to wireless users. The transport mechanisms have been tailored to maximize single cell performance by enforcing strict synchronism and orthogonality within a single cell and within a single contiguous frequency band. Various emerging trends reveal major shortcomings of those design criteria: (1) The fraction of machine-type-communications (MTC) is growing fast. Transmissions of this kind are suffering from the bulky procedures necessary to ensure strict synchronism. (2) Collaborative schemes have been introduced to boost capacity and coverage (CoMP), and wireless networks are becoming more and more heterogeneous following the non-uniform distribution of users. Tremendous efforts must be spent to collect the gains and to manage such systems under the premise of strict synchronism and orthogonality. (3) The advent of the Digital Agenda and the introduction of carrier aggregation are forcing the transmission systems to deal with fragmented spectrum. 5GNOW will question the design targets of LTE and LTE-Advanced having these shortcomings in mind. The obedience of LTE and LTE-Advanced to strict synchronism and orthogonality will be challenged. It will develop new PHY and MAC layer concepts being better suited to meet the upcoming needs with respect to service variety and heterogeneous transmission setups. A demonstrator will be built as Proof-of-Concept relying upon continuously growing capabilities of silicon based processing. Wireless transmission networks following the outcomes of 5GNOW will be better suited to meet the manifoldness of services, device classes and transmission setups being present in envisioned future scenarios like smart cities. The integration of systems relying heavily on MTC, e.g. sensor networks, into the communication network will be eased. The per-user experience will be more uniform and satisfying. To ensure this 5GNOW will contribute to upcoming 5G standardization.

The control of large queueing networks is a notoriously difficult problem. Recently, an interesting new policy design framework for the control problem called h-MaxWeight has been proposed: h-MaxWeight is a natural generalization of the famous MaxWeight policy where instead of the quadratic any other surrogate value function can be applied. Stability of the policy is then achieved through a perturbation technique. However, stability crucially depends on parameter choice which has to be adapted in simulations. In this paper we use a different perturbation technique where the required properties are much easier to implement. Specifically, we derive the theoretical fundamentals which guarantee universal stability while still operating `close' to the underlying cost criterion. Simulation examples suggest that the new approach to policy synthesis can provide significantly higher gains irrespective of any further assumptions on the network model or parameter choice.