In industrial multi-access edge computing (MEC), novel collaborative systems involving multiple automated guided vehicles (AGVs) require the execution of both critical tasks and computational tasks, such as AI-based collision avoidance. However, previous research focused mainly on scheduling process-related tasks and overlooked computational tasks. Moreover, scheduling tasks between AGVs in collaborative systems poses an integer problem, which is challenging to solve in polynomial time, highlighting the need for computationally efficient algorithms. This paper proposes a deep reinforcement learning (DRL)-based approach to multi-task scheduling (DRL-MTS) in multi-AGV systems. It involves dynamically applying a catalog of DRL models, each tailored to different numbers of AGVs. Evaluation results demonstrate that the proposed inter-AGV DRL-MTS strategy closely approaches the optimal solution, reaching up to 96% task completion compared to a 98% of the optimal solution, while significantly reducing decision times. Moreover, the training time for these models has been reduced threefold using datasets from existing optimization solvers, and transfer learning has further cut training times by up to 51%.

In cloud-native environments, executing vehicle-to-everything (V2X) tasks in edge nodes close to users significantly reduces service end-to-end latency. Containerization further reduces resource and time consumption, and, subsequently, application latency. Since edge nodes are typically resource and energy-constrained, optimizing offloading decisions and managing edge energy consumption is crucial. However, the offloading of containerized tasks has not been thoroughly explored from a practical implementation perspective. This paper proposes an optimization framework for energy-aware offloading of V2X tasks implemented as Kubernetes pods. A weighted utility function is derived based on cumulative pod response time, and an edge-to-cloud offloading decision algorithm (ECODA) is proposed. The system's energy cost model is derived, and a closed-loop repeated reward-based mechanism for CPU adjustment is presented. An energy-aware (EA)-ECODA is proposed to solve the offloading optimization problem while adjusting CPU usage according to energy considerations. Simulations show that ECODA and EA-ECODA outperform first-in, first-served (FIFS) and EA-FIFS in terms of utility, average pod response time, and resource usage, with low computational complexity. Additionally, a real testbed evaluation of a vulnerable road user application demonstrates that ECODA outperforms Kubernetes vertical scaling in terms of service-level delay. Moreover, EA-ECODA significantly improves energy usage utility.

Resource distribution among diverse administrative domains, network operators, and geographical locations across the edge-to-cloud continuum requires suitable communication and management and orchestration (MANO) mechanisms among orchestration domains. In multi-access edge computing (MEC) environments, efficient application lifecycle management and system federation are essential for scalability, optimal resource utilization, and ensuring service continuity and reliability. Existing orchestration solutions, typically designed for centralized cloud architectures, often fall short in accommodating application delay requirements and in managing the dynamic and distributed nature of MEC resources effectively. This paper introduces a cloud-native, platform-agnostic MEC Orchestrator (MEO) with enhancements over the ETSI MEC architecture, albeit aligned with GSMA and ETSI MEC federation standards, that supports cross-platform MANO and resource controllability. Federation is supported through a new MEO-to-MEO interface that enables application migration across MEC systems. Experimental results demonstrate a 95% instantiation success rate for instantiation, overperforming the baseline Kubernetes scheduler, and 93.3% for migration requests within federated MEC systems in high request volume scenarios.

Quantum networks present various research challenges that need to be addressed for the development of future quantum Internet architectures, such as the interconnection of large numbers of end devices, and remote long distance connection. The recursive inter-network architecture (RINA) introduces great flexibility, scalability and a set of principles that can be applied to quantum networks, yielding a network model based on RINA, which we refer to as RINA-based quantum network. Such approach offers an appropriate solution to address the challenges posed by quantum networks. This paper presents the authors' vision on the potential of RINA-based quantum networks to advance the quantum Internet. First, a model based on packet switching is proposed to carry quantum payloads in local-area scenarios. Then, a RINA-based approach for entanglement-based teleportation is presented for wide-area scenarios. Last, a full RINA-based quantum network model for end-to-end communication is proposed. The proposed model enables the interconnection of packet-switching networks and entanglement-based networks. To the best of the authors' knowledge, this is the first attempt at designing a RINA-based quantum inter-network model.

Cooperative, Connected and Automated Mobility (CCAM) and Future Railway Mobile Communications Systems (FRMCS) services usually require uninterrupted seamless connectivity. However, in cross-border scenarios, legacy roaming techniques lead to interruption times in the range of one to two minutes, which results unsuitable for the provisioning of demanding CAM and FRMCS services. This paper presents the implementation of state-of-the-art roaming optimization techniques in both the 5G Core and radio access network, including a novel radio optimization handover mechanism for home-routed roaming (HRR) that enables the completion of roaming procedures in 5G Standalone (SA) networks with short interruption times. In addition, a network key performance indicator (KPI) tool is presented to measure the network performance in terms of latency, throughput, and interruption time during roaming. Unlike previous works, static and dynamic performance evaluations are performed in a realistic environment over 5G SA networks deployed on the Mediterranean cross-border corridor between Spain and France. The proposed optimization mechanism yields average interruption time measurements in the range between 135ms and 155ms, enabling seamless service continuity in crossborder scenarios.

By pushing resources to far-edge servers located in the proximity of users, edge computing can greatly reduce end-to-end transmission delays. Task offloading in multi-tier networks refers to the optimization of which tasks should be offloaded from the far-edge to the edge and the cloud. Moreover, the containerization of applications can further reduce resource and time consumption and, in turn, the latency of such applications. Even though Kubernetes has become the de facto container orchestrator, not many works have considered the offloading of containerized applications in Kubernetes clusters spanning from cloud to far-edge. In this work, the problem of offloading Kubernetes tasks (or pods) in three-tier networks is formulated and optimized. First, a utility function is presented in terms of the cumulative weighted pod response time, and a utility minimization problem with central processing unit (CPU) constraints is presented. Based on the optimal theoretical solution to this problem, a three-tier offloading decision algorithm (TTODA) is developed. Horizontal scaling is considered, and specific hardware capabilities of each node are taken into account by setting specific SLAs that are fed back to the algorithm. Numerical results show that TTODA outperforms a typical Kubernetes QoS model based on first-in, first-served algorithm (FIFSA) in terms of utility, average pod response time, and usage of far-edge CPU. Further, TTODA achieves an excellent trade-off between performance and computational complexity, and thus it can help achieve the requirements of latency-sensitive applications. Moreover, TTODA can easily be extended to scenarios with joint memory and CPU constraints.

The application of non-orthogonal multiple access (NOMA) to multicarrier systems can improve the spectrum efficiency and enable massive connectivity in future mobile systems. Resource allocation in multicarrier NOMA systems is a non-deterministic polynomial time-hard problem requiring exhaustive search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed. In this paper, exact values of the optimal channel gain ratios between a pair of NOMA users are presented for the first time for quadrature amplitude modulation (QAM) schemes. Further, numerical limits are derived for the values of channel gain ratios that fulfill the system constraints. These findings are used to propose a user pairing algorithm with quasi-linear complexity. Further, a novel scheme for data rate and continuous power allocation is proposed. Through numerical simulations, it is proved that the proposed scheme yields an achievable sum-rate close to the performance of exhaustive search, and it outperforms other suboptimal resource allocation schemes.

The application of non-orthogonal multiple access (NOMA) to multicarrier (MC) systems can improve the spectrum efficiency and enable massive connectivity in future mobile communication systems. Resource allocation in MC NOMA is a non-deterministic polynomial time (NP)-hard problem with prohibitive computational complexity. Thus, efficient algorithms that provide a good trade-off between performance and complexity are needed. In this paper, exact values of the optimal channel gain ratios between a pair of NOMA users are presented for quadrature amplitude modulation (QAM). Further, numerical limits are derived for the values of channel gain ratios that fulfill the system constraints. Unlike previous works, it is demonstrated that the benefit of pairing users with very distinct channel gains is lost under practical QAM schemes, due to the inability of users with poor channel conditions to fulfill bit error rate (BER) constraints. These findings are used to propose a user pairing algorithm with quasi-linear complexity. Further, a novel scheme for data rate and continuous power allocation is proposed. Through numerical simulations, it is proved that the proposed scheme yields an achievable sum-rate close to the performance of exhaustive search, and it outperforms other suboptimal resource allocation schemes.

The problem of resource allocation in multicarrier non-orthogonal multiple access (NOMA) systems is non-deterministic polynomial time (NP)-hard and requires exhaustive search, which has prohibitive computational complexity. Computationally-efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into two sub-problems, namely subcarrier assignment and power allocation. The optimal users' channel condition relationship is derived in terms of effective system sum-rate, and a computationally-efficient ideal partner search algorithm (IPSA) is proposed. Further, optimal power allocation (OPA) is studied in terms of fairness, and an OPA coefficient is theoretically derived based on the channel condition ratio of two NOMA users. Numerical results show that IPSA with OPA outperforms IPSA with fractional transmit power allocation (FTPA), the strongest user-weakest user (SUWU) scheme and user grouping (UG) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER. Further, the computational complexity of IPSA with OPA is much lower than that of exhaustive search.

The problem of resource allocation in non-orthogonal multiple access (NOMA) systems is nondeterministic polynomial time (NP)-hard and requires exhaustive search. Hence, fast and computationally efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into the sub-problems of subcarrier assignment and power allocation. The optimal users' channel condition relationship is derived in terms of the effective system sum-rate, and a fast ideal partner search algorithm (IPSA) is proposed in terms of that relationship. The performance of IPSA is verified under fractional transmit power allocation (FTPA). Simulation results show that our algorithm outperforms the strongest user-weakest user (SUWU) scheme, user grouping (UG) and random subcarrier and power allocation (RSPA) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER.

In non-orthogonal multiple access (NOMA), signals of different users are independently encoded and modulated, then superposed in the power domain before transmission. The choice of a suitable combination of power allocation factor (PAF) and transmit constellations is hence critical to enhance the system performance. The aim of this work is to develop a tool for adaptively selecting a suitable PAF and transmit constellations for an enhanced system data rate, given some individual user bit error rate (BER) constraints that need to be jointly considered. For this purpose, analytical bit error probability (BEP) expressions are derived and evaluated. This is the first attempt to analytically evaluate the BER in NOMA systems. The expressions are evaluated in order to propose the best combination of MCS and PAF. The trade-offs involved in selecting a certain combination of transmit constellations and PAF are also investigated. Moreover, numerical evaluations prove that the attainable BEP at the near user is affected by that of the far user. Look-up tables of suitable combinations of PAF and transmit constellations are derived using the BEP expressions, with a focus on enhancing the system data rate for some given BER constraints at both users.