Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.

eXtended Reality (XR) and holographic telepresence place stringent Quality of Service (QoS) demands on network infrastructure, requiring ultra-low latency, high throughput, and reliable connectivity. Meeting such QoS demands is critical in dynamic, distributed cloud environments, but does not always guarantee a satisfactory user experience. Quality of Experience (QoE) captures the user’s perception of service performance, which may be influenced by factors not fully reflected in systemlevel metrics. Thus, novel orchestration strategies must consider both QoS and QoE. This paper proposes a Reinforcement Learning (RL)-driven approach to edge-cloud orchestration capable of adapting to dynamic network conditions, leveraging a multiobjective reward function, including both QoS and QoE aspects, to guide service placement decisions. Evaluation shows that our RL approach reaches a 21.3% QoE gain over heuristics and 14.7% over balanced strategies, with 100% request acceptance. The results highlight the robustness and scalability of RL-driven orchestration, particularly for latency-sensitive 6G applications. Our findings also reveal the limitations of traditional heuristics under complex objectives and highlight the potential of RL as a transformative tool for intelligent network and service management in next-generation communication systems.

The emergence of the 6 G era demands seamless orchestration across an increasingly heterogeneous and distributed compute continuum-spanning edge, fog, and cloud resources. Moreover, the next generation of the mobile network is poised to redefine the digital landscape by enabling pervasive intelligence, ultra-low latency communication, and extreme heterogeneity across the entire network infrastructure. This transformation introduces unprecedented orchestration challenges due to the dynamic, multi-domain, and resource-constrained nature of emerging workloads such as Generative Artificial Intelligence (GenAI) inference, immersive eXtended Reality (XR), and autonomous systems. To tackle this complexity, we advocate for a Hybrid Hierarchical Digital Twin (DT) architecture that serves as a foundation for intelligent, adaptive, and real-time orchestration in 6 G environments. We present a comprehensive vision for integrating DTs as enablers of intelligent, context-aware, and adaptive orchestration mechanisms that span across multiple domains. The proposed architecture introduces a multi-layered DT hierarchy combining local and global views, enabling scalable coordination and real-time decision-making. We highlight key architectural enhancements required to realize this vision, including inter-twin interoperability and behavioral modeling for QoE estimation. This work aims to guide researchers and practitioners in shaping the foundations of resilient and efficient orchestration frameworks for 6 G systems.

Future 6G networks will be called upon to support increasingly sophisticated applications based on the massive use of Artificial Intelligence (AI). This poses serious challenges to mobile devices that do not have adequate computational and energy capacity to support the effective execution of AI tasks. According to the emerging Split-AI paradigm, 6G networks can help user devices to perform complex AI tasks by distributing and executing a Neural Network (NN) collaboratively among different processing nodes, including network nodes in the 6G User Plane (UP). However, the extent to which these offloading mechanisms can improve the end-to-end latency of AI tasks is limited by the computational resources available in an operator’s network. The presence of Graphical Processing Units (GPUs) in some elements of the UP can certainly help, but it is necessary to quantify the cost of the task acceleration in terms of increased energy consumption entailed by GPU usage. In this work, we study on the example of Split-AI, the impact of GPU acceleration on inference time and energy consumption. Thereby, we demonstrate that GPU inclusion can reduce the latency induced by the DNN computation by up to 90%, with moderate energy consumption increase of 22% at most.

Goal-Oriented (GO) communication is an emerging paradigm that aims at enhancing the efficiency of Internet of Things (IoT) systems by sending only the minimum data needed to achieve the application goal. In GO communications, the network can apply techniques to reduce the amount of data sent through the network to a cloud node, such as analyzing and selecting data at the source with intelligent IoT devices. GO communication and the related networking techniques have primarily been studied from a device-centric perspective, often overlooking their impact on the network. However, the network consumes significant amounts of energy, with the Radio Access Network (RAN) alone accounting for approximately 70 % of the total energy consumption in mobile communication systems. Therefore, this paper introduces an end-to-end model for energy consumption, latency, and accuracy to assess GO networking strategies. We then use the model to evaluate the performance of GO strategies in edge cloud scenarios with different hardware platforms.

The future of human interaction is virtual. Thus it will require effective collaboration on tasks among users in remote settings. eXtended Reality (XR) is playing a leading role in this transition, offering a realm where virtual collaboration becomes not just possible but essential in situations where physical presence is limited by risk, cost, or complexity. However, while networks are continuously evolving, they can still introduce unexpected impairments that potentially degrade the user perception, i.e., the Quality-of-Experience (QoE), of such Collaborative Virtual Reality (CVR) scenarios. In response to this challenge, this paper presents a demonstrator designed to explicitly showcase the effects of network conditions on CVR. Our platform, centered around a pizza-making game, allows for exploration of the real-time impact of different network parameters, such as packet delay, loss, and throttling on the user engagement and perception in CVR. The framework employs a combination of subjective, objective, and physiological assessments, including the capture of heart rate and skin conductivity, to gain comprehensive insights into user experiences. Our platform not only allows users to directly experience the impact of network impairments on CVR interactions but also provides initial evidence of how such distortions affect both subjective perceptions and objective performance metrics.

In-Network Computing (INC) is a currently emerging paradigm. Realizing INC in 6G networks could mean that user plane entities (UPEs) carry out computations on packets while transmitting them. These computations may have specific requirements in terms of their completion time. In case of high compute pressure at one UPE, migrating computations to another UPE may be beneficial, in order to avoid exceeding the completion time requirement. Centralized migration approaches suffer from increased signaling and are prone to react too slow. Therefore, this paper investigates the applicability of distributed intelligence to tackle the problem of compute task migration in the 6G User Plane. Each UPE is equipped with an intelligent agent, enabling autonomous decisions on whether computations should be migrated to another UPE. To enable the intelligent agents to learn and apply an optimal task migration policy, we investigate and compare two state-of-the-art Deep Reinforcement Learning (DRL) approaches: Advantage Actor-Critic (A2C) and Double Deep Q-Network (DDQN). We show, via simulations, that the performance of both solutions, in terms of the percentage of tasks exceeding their completion time requirement, is near-optimal and training A2C is at least 60% faster than DDQN.

The deployment of network elements in 6G is expected to be significantly more distributed than the existing 5G deployments. Distributed management paradigms are compatible with such distributed network deployments. Further, owing to their ability to solve complex problems by evaluating the impact of actions on the environment, intelligent solutions based on Reinforcement Learning (RL) for distributed management are promising. However, there are still several unsolved challenges before distributed intelligence could be seamlessly integrated in 6G. This work defines relevant research questions, reports on the progress made in the PhD project and presents the next steps and future directions for the advancement of this topic.

While the concept of AI4Net has been widely discussed in the past decade and adopted in 5G, its counterpart, Net4AI, has not gained that much attention so far. This is mostly due to the absence of solutions for the network to support AI applications beyond providing the communication infrastructure. In-Network Computing (INC) is a promising paradigm, potentially being integrated into 6G, which opens new solutions for realizing Net4AI. This paper focuses on the specific case of INC-assisted Split-AI. A Neural Network (NN) is split vertically and the executions of some layers of the split NN are offloaded to the entities of an Intelligent 6G User Plane. With the example of INC-assisted Split-AI, we elaborate on the challenges of Net4AI and discuss key requirements for the 6G architecture in terms of novel capabilities and information exchange.

In future 6G networks, the deployment of network elements is expected to be highly distributed, going beyond the level of distribution of existing 5G deployments. To fully exploit the benefits of such a distributed architecture, there needs to be a paradigm shift from centralized to distributed management. To enable distributed management, Reinforcement Learning (RL) is a promising choice, due to its ability to learn dynamic changes in environments and to deal with complex problems. However, the deployment of highly distributed RL – termed massive distribution of intelligence – still faces a few unsolved challenges. Existing RL solutions, based on Q-Learning (QL) and Deep Q-Network (DQN) do not scale with the number of agents. Therefore, current limitations, i.e., convergence, system performance and training stability, need to be addressed, to facilitate a practical deployment of massive distribution. To this end, we propose improved Double Deep Q-Network (IDDQN), addressing the long-term stability of the agents’ training behavior. We evaluate the effectiveness of IDDQN for a beyond 5G/6G use case: auto-scaling virtual resources in a network slice. Simulation results show that IDDQN improves the training stability over DQN and converges at least 2 times sooner than QL. In terms of the number of users served by a slice, IDDQN shows good performance and only deviates on average 8% from the optimal solution. Further, IDDQN is robust and resource-efficient after convergence. We argue that IDDQN is a better alternative than QL and DQN, and holds immense potential for efficiently managing 6G networks.

In future 6G networks, distributed management of network elements is expected to be a promising paradigm. Recent research progress in Artificial Intelligence (AI) is rapidly driving the adoption of distributed management. However, distributed management using intelligence or distributed AI inherently suffers from a number of issues - potential conflicts, signaling required to ensure cooperation and the convergence time of the algorithm. To this end, an early understanding and analysis of the overall effort to implement distributed AI in 6G, is still unexplored. This work, therefore, examines the impact of distributed AI, by analyzing its performance and how the existing 5G architecture could be enhanced to support it in 6G. We aim to understand the impact of distributed AI in 6G by selecting a relevant beyond 5G use case - auto-scaling virtual resources in a network slice. We present the performance and architecture analysis for two distributed algorithms from the domain of Reinforcement Learning - Q-Learning and Deep Q-Networks. We argue that despite its aforementioned issues, distributed AI brings benefits such as dynamic and adaptive decision-making, making it highly applicable for certain use cases in 6G.

5G mechanisms typically rely on resource over-provisioning or static reservations to satisfy the stringent demands of critical connections. Consequently, 5G networks are inefficient in fulfilling application's low delay and high reliability requirements. Emerging 6G use cases will be even more demanding in terms of delay and reliability. Therefore, the usage of existing mechanisms would result in immense operational costs for the mobile network operators due to their in-efficiency. This highlights the need for investigating sophisticated mechanisms with the evolution towards 6G networks, providing reliability in a cost-efficient manner. This paper examines how to prioritize critical connections over non-critical ones, while constructively exploiting the available resources. To achieve this goal, we propose an intelligent admission control (AC) scheme for a radio access node (AN). More specifically, using AI/ML techniques, the AN forecasts the number of incoming critical connections and supports their reliability requirements by dynamically reserving resources for them, consequently affecting the admission of non-critical connections. Our simulation-based evaluations show that the proposed approach improves a baseline approach – not using intelligence –as it is capable of providing reliability to a larger number of connections, while efficiently utilizing system's resources. Thus, our work provides evidence of the potential of using intelligence for next-generation admission control design.

Driven by the development of programmable networking hardware, In-network Computing (INC) has gained a considerable amount of attention in recent years. However, INC has so far barely been studied in the context of mobile networks, despite the vast advantages shown for fixed networks, such as latency or traffic reduction. Motivated by an Augmented Reality (AR) use-case, our work envisions an INC-enabled Intelligent User Plane (IUP) for 6G networks, which allows offloading computational tasks to UP entities having enhanced computational capabilities. The 6G IUP thus helps to keep mobile end-devices lighter and supports meeting the stringent delay requirements of novel applications, such as AR. Besides elaborating on the involved prospects and challenges, we identify key enablers for realizing the INC-enabled IUP. We show that embedding INC into the 6G system entails major changes in the architecture, as compared to the current 5G design.

The availability of networks able to perform control and management operations at unprecedented speeds is a crucial achievement for supporting next-generation Industry 4.0 applications. In particular, the presence of humans interacting with moving objects calls for decisions to be performed at time scales such as to ensure achieving the specific requirements identified by standardization bodies for the industrial sector in Beyond-5G environments. Among these specific Key Performance Indicators (KPIs), network reliability must be carefully pondered to guarantee the safety of the human operators at any time while investing the proper level of resources. For this reason, this paper presents an optimization problem that finds the best set of redundant radio bearers for each User Equipment (UE) in the factory area while accounting for the resource usage. Such problem extends the current specification on redundant transmissions. Several heuristics have been designed to achieve the desired time scales that could not be fulfilled with an exhaustive search. Results show that the optimization algorithm and the heuristics provide the same outcomes in terms of selected bearers and loss rates, but the latter do so at significantly more stringent time scales.

In recent years, owing to the architectural evolution of 6G towards decentralization, distributed intelligence is being studied extensively for 6G network automation. Distributed intelligence, based on Reinforcement Learning (RL), particularly Q-Learning (QL), has been proposed as a potential direction. The distributed framework consists of independent QL agents, attempting to reach their own individual objectives. The agents need to learn using a sufficient number of training steps before they converge to the optimal performance. After convergence, they can take reliable management actions. However, the scalability of QL could be severely hindered, particularly in the convergence time - when the number of QL agents increases. To overcome the scalability issue of QL, in this paper, we explore the potentials of the Deep Q-Network (DQN) algorithm, a function approximation-based method. Results show that DQN outperforms QL by at least 37% in terms of convergence time. In addition, we highlight that DQN is prone to divergence, which, if solved, could rapidly advance distributed intelligence for 6G.

Recent studies on designing 6G networks, especially when it comes to satisfying the stringent latency and reliability requirements, identify native integration of Artificial Intelligence (AI) as a key enabler. Although 5G systems support features to provide high-reliability communication, they mostly rely on resource over-provisioning and are hence inefficient. We identify the need for addressing the problem of resource-efficiency from the perspective of 6G systems. The dynamic behaviors of radio access networks, due to varying radio channel conditions, pose an additional challenge in achieving high resource-efficiency. In this respect, we present and evaluate a Machine Learning (ML)-based mechanism for efficient support of safety-critical communication with stringent latency and reliability requirements. This mechanism is embedded in the admission control (AC) of an access node (AN) and uses Least Absolute Shrinkage and Selection Operator (LASSO)-based resource budget prediction while admitting new connection requests to the network. The goal is to maximize the number of clients admitted into the system while maintaining the reliability of the previously admitted critical connections. Our simulation-based evaluations highlight that the proposed approach outperforms the baseline approach - not featuring ML - by about 17% in terms of the average per-session reliability of safety-critical connections at different network load conditions, and hence, provide further evidence on the potentials of ML for next-generation networks design.

Distributed automation is expected to play a significant role in the management of 6G networks, as it avoids the drawbacks of a single point of failure and signaling overhead inherent in a centralized paradigm. However, the issue of conflicts is intrinsic to a distributed architecture and when left unaddressed, may severely impair system KPIs. Considering the conflict problem, it is unclear if distributed automation would be scalable to realize the potential of 6G networks. In this paper, we validate the scalability of distributed intelligence, specifically based on Q-Learning, Q-Learning for Cooperation (QLC), consisting of intelligent agents that learn to cooperate on a discrete state space. Results show that the performance of QLC is scalable when compared to the optimal, computed by a centralized solution. Scalability may be limited by the convergence time that increases with the number of agents and the size of the discrete state space. The cooperation overhead is also not critical. These findings indicate that QLC is promising and may be applied to other use cases if the speed of convergence is not a significant detriment in distributing intelligence in 6G.

Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to application-specific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming. The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO’s point of view.

Network operators generally aim at providing a good level of satisfaction to their customers. Diverse application demands require the usage of beyond best-effort resource allocation mechanisms, particularly in resource-constrained environments. Such mechanisms introduce additional complexity in the control plane and need to be configured appropriately. Within 5G mobile networks, two new mechanisms for QoS-aware resource allocation are introduced. While QoS Flows enable specifying various QoS profiles on a per flow granularity, slices are dedicated virtual networks, strongly isolated against each other, with aggregated QoS guarantees. It is, however, unclear how QoS Flows and network slicing can optimally be exploited to ensure a high customer QoE while efficiently utilizing the available network resources. We address this research question and evaluate the outlined interplay using the OMNeT++ simulation environment in a multi-application scenario. We show that resource isolation induced by slicing may negatively affect application quality or system utilization, and that this impact can be overcome by finetuning the system parameters.

URLLC is the key feature of 5G enabling extensive V2X applications and control of complex industrial processes via wireless communication systems. In order to ensure the highest reliability requirements with low latency communication, the fluctuating characteristics of a wireless link must not become a limiting factor for the use case. Cooperative communication approaches eliminate this problem by exploiting additional links via relay nodes to transmit data redundantly on different paths in the network to the destination node. We present a suitable multi-path scheme for integration in 5G and B5G systems. The theoretical analysis shows nearly one order of magnitude improvement of reliability for each additional path with the proposed architecture. Furthermore, we demonstrate a significant gain in terms of reliability for the overall link even for highly disrupted relay paths. Using experimental vehicle platooning measurements in real-world traffic based on LTE and IEEE802.11p, we confirm the conceptual advantage of our multi-path approach.

Reliable connectivity between vehicle and mobile edge computing (MEC) server is paramount to exchange the information in time and without loss. Reliable vehicle-MEC communication is supported by different mechanisms of 3GPP 5G mobile networks, however, they are often inefficient in keeping the balance between reliability of the connection and the associated operational service costs of mobile operators, such as resource usage and signaling load, to provide the desired reliability. In this respect, we identify the challenges that need to be overcome to maintain cost-efficient reliable connection between vehicle and MEC server. We propose the usage of distributed intelligence to address the challenges and analyze the categories of distributed machine learning from the perspective of solving the challenges.

Since their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties' Application Functions (AFs). Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is presented and evaluated. The mechanism relies on an ML algorithm embedded in NWDAF, the collection of 5G network KPIs, and the collection of QoE information from video streaming service provider, i.e., the 3rd party AF. The mechanism has been evaluated in terms of QoE estimation accuracy against the cost in terms of required input sources and data for the estimation, and its performance has been compared to alternative methodologies not making use of ML. The evaluation, via simulation activity, clearly highlights the benefits of the proposed mechanism. Based on the derived results, the required input sources are ranked with respect to their importance.

5G systems (5GS) have been conceived to provide, besides traditional mobile broadband services, also connectivity required by vertical industries, among which Vehicular to Everything (V2X) communication is a prominent example. Enhanced V2X communication extends its reach to safety critical services, which impose severe Quality of Service (QoS) requirements going beyond the capabilities of the early version of 5GS, corresponding to 3GPP release 15 standard specifications. This paper discusses a novel Multi Level QoS (MLQ) feature allowing a tighter QoS monitoring and control, aiming at improving service availability and continuity, essential for safety critical applications. Starting from a review of 3GPP Release 15 QoS model, the paper provides design details for the introduction of MLQ feature in the standard specifications. The paper also provides experimental evaluation of MLQ performance, combining on-field measurements and system level simulations. Results, albeit preliminary, hint to up to two-digit gain in terms of service availability and continuity, at a limited cost both in terms of complexity and additional required signaling.

Within the ongoing activities devoted to the definition of 5G networks, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet QoS constrained, mIoT services will require adhoc end- to-end (E2E) solutions, i.e., featuring access and core network enhanced Control and User planes (CP/UP) mechanisms. This paper presents and evaluates a novel connectivity solution to manage massive number of devices. The paper presents an analytical model developed to evaluate the performance of the proposed solution. Quantitative results derived from the model demonstrate the effectiveness of the solution proposed in this paper, compared to 4G systems, and its ability to reduce CP signaling and optimize UP resource utilization for massive device deployment.

Virtual Network Embedding (VNE) is considered a key technology to instantiate and operate Data and Control planes in next generation (5G) SDN-based Networks. Within this domain, Network Resource Management (NRM) is an essential feature to allow efficient resource utilisation, to enable network slicing and to guarantee fairness among the supported QoS classes. This paper presents and evaluates three alternative NRM policies: Full Sharing, Full Split and Russian Dolls. Policies define how different QoS classes share the available bandwidth on per link basis. The policies have been integrated in a MIP-based Virtual Link Mapping formulation (VLM+) supporting multi-constrained end to end QoS. Simulation results show different policies can suit different network operator's requirements. Also, results highlight Russian Dolls significantly outperforms other policies in terms of Embedding Rate and Link Utilisation, still preserving fairness among QoS Classes. VLM+ Convergence Time has also been evaluated, showing all policies are compatible with timing requirements for a real 5G system implementation.

This paper presents VLM+, a Virtual Link (VL) Embedding algorithm supporting Quality of Service (QoS). VLM+ has been developed evolving its Mixed Integer Programming (MIP) based Virtual Link Mapping algorithm precursor (VLM), whose analytical model has been enhanced including the ability to fulfill VL end-to-end QoS requirements and to prioritise VL requests according to their QoS class or revenue profile. Additionally, VLM+ supports three different physical resource sharing policies, this providing Physical Infrastructure Providers (PIPs) with high flexibility in the allocation of resources to embed QoS-constrained VLs. VLM+ has been designed and evaluated targeting next generation networks, conceived around SDN and NFV paradigms and expected to support, among others, delay critical services. VLM+ performance has been evaluated via simulation and compared to VLM, Shortest Path First (SPF) and Constrained SPF algorithms. Promising quantitative results demonstrate VLM+ ability to embed VLs fulfilling end-to-end QoS requirements, still achieving an efficient resource utilisation and at a moderate cost in terms of increased complexity and convergence time.

One of the main goals of future telecom networks is to achieve softwarization of network functions. A requirement to achieve this softwarization is the ability to construct on-demand networks across multi-domain network and/or cloud service providers (NCSP). Most current algorithms and MILP formulations that solve the multi-domain embedding problem work on a flat infrastructure using simplified physical resource models. In this paper we propose an abstraction of the physical network domains to make the multiple domains appear as a pseudo flat infrastructure enabling re-use of existing flat infrastructure embedding algorithms with a few modifications. We incorporate these modifications in our previously proposed embedding ILP formulation. Our results show that we can speed up the process of embedding in large multi-domain networks considerably while trading off some efficiency in resource utilization due to abstraction.

Virtual network embedding is considered an important problem to solve in order to make infrastructure virtual-ization economically reasonable. The most efficient algorithms proposed so far define link and node mappings as optimal solutions of Integer Programming (IP) problems. They exhibit reasonably good performance only for small problem instance sizes, including few tens of nodes and links per physical substrate, few nodes and links per virtual request and a dozen of virtual requests to handle in parallel. However, we find these instances too small to be of any practical use. To address this scalability issue, we propose VNetMapper, an algorithm to solve the virtual network embedding problem based on an integer program formulation with appropriately selected objective function, variables and the set of constraints tuned to give an optimal performance. We show through simulations that VNetMapper can quickly, within seconds, solve large problem instances, involving physical substrates with hundreds of nodes and thousands of links and batches of hundreds of virtual network requests. By identifying exact properties that make VNetMapper so fast and scalable, we present guidelines for designing scalable integer programs.

The development of methodologies to manage and orchestrate virtualised resources and network functions is a fundamental enabler for optimally utilising physical ICT infrastructures. Algorithms for optimal location (embedding) of network functions, IT and CT resources, services and corresponding states, especially at the network edge, will enable new business models and provide a key competitive advantage to network administrators. This paper introduces a novel Mixed Integer Programming (MIP) formulation for a coordinated node and link mapping onto the underlying network infrastructure. Extensive simulation results show that the proposed algorithm outperforms prior art formulations: two digit gains were attained in terms of resources utilisation, embedding, revenues and, especially convergence time. The proposed methodology is applicable to a number of relevant use cases, as constraints and objective functions can be flexibly defined by network operators.

High performance systems require high levels of reliability. Many functions involved in telecommunication and IT networks have reliability requirements that can only be achieved by introducing redundant resources. In the telecom sector, recently, there has been a significant effort on moving carrier grade systems and functions to virtualized network infrastructure. The management and coordination of those virtualized systems to achieve an optimal mapping (or embedding) to the physical resources that host them is known as virtual resource orchestration. In our prior work we introduced a novel model, based on Mixed Integer Programming (MIP) problem formulation, as one significant way of achieving this optimality in embedding. This paper extends the model to include reliability requirements, improving prior art techniques as well as implementing a novel approach, denoted as reliability assurance. The confidence of the target reliability of the embedded virtual graphs can be traded with the efficiency of the substrate utilization. Extensive simulation results show that our model provides embedding rates and infrastructure utilization comparable with prior art while fulfilling high reliability requirements.

In this paper we present simulation results to demonstrate that a simple distributed intra frequency load balancing algorithm based on automatic adjustment of handover thresholds can significantly reduce the call blocking rate and increase cell-edge throughput in an LTE network.

In this paper, we consider the related issues of pre-emption and congestion control in Long Term Evolution (LTE) networks. A load-reduction method which can be used for either pre-emption or congestion control is described and evaluated by simulation. The simulation results show that the proposed mechanism can significantly improve dropping and blocking probabilities, depending on the bearer priorities.

The WiMAX 802.16e system is intended to support applications with challenging quality of service constraints in heterogeneous scenarios where both low and high mobility users are simultaneously active. However, the fast and deep channel fluctuations that are leveraged in rich scattering environments (i.e., urban scenarios) affect the performance of e.g., channel estimation, link adaptation and scheduling algorithms. Therefore, the physical and MAC layer algorithms need to be designed in order to support both low and high mobility users. Herein, the impact of users mobility on the design and the performance of the physical and MAC layers of a WiMAX base station is assessed, and suitable solutions for the channel estimation, dynamic link adaptation and scheduling algorithms are provided. Simulation results provide indications on the achievable maximum cell load for different mobility and QoS profiles. The optimal trade-off between the PUSC and AMC permutations is also numerically investigated.