This paper presents a comprehensive survey of semantic protocols, protocol learning, and AI-enabled semantic protocols within the context of Open RAN and 6G networks. We systematically review the significant progress achieved in these domains, highlighting key methods such as transformer-based semantic encoders, reinforcement learning–driven protocol adaptation, and federated learning frameworks for distributed training. Across surveyed studies, notable achievements include bandwidth savings of 35-70%, improved robustness under noisy conditions, and enhanced interoperability in multi-vendor environments. By consolidating findings, we identify major challenges such as the lack of standardized semantic KPIs, computational overhead at the edge, interoperability issues, and emerging security vulnerabilities. Furthermore, we categorize open research opportunities into theoretical, methodological, technical, and implementation directions, providing a clear roadmap for future development. This survey ultimately positions semantic communication and AI-enabled protocols as pivotal enablers for meaning-centric, adaptive, and efficient next-generation O-RAN/6G networks.

As communication systems evolve toward 6G, semantic communication is emerging as a transformative paradigm that prioritizes the accurate transmission of meaning rather than just bits. While artificial intelligence enables this shift by facilitating intelligent interpretation and context-aware processing, it also introduces significant challenges related to transparency, reliability, and user trust. XAI has thus become essential in making AI-enabled semantic communication systems more interpretable, auditable, and accountable. To the best of our knowledge, this is the first survey that systematically analyzes how explainability can be embedded across all stages of the semantic communication pipeline, integrating architectural design, metrics, security considerations, and human-in-the-loop mechanisms. Additionally, the survey identifies pressing research challenges, including the lack of standardization, real-time applicability, and vulnerabilities introduced by opaque AI models. By drawing attention to these issues and outlining future research directions, this survey aims to guide the development of responsible and trustworthy semantic communication systems for next-generation wireless networks.

The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we proposeDiktopos, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos' effectiveness in dynamic, resource-constrained edge scenarios.

Efficient workload scheduling in Kubernetes is crucial for optimizing energy consumption and resource utilization in large-scale and heterogeneous clusters. However, existing Kubernetes schedulers either ignore power-awareness or rely on simplified, static power models, which limit their effectiveness in managing energy efficiency under dynamic workloads. To address these shortcomings, we present a multi-objective scheduling framework for online Kubernetes pod placement that jointly considers power consumption, resource utilization, and load balancing. The framework follows a two-stage design: (i) a node power–profiling component trains a machine–learning model from real power measurements to predict per-node consumption under varying utilizations; and (ii) an online scheduler uses these predictions within a multi-objective optimization formulation. We implement scheduling optimization using two algorithms, TOPSIS and NSGA-II, adapting them to the Kubernetes context, and also propose a distributed variant of the NSGA-II algorithm that parallelizes fitness evaluation with controlled migration between workers. Experimental results show that the proposed framework outperforms baseline schedulers, achieving a 40% reduction in power consumption and improvements of 74% and 68% in CPU and memory utilization, respectively, while sustaining scalability under high workloads. To the best of our knowledge, this is the first work to integrate learned power models and distributed multi-objective optimization into Kubernetes for power-aware pod scheduling.

Open Radio Access Network (O-RAN) enables modular and intelligent control of radio resources through open interfaces and programmable RAN components. As networks evolve toward sixth-generation (6G) systems, the proliferation of autonomous xApps and rApps introduces a critical challenge: Coordinating concurrent AI-driven control actions under tight near-real-time constraints while avoiding instability and conflicting decisions. This paper focuses on two tightly coupled enablers for AI-native O-RAN orchestration: Conflict-aware control and intent-driven automation. We propose an AI-native orchestration framework centered on a CME integrated into the Near-RT RIC, and a complementary LLM-based intent orchestration module deployed in the Non-RT RIC. The CME is designed to autonomously arbitrate conflicting xApp actions by learning adaptive mitigation policies from structured conflict signals, system context, and performance feedback, rather than relying on static priorities or predefined conflict classes. The LLM module translates high-level operator intents into policy constraints and control objectives that guide conflict resolution and xApp behavior. Overall, this work advances AI-native O-RAN orchestration by grounding conflict-aware control and LLM-assisted intent translation in practical measurements, and by outlining a clear path toward scalable, adaptive, and resilient control mechanisms required for future 6G RIC deployments.

In this paper, we present a programmable data plane design that implements IEEE 802.1Qcr’s Asynchronous Traffic Shaper (ATS) purely in software using Extended Berkeley Packet Filter (eBPF), eliminating specialized hardware requirements for industrial Time-Sensitive Networking (TSN) deployments. Unlike hardware-bound TSN solutions, our approach dynamically decouples and reprograms functions like filtering, metering, and queuing through in-kernel eBPF hooks, enabling adaptive priority management for concurrent streams within shared priority queues. The design explicitly models the ATS scheduler to parameterize per-stream eligibility times in TSN bridges while maintaining deterministic operation. This software-defined method provides a vendor-agnostic path for integrating ATS capabilities into existing industrial networks, particularly for Industry 5.0’s distributed control scenarios requiring flexible traffic multiplexing. The results confirm correct enforcement of ATS scheduling semantics under heterogeneous workloads.

The Cloud Edge Continuum enables the deployment of latency-sensitive and data-intensive applications closer to end users, but it poses challenges for microservice placement due to resource heterogeneity and limited edge capacity, especially when storage requirements must be met through aggregated node resources. To address this, we propose a two-stage, topology-aware optimization framework that jointly handles microservice deployment and distributed storage volume allocation in edge networks. Our framework decomposes this joint placement problem into two subproblems, microservice placement followed by a distributed volume allocation subproblem, with the goal of optimizing computation, communication, energy, and storage costs. At its core is a lightweight, rank-based heuristic that ensures scalable, accurate placement across distributed edge nodes. Evaluations on real-world scenarios show our method achieves near-optimal placement (within 1.67% of the exact solution), reduces system costs by up to 30%, and accelerates convergence by 5× compared to state-of-the-art approaches, demonstrating its suitability for dynamic, resource-constrained edge environments.

International audience

Advancement of edge and far-edge computing, driven by the increasing demand for real-time, data-intensive applications, has heightened the need for reliable and efficient resource management in volatile environments. This paper introduces GTMixer, a deep learning architecture tailored for predicting resource usage in volatile edge computing scenarios. GTMixer utilizes a Dynamic Temporal Graph (DTG) to capture the evolving interdependencies in workload exchanges across edge and far-edge nodes. By processing snapshots of this graph, GTMixer identifies patterns in resource utilization, even in the absence of historical CPU data. Our contributions include: (1) the creation of a DTG that reflects migration patterns and resource utilization among nodes; (2) the development of the GTMixer model, which integrates feature mixing with graph neural networks for improved predictive accuracy; and (3) empirically evaluating GTMixer against state-of-the-art models using a modified version of the Alibaba 2021 traces dataset that accounts for volatility. Our results demonstrate that GTMixer not only effectively anticipates resource requirements in unpredictable Multi-access Edge Computing (MEC) scenarios but also significantly outperforms current state-of-the-art models in terms of efficiency, showcasing its potential to enhance the reliability and performance of edge computing systems crucial for nextgeneration network technologies and applications.

The rise of real-time networking demands has driven the IEEE Time-Sensitive Networking (TSN) task group to develop new standards that ensure high bandwidth and lowlatency Ethernet communication. TSN is an essential component of next-generation 6G networks. It offers features that ensure deterministic data transmission and alleviate network congestion. These features are crucial in time-sensitive applications and systems, whereby both precision and reliability are paramount. While early TSN implementations relied heavily on synchronous communication, newer standards, such as IEEE 802.1Qcr, have introduced asynchronous mechanisms via Urgency-Based Scheduler (UBS). UBS employs advanced queuing and traffic shaping strategies, to guarantee minimal delay for real-time applications. Within the scope of 6G, this study evaluates the queuing and shaping strategies applied to multiple flows within the UBS framework. Moreover, we assess their impact on frame transmission rates at the shaper level, highlighting the optimal use case for each strategy.

This paper proposes a new scheduling framework to optimize the placement of cloud workloads submitted online by users within a Kubernetes-orchestrated environment. The proposed method aims to incorporate power awareness during the scheduling process, along with other criteria, such, load balancing, and bin packing. The framework equitably distributes workloads across cluster nodes while also selecting the most resource-efficient node to reduce the number of active nodes and prevent resource fragmentation. Existing strategies often focus on a single criterion, leading to suboptimal and unsatisfactory workload placements. The proposed framework utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision analysis algorithm, to account for power consumption and other criteria defined by cloud operators, such as load balancing and bin packing. The algorithm is implemented as a Kubernetes scheduling plugin to rank worker nodes based on these criteria and the submitted workloads. Simulation results demonstrate the effectiveness of the proposed strategy across various scenarios, reducing power consumption by 26.46% and comparable CPU and memory load balancing performance within a large Kubernetes cluster under heavy workloads.

With the rapid advancement of telecommunications, 6G networks are expected to become more intelligent and capable of making autonomous decisions. Artificial Intelligence (AI) will play a crucial role in achieving this, particularly through the use of Large Language Models (LLMs). These models are increasingly being adopted for networking tasks due to their advanced capabilities in coding, reasoning, and language processing. LLMs have significant potential to support the development of autonomous networks by reducing or even eliminating the need for human intervention. However, LLMs are computationally expensive, which necessitates their shared use across different 6 G applications, i.e., a single LLM might be required to perform multiple tasks within a 6 G network. To this end, routing tasks to the appropriate LLMs presents several challenges: (i) the arrival time of tasks is unpredictable, (ii) tasks must meet specific deadlines, which are part of the ServiceLevel Objectives (SLOs), and (iii) each LLM may perform better on different types of tasks, leading to varying task scores. In this paper, we propose a Deep Reinforcement Learning (DRL) approach for routing tasks to a set of LLMs (task scheduling). Our goal is to maximize task scores while ensuring their deadlines are met. Evaluations conducted under real-world conditions show that our DRL-based approach outperforms traditional methods like Round-Robin (RR) and random scheduling.

The Internet of Things (IoT) is rapidly expanding globally, but the limited size of IoT devices restricts their battery capacity, computational resources, and wireless bandwidth, making it difficult to handle resource-intensive tasks. Edge Computing addresses these challenges by enabling task offloading to more capable edge servers. However, optimal task offloading in Edge-IoT networks is complex due to dynamic conditions, such as varying server loads and wireless fluctuations. Traditional and some machine learning-based offloading methods often fall short in adaptability or efficiency. This paper introduces a bi-level optimization approach using Deep Reinforcement Learning (DRL) agents for IoT-level offloading and a priority-aware greedy heuristic for resource allocation on edge servers. The proposed method effectively improves QoS by balancing task execution latency and power consumption, as demonstrated by simulation results.

In recent years, AI inference has seen widespread adoption across fields like finance and healthcare, driving significant demand for high-performing applications. This demand brings about a complex relationship between inference application types, such as real-time applications, and their specific service level objectives (SLOs), like tail-latency. Tail-Latency is a metric requiring a defined percentage of requests to meet a maximum response time, which is crucial for applications where delays can impact user experience or decision-making. This dependency creates a challenging research problem in scheduling inference workloads. The core question becomes: How can we deploy AI workloads in a way that minimizes SLO violations?Specifically, we worked on real-time applications that require tail-latency guarantees. To address this, we developed a tail-latency-aware scheduler designed for resource-constrained devices. Our scheduler employs advanced machine learning techniques to optimize task placement, aiming to minimize SLO violations and enhance performance for latency-sensitive applications. We have developed and integrated our custom scheduler into Kubernetes, which operates on a specially configured cluster designed to test its performance. This cluster features diverse computing capabilities, enabling a comprehensive evaluation of the scheduler’s effectiveness. The experimental results highlight that our proposed scheduler outperforms the native Kubernetes scheduler in terms of efficiency.

The emergence of IoT and the rise of latency-sensitive services are pushing computation from centralised clouds toward a Cloud-Edge Continuum (CEC), where services of the same application are deployed across different CEC nodes, with services requiring low latency closer to the user. This transition introduces new challenges, especially interconnecting these CEC nodes and deployed services dynamically to meet their service level agreements (SLAs). In this paper, we address dynamic Traffic Engineering (TE) in SD-WAN in order to maximise services’ QoS requirements fulfilment. We introduce a solution based on Hierarchical Multi-Agent Reinforcement Learning (H-MARL) for dynamically routing service flows through appropriate overlay links that maximise the QoS and reduce the network cost. Simulation results show the efficiency of the proposed solution in dropping overall latency and improving QoS fulfilment by about 10% compared with a flat single-layer MARL.

With the high demands of 6 G networking services in terms of Quality of Service (QoS), managing these networks requires intelligent next-generation Operations Support Systems (OSSs). According to standardization bodies such as ETSI and 3GPP, OSS must support end-to-end, cross-domain management across all 6 G domains. They are making significant efforts to standardize Application Programming Interfaces (APIs) to enable Intent-Based Networking (IBN), which simplifies network management by allowing users to express their intentions in a declarative manner. However, these systems remain complex for users with limited domain knowledge who need to interact with these standardized APIs. Moreover, adding new functionalities to OSS often requires users to learn new API endpoints and structures, which can be time-consuming. To address these challenges, we propose enabling natural language interaction with OSS by leveraging Large Language Models (LLMs). Our approach offers two key advantages: simplifying user interaction with the system using natural language, and enabling the system to autonomously adapt to new API features. Since fulfilling a user's intent may involve multiple low-level API calls, our solution is designed to plan and execute them in a coordinated manner. We employ multi-agent LLMs with a hierarchical planning mechanism, creating a chatbot-like system that processes natural language inputs effectively. Real-world experiments conducted at EURECOM's OSS demonstrated that the proposed approach can efficiently manage all 6 G domains using natural language.

Dynamic Discontinuous Reception (D-DRX) is an innovative power-saving mechanism for mobile devices in cellular networks, enhancing traditional DRX by enabling real-time adjustments to DRX parameters based on network conditions and dynamic traffic patterns. In this demonstration, we showcase a novel implementation of D-DRX on top of the open-source OpenAirInterface (OAI) [1] 5G platform, namely Deep Reinforcement Learning based Energy Saver (DRL-ES). DRL-ES is running as an xApp within the open-source O-RAN compliant RIC, FlexRIC [2], allowing for seamless adaptation to changing network dynamics. The DRL-ES operates by continuously monitoring Radio Link Control (RLC) latency data as input from the Distributed Unit (DU). DRL-ES leverages the Deep Q-Network (DQN) algorithm, which is trained to select the best DRX parameters, specifically the on-duration parameter for each User Equipment (UE). This focused approach optimizes the UE's active listening period within each DRX cycle based on observed RLC latency and achieves a balance between energy saving and Low latency. The DRL-ES utilizes two O-RAN compliant Service Models (SM): the Key Performance Measurement (KPM) SM to obtain RLC latency input and the RAN Control (RC) SM to send the on-duration values. This implementation adheres to existing 3GPP and O-RAN standards while demonstrating the potential of Artificial Intelligence (AI) driven optimization in 5G networks and beyond. Using DRL-ES, network operators can enhance battery life for mobile devices without compromising performance, particularly critical services.

In dense urban environments, traditional GNSS-based navigation for Unmanned Aerial Vehicles (UAVs) suffers from multipath interference and signal obstructions, compromising positioning accuracy and increasing risks of collisions and airspace violations. This paper proposes a novel machine learning-based system architecture for autonomous UAV parcel delivery, leveraging standardized 5G Network Exposure Function (NEF) and CAMARA Device Location API to achieve sub-meter location precision. Our approach integrates dynamic geofencing and predictive rerouting at the network edge, powered by a Random Forest-based collision prediction model that proactively adjusts UAV trajectories to avoid restricted zones in real time. Through simulations of six UAVs navigating dynamically updated no-fly zones, we demonstrate that our system significantly reduces time spent in restricted areas to near zero, compared to GNSS-only and rule-based methods, while limiting path-length inflation to approximately 30% for five of six flights. These results underscore the potential of combining 5G-enabled location services with edge intelligence to enhance safety and compliance in urban UAV operations.

Recent advancements in serverless computing and the cloud-edge continuum have increased interest in WebAssembly (WASM). This technology enables portability and interoperability across diverse computing environments while achieving near-native execution speeds. Currently, WASM supports Single Instruction Multiple Data (SIMD), which allows for data-level parallelism that is particularly beneficial for vectorizable operations such as general matrix-matrix multiplication (GEMM) and convolutional layers. However, WASM lacks native integration with specialized hardware accelerators like GPUs, TPUs, and NPUs, as well as the ability to benefit from multi-core processing capabilities, which are critical for efficiently running Deep-Learning (DL) workloads. In contrast, despite these gains, WASM still lacks native support for heterogeneous accelerators such as GPUs, TPUs, and NPUs, as well as full multi-core parallelism capabilities that are critical for meeting the latency and throughput requirements of modern DL inference services. To bridge this gap, WASI-NN was developed, enabling WASM to integrate with external runtimes such as OpenVINO and ONNX Runtime, which leverage hardware acceleration. However, these current integrations often introduce performance overhead on certain devices, restricting their usability across the CECC. To address these challenges, we propose a new integration focusing on TVM as an external runtime for WASI-NN to enhance WASM’s performance and expand support to a broader range of devices. Additionally, we integrate this solution into Knative, a serverless framework, to provide a scalable and flexible platform for DL deployment. Using WASM technology, we evaluate our TVM-based solution through comparative studies. Results on AMD CPUs demonstrate the effectiveness of our approach, achieving 58% overall gain over other WASI-NN integrations (e.g., ONNX Runtime and OpenVINO) for CNN-based models while also achieving optimal performance on different platforms, such as Intel GPUs. These findings highlight the effectiveness of our solution.

The emergence of new computing paradigms such as Edge Computing, Fog Computing, and Far-Edge Computing is driven by the increasing demands of modern applications. Together, these paradigms form the Cloud-Edge Computing Continuum (CECC), presenting new challenges in resource allocation and incentive-driven interactions. New stakeholders are joining the business market to make a profit by selling their services (i.e., infrastructure resources, applications, or virtual resources). These actors, namely, infrastructure providers and service providers, have conflicting goals in terms of making a profit. There is a need to study and model the business interaction between these actors, especially considering the distributed nature of continuum. In this paper, we tackle the resource allocation and pricing problem in the context of CECC. We first propose a system model of the incentive interactions between actors of the continuum, where the price of resources varies based on different factors. Then, we formulate a budget-aware resource bidding problem where the objective is to jointly maximize the budget of a service provider and minimize Service Level Agreement (SLA) violations. To address this challenge, we propose a Deep Reinforcement Learning (DRL) approach that efficiently balances budget expenditure and SLA compliance. Our experimental results demonstrate that the proposed method effectively achieves a favorable trade-off between budget management and SLA satisfaction.

With the advent of beyond 5G applications, the execution of computationally intense tasks moves further closer to the network edge. Alongside the capabilities of a Multi-Access Edge Computing (MEC), smart decision-making considering sustainability aspects has become achievable. In this paper, a resource management technique utilizing Reinforcement Learning (RL) at the MEC is presented in order to promote power efficient solutions. CPU resources at the MEC are managed and distributed to several network services for their individual disposal. A direct relation between the CPU resources and power consumption at the MEC is proposed further establishing the need for efficient resource handling. A Soft-Actor Critic (SAC) approach is leveraged to learn the patterns for intelligent resource allocation minimizing the power expenditure. Further, two baseline algorithms, the Knapsack method and the proportional resource allocation scheme, are implemented to prove the dominance of the proposed RL-based algorithm. The results confirm that in the SAC-based RL implementation, the power consumption at the MEC server is lower compared to the two baseline algorithms. The promising results pave way for the deployment of RL-based algorithms for efficient performance, thus promoting green technologies at the MEC.

Driven by the emergence of IoT services and the need to process data as close as possible to its source, the shift from centralized Cloud computing to Cloud-Edge computing has become essential. This transition has given rise to a new computing paradigm known as the Cloud Edge Computing Continuum (CECC), where microservices that constitute Cloud-native applications are geographically dispersed across different federated CECC nodes. This transformation has brought with it new challenges in terms of interconnecting geographically dispersed CECC nodes through Wide Area Networks (WANs) to support microservices interconnection needs (bandwidth, latency, etc.). To address this challenge, in this paper, we leverage Software Defined Wide Area Networks (SD-WAN) for interconnecting the CECC nodes. We first introduce a comprehensive CECC SD-WAN architecture by demonstrating how dynamic application-aware routing facilitated by SD-WAN can overcome these limitations, ensuring efficient and flexible network performance across geographically dispersed Cloud sites. Then, for the instantiation of the architecture, we adapt an open-source SD-WAN implementation to the context of CECC. We also show performance improvement, with our approach being 10% more efficient in terms of memory usage than the VRF lite approach in the open-source implementation.

Software-Defined Networking (SDN) has emerged as a transformative technology that revolutionizes network management and architecture by providing unparalleled flexibility and control over data traffic flows. This flexibility is increasingly crucial in managing the complex demands of modern networks, whereby efficient traffic management is essential for mitigating congestion and enhancing operational efficiency. This paper introduces a novel traffic management model that employs Deep Reinforcement Learning (DRL) to transcend the conventional limitations typically associated with routing strategies that prioritize the shortest path or make non-optimal decisions when forwarding the traffic between different peers. Our model not only reduces overall network congestion but also aims to minimize bandwidth usage and enhance routing mechanisms within SDN environments. By incorporating DRL-based load balancing mechanisms, the model intelligently redistributes traffic across multiple pathways, shifting the focus from proximity to efficiency. This strategic redistribution prioritizes routes that optimize both, transmission time and network performance, rather than merely the shortest path. Moreover, the integration of DRL allows for real-time decision-making, enabling our system to dynamically adapt to changing traffic conditions and user demands. This capability is instrumental in significantly reducing transmission times and improving the overall efficiency of traffic flow across the network. Our findings highlight the substantial benefits of integrating SDN with advanced DRL techniques, offering a pioneering perspective on traffic routing within SDN networks. We evaluated the proposed framework via simulations and the obtained results demonstrated the efficiency of our solution compared to the baseline approaches.

This paper describes the SDN-based L4S solution, a congestion control algorithm designed to improve QoS in 5G and Beyond networks. Inspired by the IETF specifications, our framework tackles challenges prevalent in immersive applications like video streaming and cloud gaming, such as ultra-low latency and packet loss. The proposed solution seamlessly integrates L4S techniques into SDN, thereby optimizing queue management within the transport network. Additionally, it employs Explicit Congestion Notification (ECN) to mark packets during congestion scenarios. This synergistic approach facilitates dynamic adjustments in transmission rates, enhancing the overall efficiency of the transport network, particularly in accommodating various classes of traffic. Evaluation results demonstrate that our solution outperforms the benchmarks by a substantial margin in terms of End-to-End latency, and packet loss.

Multi-Access Edge Computing (MEC) is a key technology in the field of telecommunications and computing. It brings computing and storage resources closer to the edge of the network, typically at or near base stations and hence reduces the access latency of User Equipment (UE) to applications hosted at the edge. However, mobility of UEs brings challenging issues for service continuity and Service Level Agreement (SLA) fulfilment of 5G services. To solve these issues, 3GPP introduced a new Network Function (NF) called the Edge Application Server Discovery Function (EASDF) [1]. The latter aims to support session breakouts by dynamically resolving the Domain Name Service (DNS) of MEC applications to application servers closer to the UE's physical location. However, the 3GPP specifications [1] do not provide details about how the EASDF handles the UE's mobility. To fill this gap, we propose a novel design and implementation of the EASDF on the top of OpenAirInterface (OAI) open-source 5G network [2]. Simulation results show the efficiency of the EASDF in reducing the access latency during the UE's mobility with a small overhead of less than 4ms in high-load scenarios.

Training without sharing data is one of the drivers that makes Federated Learning (FL) more attractive, compared to centralized approaches. However, requiring each learner to train the full model may not be efficient, particularly for devices with restricted resources, such as those available in Unmanned Aerial Vehicles (UAVs). To address this issue, a variation of FL technique, specifically Split Federated Learning (SFL), has recently been proposed. Unlike FL, the key concept of SFL is to divide the layers of the neural network among the involved learners. Therefore, each individual client will train only a segment of the model (submodel) rather than the entire model. Clearly, this technique, besides data privacy, optimizes the utilization of computational resources, reduces client-side training time, and enhances model privacy. However, there are questions that require answers: How should we split the model? Shall we systematically divide it in half, or is there a more optimal approach? In this line of thought, this paper provides a detailed analysis of possible splitting schemes of a power consumption prediction model for UAV s. First, the SFL-enabled model is presented. Second, an experimental analysis is conducted in which different splitting alternatives are made and numerically analyzed to examine the influence of network layering on split federated learning performance.

The emergence of 6G networks heralds a transformative era in network slicing, facilitating tailored service delivery and optimal resource utilization. Despite its promise, network slice optimization heavily relies on Deep Reinforcement Learning (DRL) models, often criticized for their black-box decision-making processes. This paper introduces a novel Composable eXplainable Reinforcement Learning (XRL) framework customized for distributed systems like 6G Network Slicing. The proposed framework leverages Large Language Models (LLMs) and Prompt Engineering techniques to elucidate DRL algorithms’ decision-making mechanisms, with a specific emphasis on user profiles. The latter transforms the inherently opaque nature of DRL into an interpretable textual format accessible not only to eXplainable AI (XAI) experts but also to diverse network slice provider stakeholders, engineers, leaders, and beyond. Experimental results underscore the efficacy of the proposed Composable XRL framework, showcasing substantial improvements in transparency and comprehensibility of DRL decisions within the context of 6G network slicing.

Intent-Based Networking (IBN) is a promising paradigm for next generation networks, enabling automated network management based on user-defined business network requirements (Intents). However, current IBN approaches consider that users require expertise in some formal and technical models (e.g., Network Service Descriptors - NSDs) to define these Intents, necessitating substantial effort. A natural progression of IBN systems is to define Intents using natural language instead of structured models. However, dealing with this becomes challenging due to the unstructured and ambiguous nature of natural language. Fortunately, Large Language Models (LLMs) are becoming very powerful in understanding human language, making them well-suited for this task. This paper proposes an LLM-based Intent translation system that allows users to express Intents in natural language, which the system subsequently converts into NSDs. Moreover, we employ a Human Feedback (HF) loop that enables the system to learn from past experiences. Evaluations conducted at the EURECOM 5G facility [1] confirm the effectiveness of our approach in generating accurate NSDs suitable for deployment on an edge computing cluster.

The mobile industry - via forums such as the O-RAN Alliance and Linux Foundation CAMARA - is working on network APIs that allow a mobile network operator to expose network capabilities to application developers. One of these APIs is the Quality on Demand (QoD) API, which enables the application to ask for additional network resources for improved latency or bandwidth. In this work, we show how an intelligent content delivery network (CDN) can exploit these APIs to improve the quality of experience (QoE) of video streaming despite difficult network conditions by boosting the available network bandwidth at precise moments in time. As the bandwidth boost is only applied whenever necessary, we avoid the caveat of constantly and statically assigning network resources to a service. We propose two boosting strategies both relying on information provided by the video player via Common Media Client Data (CMCD). We implemented the approach and evaluated it on an emulation testbed and on top of an actual 5G O-RAN compliant network capable of running xApps and the CAMARA QoD API. Our evaluation shows the gains in terms of QoE but also highlights possible caveats and adverse interactions with the ABR algorithm of the video player.

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

Integrating low-Earth orbit (LEO) satellite constellations with terrestrial mobile networks can achieve global coverage and complement terrestrial networks. The inherent mobility of satellites induces frequent handovers of user equipment (UE), generating massive signaling. Coupled with limited satellite resources, the network functions (NFs) deployed on satellites cannot process these signaling promptly, leading to increased queuing time. Additionally, the movement of onboard NFs increases the distance to UE, extending propagation delay. Extended procedure completion time (PCT) of control plane procedures degrades user plane Quality of Service (QoS). To address the above challenges, we propose a satellite-terrestrial integrated core network architecture to enhance signaling processing performance. First, we redesign the control plane NFs and introduce a satellite-ground synergy method (SGSM), categorizing signaling into time-sensitive and time-tolerant types. The former is processed onboard, while the latter is handled terrestrially, utilizing a designed UE context synchronization mechanism. Furthermore, migration is employed to counteract the movement. We devise a migration procedure to reduce transferred data during migration. Moreover, we model instance migration as a Markov decision process and proposed an online NFs migration algorithm based on deep reinforcement learning to determine migration timing and target satellites. Extensive experiments demonstrate that the proposed methods significantly reduce queuing time and the volume of transferred data, while also exhibiting superior performance in terms of propagation delay and the migration frequency.

Dynamic Time Division Duplex (D-TDD) is a promising solution to address newly emerging 5G and 6G services characterized by asymmetric and dynamic uplink (UL) and downlink (DL) traffic demands. However, there are two major issues: (i) determining the TDD scheme (i.e., the number of slots devoted to UL and DL) to meet the dynamic traffic demands of the Users Equipment (UE); (ii) cross-link interference between cells that use different TDD schemes. The 3GPP standard neither specifies algorithms or solutions to derive the TDD configuration nor solves the cross-link interference. To fill this gap, we model the dynamic TDD problem in 5G NR as a linear programming problem. Then, we design Multi-Agent Deep Reinforcement Learning based 5G RAN TDD Pattern (MADRP), a fully decentralized solution based on the Multi-Agent Deep Reinforcement Learning (MADRL) approach. Based on the simulation results, the algorithm effectively prevents buffer overflows, avoids cross-link interference, and adapts to changes in the traffic pattern, ensuring its versatility. We compared our solution with the optimal solution and different static TDD configurations. We found that MADRP outperforms the static TDD configurations. We finally discuss the algorithm's limitations in terms of the number of cells, traffic variance, and cross-link interference probability.

In this paper, we unveil the Software-Defined Low Latency (SDLL) framework based on Software-Defined Networking (SDN) to provision the Quality of Service (QoS) and guarantee ultra-low latency in 5G and beyond Transport Networks (TN). SDLL aims to tackle Time Sensitive Networking (TSN)'s weaknesses by providing agility and flexibility in terms of Traffic Engineering (TE) and Queue Management (QM) to guarantee low end-to-end (E2E) latency even under congested links. SDLL provides a flexible and on-demand way to change end-to-end paths and queue configurations (ex., add or remove queues). We conducted extensive experimentation by implementing SDLL using Open Network Operating System (ONOS) and Open vSwitch (OVS) tools and comparing its performances against two standard solutions: SDN Shortest Path (SDNSP) (one queue per port) and Software-Defined QoS (SDQoS) (three queues per port). Obtained results indicate that SDLL can guarantee low E2E latency compared to the two other solutions, particularly when: (1) the links are congested and (2) many low-latency critical services are run in parallel.

The Network Data Analytics Function (NWDAF) is a key component of the 5G Core Network (CN) architecture whose role is to generate analytics and insights from the network data to accommodate end users and improve the network performance. NWDAF allows the collection, processing, and analysis of network data to enable a variety of applications, such as User Equipment (UE) mobility analytics and UE abnormal behaviour. Although defined by 3GPP, realizing these applications is still an open problem. To fill this gap: (i) we propose a microservices architecture of NWDAF to plug the 3GPP applications as mi-croservices enabling greater flexibility and scalability of NWDAF; (ii) devise a Machine Learning (ML) algorithm, specifically an LSTM Auto-encoder whose role is to detect abnormal traffic events using real network data extracted from the Milano dataset [1]; (iii) we integrate and test the abnormal traffic detection algorithm in the NWDAF based on OpenAirInterface (OAI) 5G CN and RAN [2]. The experimental results show the ability of NWDAF to collect data from a real 5G CN using 3GPP-compliant interfaces and detect abnormal traffic generated by a real UE using ML.

Nowadays, Internet of Things (IoT) devices are gaining momentum globally. However, due to their limited size, these devices have limited battery capacity, computational resources, and wireless bandwidth, making it impossible to run resource-intensive applications on these devices. Fortunately, Edge Computing has emerged as a promising solution to meet this demand by enabling data processing in more capable devices. Task offloading is a crucial technique used in Edge Computing to overcome the limitations of IoT devices by offloading some of their computational tasks to more powerful edge servers. The traditional methods used for task offloading are often based on heuristics or simple rules, which may result in sub-optimal solutions. Moreover, the increasing complexity and heterogeneity of edge networks, as well as the stochastic nature of the wireless channel, pose significant challenges for these methods. In this paper, we leverage Federated Learning (FL) to efficiently train Deep Reinforcement Learning (DRL) agents to make the best offloading and power allocation decisions by achieving the near-optimal trade-off between task execution latency and the power consumption of the end device. The obtained simulation results of the proposed method demonstrate its remarkable and superior performance in comparison to central DQN.

This paper proposes a novel approach to Software-Defined Networking (SDN) Admission Control (AC) based on Graph Neural Networks (GNNs) for Beyond 5G (B5G). AC is a critical function in SDN, as it determines which traffic flow to pass by the network and which should be rejected. GNNs are a type of Neural Networks (NNs) that are able to learn how to make real-time AC decisions by training on pre-existing data, including network topologies and traffic characteristics. The solution we propose is made of two layers: (i) Network Delay Predictor (NetDelP) leveraging on the RouteNet-Fermi GNN model, used to predict the network latency for different topologies and traffic patterns. (ii) Admission Control Agent (AdConAgt) supporting the SDN and used to regulate the traffic flow in the network. The outlined concept is able to manage large-scale and complex topology networks with optimized Key Performance Indicators (KPIs) such as network latency and Packet Loss Rate (PLR). The envisioned approach is evaluated with various network topology scales and classes of traffic. The obtained results outperform the SDN-Shortest Path (SDN-SP) solution by demonstrating the ability of our proposal to guarantee the End-To-End (E2E) latency and prevent link congestion in order to meet the QoS requirements.

Future generation cellular networks consider Power Consumption (PC) as a key concern in designing and operating wireless communication systems. In this context, 3GPP has proposed several techniques to reduce User Equipment (UE) PC, such as Connected-mode Discontinuous Reception (C-DRX), with a new set of parameters introduced by 5G New Radio (NR) and BandWidth Part (BWP) adaptation. However, they did not specify how to derive the C-DRX parameters and BWP configuration that reduce the PC while avoiding latency overflow. To address this shortcoming, we propose a novel solution to jointly derive the C-DRX parameters and the BWP configuration to find a trade-off between low PC and low latency. Given the inherent dynamics and uncertainty in wireless network environments, our solution relies on Deep Reinforcement Learning (DRL) to learn from the dynamic traffic pattern and derive the best C-DRX and BWP configuration that minimizes PC while achieving low latency. Simulation results demonstrate the effectiveness of the proposed methodology in reducing the PC (i.e., 50-95% power gain) while avoiding latency overflow for a different number of connected UEs (i.e., 1 to 20 UEs).

Fine-granular management of cloud-native computing resources is one of the key features sought by cloud and edge operators. It consists in giving the exact amount of computing resources needed by a microservice to avoid resource over-provisioning, which is, by default, the adopted solution to prevent service degradation. Fine-granular resource management guarantees better computing resource usage, which is critical to reducing energy consumption and resource wastage (vital in edge computing). In this paper, we propose a novel Zero-touch management (ZSM) framework featuring a fine-granular computing resource scaler in a cloud-native environment. The proposed scaler algorithm uses Artificial Intelligence (AI)/Machine Learning (ML) models to predict microservice performances; if a service degradation is detected, then a root-cause analysis is conducted using eXplainable AI (XAI). Based on the XAI output, the proposed framework scales only the needed (exact amount) resources (i.e., CPU or memory) to overcome the service degradation. The proposed framework and resource scheduler have been implemented on top of a cloud-native platform based on the well-known Kubernetes tool. The obtained results clearly indicate that the proposed scheduler with lesser resources achieves the same service quality as the default scheduler of Kubernetes.

Dynamic Time Duplex Division (D-TDD) is a promising solution to accommodate the new emerging 5G and 6G services characterised by asymmetric and dynamic Uplink (UL) and Downlink (DL) traffic demands. D-TDD dynamically changes the TDD configuration of a cell without interrupting users’ connectivity, hence balancing the bandwidth for UL or DL communication according to the traffic pattern. However, 3GPP standard does not specify algorithms or solutions to derive the TDD configuration, i.e., the number of slots to dedicate to UL and DL. In [1], we have proposed a Machine Learning (ML)-based solution relaying on Deep Reinforcement Learning (DRL) to allow the base station (or gNB) to self-adapt to the traffic pattern of the cell by periodically adapting the number of slots dedicated to UL and DL. In this work, we implemented the DRL algorithm on top of an open-source gNB based on OpenAirInterface (OAI) [2] to demonstrate its efficiency. To this end, we relied on the O-RAN architecture [3], where the proposed DRL algorithm is deployed as xApp at the Near Real-time RAN Intelligent Controller (RIC) and communicates with the base station using O-RAN E2 interface. We developed xTDD Service Model (SM) following the E2SM standard [3], allowing the DRL solution to monitor DL and UL buffers from the gNB to deduce the optimal TDD configuration that accommodates the current traffic. Then, the decision (i.e., TDD configuration) is pushed to the base station. We implemented the solution on top of the OAI 5G StandAlone (SA) platform and Flexric RIC [4]. To the best of our knowledge, this is the first demonstration of a ML-based D-TDD on top of a real 5G network, showing the advantage of O-RAN architecture to building Self Organized Network (SON) function for dynamic configuration of D-TDD.

Managing network slices in 5G networks and in communication technologies Beyond-5G (B5G) requires intelligent mechanisms to ensure users’ service access and to maximize the utility and efficiency of the network’s physical resources. To achieve this, we propose a mechanism based on Reinforcement Learning (RL) for the Admission Control (AC) of User Service Requests (USRs) into network slices through dynamic bandwidth (BW) reallocation. Our approach admits, delays or rejects USRs into service depending on the BW of the slice and the utility this generates for the infrastructure provider (InP). This approach achieves very low USR rejection rates (RRs) with very high resource efficiency, even when peak traffic loads considerably exceed the BW capacity causing resource scarcity scenarios. When compared against a static BW allocation mechanism, our approach achieves RRs that are a fraction (0,33) of those achieved by static (smaller RRs are better), with 33,2x less resource overallocation, significantly achieving a very high resource efficiency.

Network slicing is a promising technique that has vastly increased the manifoldness of network services to be supported through isolated slices in a shared radio access network (RAN). Due to resource isolation, effective resource allocation for coexisting multiple network slices is essential to maximize network resource efficiency. However, the increased network flexibility and programmability offered by virtualized radio access networks (vRANs) come at the expense of a higher consumption of computing resources at the network edge. Additionally, the relationship between resource efficiency and computing cost minimization is still fuzzy. In this paper, we first perform extensive experiments using the vRAN testbed we developed and assess the vRAN resource consumption under different settings and a varying number of users. Then, leveraging our experimental findings, we formulate the problem of cost-efficient network slice dimensioning, named cost-efficient slicing (CES), which maximizes the difference between total utility and CPU cost of network slices. Numerical results confirm that our solution leads to a cost-efficient resource slicing, while also accomplishing performance isolation and guaranteeing the target data rate and delay specified in the service level agreements.

Massive machine-type communications (mMTCs) network slices in 5G aim to connect a massive number of MTC devices, opening the door for a widened attack surface. Network slices are well isolated, resulting in a low impact on other running slices when attackers control IoT devices belonging to an mMTC network slice (i.e., in-slice attack). However, the impact of the in-slice attacks on the shared infrastructure components with other slices, such as the 5G core network (CN), can be harmful, considering the massive number that can be part of mMTC slice. In this article, we propose a zero-touch security management solution that uses machine learning (ML) to detect and mitigate in-slice attacks on 5G CN components, focusing on Distributed Denial-of-Service (DDoS) attacks. To this aim, we propose: 1) a novel closed-control loop that assists the 5G CN in detecting and mitigating attacks; 2) an ML algorithm that predicts the upper bound of expected MTC devices Attach Requests during a time interval (or an event); 3) a detection algorithm that analyzes an event and uses the ML output to compute a probability that a specific device has participated to an attack; 4) a mitigation algorithm that disconnects and blocks MTC devices suspected to be part of an attack; and (5) a proof-of-concept implementation on top of a 5G facility.

The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to automate the management and orchestration of running network slices. This requires heavy usage of advanced deep learning techniques in a closed-loop way to auto-build the suitable decisions, enabling to meet network slices’ requirements. In this context, Federated Learning (FL) is playing a vital role in training deep learning models in a collaborative way among thousands of network slice participants while ensuring their privacy and hence network slice isolation. Specifically, running network slices may share only their model parameters with a central entity, e.g., Inter Domain Slice Manager, to aggregate them and build a global model. Thus, the central entity does not directly access the training data. However, FL is vulnerable to poisoning attacks, where an insider participant may upload poisoning updates to the central entity so that it can cause a construction failure of the global model and thus affect its global performance. Therefore, it is crucial to design security means to detect and mitigate such threats. In this paper, we design a novel framework to automatically detect malicious participants in the FL process. In particular, our framework first uses a deep reinforcement algorithm to dynamically select a network slice as a trusted participant, based mainly on its reputation. The selected participant will then be in charge of identifying poisoning model updates by leveraging unsupervised machine learning. We demonstrate the feasibility of our framework on top of a real dataset that we generate using the 5G OpenAirInterface (OAI) platform. Evaluation results show the efficiency of our framework in dealing with poisoning attacks even with the presence of several malicious participants.

Edge computing is one of the key technology of the last decade, enabling several emerging services beyond 5G (e.g., autonomous driving, robotic networks, Augmented Reality (AR)) requiring high availability and low latency communications. While in cloud native paradigm, highly embraced by cloud providers, network functions and applications are decomposed into microservices run in a container. Defacto container orchestration engine, namely Kubernetes, deploys multiple containers inside a pod. The mapping between microservices and pod highly affects the availability and latency of deployed microservices and hence the run application. In this paper, we propose novel availability and latency-aware deployment models for an edge service composed of multiple applications designed as multiple microservices. The two considered deployments are analyzed and evaluated using experimentation and an analytical model, considering critical performance criteria for edge-oriented services, like availability and latency requirements.

5G networks and beyond are shifting from dominant Downlink (DL) traffic to a more equilibrate DL/UpLink (UL) and dominant UL traffic for specific emerging services. Particularly for ultra-Reliable and Low Latency Communications (uRLLC) services, the UL latency becomes an essential factor to consider. However, current UL scheduling methods are not efficient in terms of Physical Resource Blocks (PRBs) allocation, latency, or link adaptation. In this paper, we address the emerging challenge related to the UL latency in 5G networks and beyond. We introduce a solution based on Deep Reinforcement Learning (DRL) to dynamically allocate the future UL grant by learning from the dynamic traffic pattern. Simulation results demonstrate the efficiency of the proposed methodology in reducing the UL latency down to 0.25 ms and ensuring the generality by reacting to different traffic models.

The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration and management of running network slices, in Beyond 5G networks (B5G), with an unprecedented level of scalability. To achieve this vision, ZSM calls for a large usage of advanced deep learning algorithms, in order to dynamically build efficient decisions. In this context, Federated deep Learning (FL) proved their efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Indeed, FL-based solutions give “machine-centric” decisions about running network slices and their performance, which will be then executed/applied by managers, i.e., slice manager staff/module. However, FL-enabled solutions do not provide any details about why and how such decisions were made, and thus such decisions cannot be properly trusted/understood by slice managers. To alleviate this issue, we leverage eXplainable Artificial Intelligence (XAI) paradigm that aims to improve the transparency of black-box FL decision-making process. In particular, XAI helps to explain the FL-based decisions to make them interpretable/trustable by network slices managers. In this paper, we design a novel XAI-powered framework to explain FL-based decisions. We first build a deep learning model in federated way, to predict key performance indicators (KPI) of network slices. Our FL-based KPI prediction is useful for the configuration and the management of network slice lifecycle, especially for the Service Level Agreement (SLA) violation and the network slice re-configuration. Then, we develop several XAI models on the top of our FL-based model, such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), RuleFit, and Partial Dependence Plot (PDP), to enhance the level of trust, credibility (of the local data/model), transparency, and explanation of the FL-based decisions, while adhering the data privacy, to different B5G network stakeholders, such as slice managers. Experiments results show the efficiency of our XAI-powered framework, to explain FL-based decisions related to latency KPI predictions.

Edge computing is one of the critical components enabling low-latency demanding services in beyond 5G networks. Indeed, the deployed applications at the edge benefit from their close position to end-users to guarantee low latency access. Considering the case of a network slicing enabled network, we introduce Lightweight edge Slice Orchestration (LeSO) frame-work, a cloud-native oriented orchestrator that orchestrates and manages the deployment of micro-services as sub-slices at the edge. Whilst the existing orchestration frameworks are greedy of computing resource consumption and fail to integrate with the Multi-access Edge Computing (MEC) domain, LeSO by design is very lightweight and integrates a MEC platform-like component to guarantee traffic steering to automate edge slice deployment. Experiment results show that LeSO necessities a small amount of CPU and memory, even when a high number of edge slices are deployed.

5G New Radio (NR) introduces several key features to support the new emerging vertical industry use-cases, mainly: (1) Different numerology that gives more flexibility in managing time slot duration, and hence satisfying different delay requirements; (2) Bandwidth part that permits dedicating parts of the bandwidth to ensure different data rate requirements. However, although 5G NR introduces several enhancements, it makes radio resource management, more precisely resource scheduling, more complex and challenging. In this paper, we address the challenge of radio resource management in 5G NR featuring network slicing. We introduce a novel scheduling solution based on Deep Reinforcement Learning (DRL) to allocate resources and numerology for UEs to satisfy their different requirements. We evaluated the solution for different network configurations and compared its performance with the maximum achievable throughput. Simulation results demonstrated the efficiency of the proposed algorithm to allocate resources and the ability to scale for larger bandwidths covering both Frequency Range 1 (FR1) and FR2, as well as serving a higher number of User Equipment (UE).

Cloud-native rethinks the application architecture by embracing a micro-service approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the amount of CPU and memory limit to run their workload. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will impact the service performances and the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct in this paper an experimental study aiming to detect if a tenant’s configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results provide insights on how to detect and correct performance degradation due to misconfiguration of the service resource.

Service Function Chaining (SFC) is a networking concept by which traffic is steered through a set of ordered functions composing an end-to-end service. It represents one of the facilitating technologies for 5G, and is enabled by the Network Function Virtualization (NFV) and Software Defined Networks (SDN) paradigms. In the multi-domain context, SFC placement faces new challenges related to the lack of visibility on the local domain's networks. Indeed, the domain operators are often reluctant to unveil details on their topology to external parties. Furthermore, the new 5G use cases introduce new requirements for services such as end-to-end latency, and a minimal guaranteed bandwidth that the placement process needs to optimize simultaneously. In this article, we propose a centralized framework that allows SFC partitioning and embedding over multiple domains with a limited visibility over the global infrastructure. We model the multi-objective SFC placement problem using the Physical Programming method, which allows the expression of the Decision Maker's preferences through meaningful parameters, and propose an exact algorithm as well as a scalable heuristic solution. We then perform an extensive evaluation of the framework as well as the proposed algorithms. The results demonstrate our solution's effectiveness with a limited visibility on the network.

Industry 5.0 is rapidly growing as the next industrial evolution, aiming to improve production efficiency in the 21stcentury. This evolution relies mainly on advanced digital technologies, including Industrial Internet of Things (IIoT), by deploying multiple IIoT devices within industrial systems. Such a setup increases the possibility of threats, especially with the emergence of IIoT botnets. This can provide attackers with more sophisticated tools to conduct devastating IIoT attacks. Besides, machine learning (ML) and deep learning (DL) are considered as powerful techniques to efficiently detect IIoT attacks. However, the centralized way in building learning models and the lack of up-to-date datasets that contain the main attacks are still ongoing challenges. In this context, multiaccess edge computing (MEC) and federated learning (FL) are two promising complementary technologies. MEC brings computing capabilities at the edge of the industrial systems, while FL leverages the edge resources to enable a privacy-aware collaborative learning, especially in multiindustrial systems context. In this article, we design a novel MEC-based framework to secure IIoT applications leveraging FL, called FedGame. Specifically, FedGame enables multiple MEC domains to collaborate securely to deal with an IIoT attack, while preserving the privacy of IIoT devices. Moreover, a noncooperative game is formulated on the top of FedGame, to enable MEC nodes acquiring the needed virtual resources from the centralized MEC orchestrator, to deal with each type of IIoT attacks. We evaluate FedGame using real-world IIoT attacks; the experimental results show not only the accuracy of FedGame against centralized ML/DL schemes while preserving the privacy of Industrial systems but also its efficiency in providing required MECs resources and, thus, dealing with IIoT attacks.

An efficient and scalable monitoring system is a critical component for any network to monitor and validate the functioning of the running services and the underlying infrastructure. This is more valid in 5G, as it relies on the network slicing concept, which adds many challenges to the monitoring system. Besides data isolation and multi-tenancy support, network slices require monitoring different types of resources, like RAN, computing, memory, network data rate, which belong to different technological domains, managed by different entities. Moreover, the monitoring system needs to be scalable, as in 5G a high-number of running network slices is envisioned. In this paper, we devise a novel monitoring framework for network slicing ready mobile networks, which features: 1) scalable monitoring system that supports a high number of running network slices in parallel; 2) technological domain agnostic thanks to a novel data collection (or monitoring) communication protocol; 3) support of multi-tenancy in a cloud-native environment. The framework has been implemented in a 5G facility, and its performance has been extensively evaluated.

The deployment of 5G and 6G is highly motivated by the emerging network services that demand more band-width and very low latency. Besides, these services are shifting from dominant Downlink (DL) Traffic to a more equilibrate DL/UpLink (UL) and dominant UL traffic for specific emerging services. One option to accommodate this new behavior is to use Time Duplex Division (TDD), where the radio frame is shared between UL and DL time slots, namely UL/DL pattern. While 4G TDD has a fixed number of configurations that cannot be updated on runtime, 5GNR allows complete flexibility to define the UL/DL pattern. Therefore, 5G base stations can dynamically change the pattern to adapt to the type of traffic (i.e., UL or DL). However, the 5G standard does not specify algorithms or solutions to derive the UL/DL pattern. To fill this gap, we propose a Deep Reinforcement Learning (DRL) that adds intelligence to the base station to self-adapt to the traffic pattern of the cell type. The proposed DRL algorithm monitors UL and DL buffers at the 5G base station to derive the optimal UL/DL pattern in respect to the current traffic configuration. The proposed solution delivers the optimal configuration in a timely and efficient manner. Simulation results demonstrated the efficiency of the proposed algorithm to avoid buffer overflow and ensure the generality by reacting to traffic pattern changes.

Radio Link Failure (RLF) is a challenging problem in 5G networks as it may decrease communication reliability and increases latency. This is against the objectives of 5G, particularly for the ultra-Reliable Low Latency Communications (uRLLC) traffic class. RLF can be predicted using radio measurements reported by User Equipment (UE)s, such as Reference Signal Receive Power (RSRP), Reference Signal Receive Quality (RSRQ), Channel Quality Indicator (CQI), and Power HeadRoom (PHR). However, it is very challenging to derive a closed-form model that derives RLF from these measurements. To fill this gap, we propose to use Machine Learning (ML) techniques, and specifically, a combination of Long Short Term Memory (LSTM) and Support Vector Machine (SVM), to find the correlation between these measurements and RLF. The RLF prediction model was trained with real data obtained from a 5G testbed. The validation process of the model showed an accuracy of 98% when predicting the connection status (i.e., RLF). Moreover, to illustrate the usage of the RLF prediction model, we introduced two use-cases: handover optimization and UAV trajectory adjustment.

The fifth generation of the mobile network aims to revolutionize mobile communication by offering both unparalleled performance and broader service offerings. 5G technology responds to the growing demand for higher bandwidth and lower latency, caused by a significant increase of connected resources. Leveraging on Software-Defined Networking (SDN) and artificial intelligence (AI) technologies, the 6G system can autonomously adapt to user requirements. This paper proposes a framework, named intelligent optimization framework (IoF), that leverages both network optimization and machine learning techniques for achieving the best performance results. The IoF framework allows for finding an exemplary resource allocation configuration of the mobile network by leveraging SDN technology. This work aims to configure the SDN-enabled switches and Open vSwitchs (OVSs) to enable an optimized data plane that reduces the overall operational expense (OPEX) cost and capital expense (CAPEX) cost within an optimized execution time. The evaluation results show our proposed framework's efficiency for delivering optimal configurations by reducing the number of allocated OVSs in a reasonable execution time.

Service Function Chaining (SFC) has emerged as a promising technology for 5G and beyond. It leverages Network Function Virtualization (NFV) and Software Defined Networking (SDN) and allows the decomposition of a given service into a set of blocks that successively process data. The SFC placement issue has been extensively studied in the literature, and different solutions have been proposed using mathematical models and heuristics. More recently, Reinforcement Learning (RL) has emerged as a tool for decision-making that allows agents to elaborate policies based on the environment's feedback. In this paper, we study the benefits of using Deep Reinforcement Learning methods for the multi-domain SFC placement problem. We propose a Deep Deterministic Policy Gradient (DDPG) approach, where Linear Physical Programming is employed to generate rewards that reflect the solution's quality in terms of cost and latency. Through our experiments, we are able to demonstrate the efficiency of our approach with results that satisfy the SLA requirements.

Cloud-native technologies have recently entered the telecommunication world. These technologies were specially designed for developing and orchestrating container-based applications. The new cloud-native network functions use container based virtualization instead of virtual machine-based virtualization. These network functions have low resource footprints and low deployment time, making them suitable for a distributed environment. To adopt these new network functions and cloud native approach the network function virtualization vision needs alterations. In this paper, we use cloud-native approach to provide resilience to cloud-native network services. We proposed dynamic resource allocation and placement algorithm for modeling and placing a simple cloud-native network service. The algorithm aims to minimize infrastructural resource utilization under the constraint of abiding service availability mentioned in the service level agreement.

It is well established that 5G will impact not only the end-users by allowing several new services, but also the vertical industry and network operators business. 5G will open the business market to new stakeholders with the introduction of Network Slicing, namely the vertical or tenant, the network slice provider, and the infrastructure provider. The Network Slice provider sells end-to-end network slices (virtual end-to- end mobile network) to the vertical while leasing virtual and physical resources from Infrastructure Providers to enforce these end-to-end network slices. Accordingly, there is a need to establish Service Level Agreement (SLA) among these actors to ensure: (1) that the service is well-delivered to the vertical and (2) the infrastructure providers are respecting their involvement with the network slice provider. To fill this gap, in this paper, we propose a trust architecture to automatically manage the SLAs and apply penalties and compensations if the SLAs are not respected by one of the involved actors.

Service Function Chaining (SFC) is the process of decomposing a network service into multiple functions that successively process packets to deliver the end-to-end service. In a multi-domain context, SFC placement is a challenging problem due to limited knowledge of the infrastructure of the local domains, which complicates the process of finding the optimal placement solutions. On the other hand, Reinforcement Learning has gained momentum as a tool for decision-making, allowing agents to construct and improve policies using feedback from the environment. In this paper, we leverage Deep Reinforcement Learning (DRL) to perform SFC placement on multiple domains. We devise a hierarchical architecture where the local domain agents and the multi-domain agent are trained using different DRL models to perform SFC and sub-SFC placement while satisfying the SLA requirements.

The increased flexibility and interconnectivity of modern industrial communication networks, obtained through the use of innovative technologies like network function virtualization and software-defined networking, require a secure and manageable framework to support the new communication and computing needs. To focus on these requirements, this article proposes a framework for reliable placement of services across physically separated locations, which offers both system optimization, in terms of latency and resource utilization, and connectivity policy enforcement to guarantee service reliability, safety, and security. This is achieved by exploiting a new approach to solve the virtual network embedding problem, using optimization modulo theories (MaxSMT), which allows the use of very expressive constraints.

One of the main challenges when it comes to deploying Network Slices is slicing the Radio Access Network (RAN). Indeed, managing RAN resources and sharing them among network slices is an increasingly difficult task, which needs to be properly designed. The goal is to improve network performance and introduce flexibility and greater utilization of network resources by accurately and dynamically provisioning the activated network slices with the appropriate amounts of resources to meet their diverse requirements. In this paper, we propose a data-driven RAN slicing mechanism based on a resource sharing algorithm running at theSlice Orchestrator(SO) level. This algorithm computes the necessary radio resources to be used by each deployed network slice. These resources are adjusted periodically based on current estimates of achievable throughput performance derived from channel quality information, and in particular from the Channel Quality Indicator (CQI) values of the users of each network slice retrieved from the RAN. CQI information is reported to base stations by the User Equipment (UE) following standard procedures, but extracting and frequently reporting it from base stations to the SO may result in significant communication overhead. To mitigate this overhead while maintaining at the SO level an accurate view of UE channel qualities, we propose a machine learning approach to infer the stability of UE channel conditions, as well as predictive schemes to reduce the CQI reporting intensity based on the inferred channel status. Through extensive simulations, we demonstrate the efficiency of our data-driven RAN slicing framework, which allows to meet the stringent requirements of two main classes of network slices in 5G, i.e., enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communication (URLLC).

The imminent arrival of 5G will drive changes in communications service provider networks, leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies. The upcoming 5G ecosystem will address vertical markets to give rise to a plethora of novel services with different requirements, such as Ultra-Reliable Low-Latency Communication (URLLC), machine Massive Type Communications (mMTC), and enhanced Mobile Broadband (eMBB). To be able to offer such variety of services with different needs over the same network infrastructure, this latter should be split into multiple logical networks known as network slices. In this paper, we propose an architecture design for 5G network slicing inspired by ONF and 3GPP models. We provide and analyse our experiment results of instantiating multiple networks slices with our scalable 4G/5G VNFs in a Network Slice as a Service (NSaaS) approach on a real platform.

Unmanned Aerial Vehicle (UAV) applications and services have gained a huge deployment and adoption in different fields, such as the military domain (Defense or reconnaissance) and the civilian domain (Healthcare, surveillance, and transport). UAV operations are generally critical and require, during operations, a control link with the drones, which should be reliable with very low latency. To ensure low-latency, 5G architecture intends to deploy Mobile Edge Computing (MEC) servers, which provide cloud computing capabilities close to the end-users. Consequently, it is envisioned that the AutoPilot application will be deployed at the MEC in order to ensure a low latency connection to the drones. However, the high mobility of drones makes the migration of the AutoPilot applications among MEC servers unavoidable; in order to maintain a low latency connection with the flying drones. This may lead to frequent downtime of the service, which may impact the AutoPilot performances, and hence service migrations should be limited as much as possible. Accordingly, this paper aims to reduce the number of service migrations of drones by introducing novel algorithms that act at the mission planning phase, where the path of the drones is defined.

Service Function Chaining (SFC) refers to the process of steering packets between a set of functions to deliver an end-to-end service. It is considered as one of the enabling technologies for 5G along with Software Defined Networks (SDN) and Network Function Virtualization (NFV). One of the challenges for SFC deployment is the end-to-end orchestration, particularly in a multi-domain scenario where additional issues, such as the lack of visibility and control, and interoperability, need to be taken into account. In this paper, we propose a novel framework that leverages on and extends existing standards in order to perform an end-to-end cross-domain orchestration of SFCs, and ensure the desired forwarding of packets between the domains. A Proof of Concept implementation of our approach is also deployed and evaluated.

Channel quality feedback is crucial for the operation of 4G and 5G radio networks, as it allows to control User Equipment (UE) connectivity, transmission scheduling, and the modulation and rate of the data transmitted over the wireless link. However, when such feedback is frequent and the number of UEs in a cell is large, the channel may be overloaded by signaling messages, resulting in lower throughput and data loss. optimizing this signaling process thus represents a key challenge. In this paper, we focus on Channel Quality Indicator (CQI) reports that are periodically sent from a UE to the base station, and propose mechanisms to optimize the reporting process with the aim of reducing signaling overhead and avoiding the associated channel overloads, particularly when channel conditions are stable. To this end, we apply machine learning mechanisms to predict channel stability, which can be used to decide if the CQI of a UE is necessary to be reported, and in turn to control the reporting frequency. We study two machine learning models for this purpose, namely Support Vector Machines (SVM) and Neural Networks (NN). Simulation results show that both provide a high prediction accuracy, with NN consistently outperforming SVM in our settings, especially as CQI reporting frequency reduces.

Multi-access Edge Computing (MEC) is one of the key enablers in 5G, where the objective is to bring computation very close to the end users. MEC, as defined by ETSI, introduces several services that can be exposed to MEC applications regarding the mobile users, such as the Radio Network Information Service (RNIS) and the Location Service, which provide low-level information on mobile users (e.g., Channel Quality Indicator - CQI), allowing the development of context-aware edge applications. In this paper, we address the challenging question of where to deploy a set of MEC applications on a federated edge infrastructure so as to meet the applications' requirements in terms of computing resources and latency, while ensuring that the MEC platform services required by each application are available at the selected edge locations. We formulate this service placement problem as an Integer Linear Program, which aims at balancing the computing load between available Mobile Edge Platforms (MEP), while respecting application latency and MEP service availability constraints. This problem is shown to be NP-hard. To solve it computationally efficiently, we propose an algorithm based on the Tabu-Search (TS) meta-heuristic. Via simulation, we demonstrate the efficiency of our scheme in balancing computational load among available MEPs and its ability to optimize service placement.

To achieve the vision of Zero Touch Management (ZSM) of network slices in 5G, it is important to monitor and predict the performances of the running network slices, or their Key Performance Indicator (KPI). KPIs are usually monitored, but also with the advance of Machine Learning (ML) techniques are predicted, aiming at proactively reacting to any service degradation of running network slices. While network- and computation-oriented KPIs can be easily monitored and predicted, service-oriented KPIs are difficult to obtain due to the privacy issue, as they disclose critical information on the performance of services. To tackle this issue, in this paper, we propose to use a new ML technique, known as Federated Learning (FL), which consists of keeping raw data where it is generated, while sending only users' local trained models to the centralized entity for aggregation. Hence, making FL as an adequate candidate to be used for predicting slices' service-oriented KPIs.

The 5th generation of the cellular mobile communication system (5G) is in the meantime stepwise being deployed in mobile carriers' infrastructure. Various standardization tracks as well as research activity are investigating the exploitation of the very flexible 5G system architecture for customized deployments, meeting requirements of the vertical industry, such as for automotive, factory, or smart city. A very common base is a cloud-native development and decentralized deployment of the 5G system along with services in distributed resources per the Multi-Access Edge Computing (MEC) architecture to locate services topologically close to (mobile) users, e.g. along public roads, and to enable low-latency communication with local services. Automated management of such a distributed deployment in an agile environment is a prerequisite. This paper investigates the use of Recurrent Neural Networks (RNN) for accurate user mobility prediction in an automotive scenario. By the use of simulated vehicular traffic, a suitable RNN configuration using Long Short-Term Memory (LSTM) has been found, which provides accurate prediction results. Proof of value has been accomplished by an experimental decision algorithm, which balances the use of available distributed resources through service scale, migration or replication decisions while meeting mobile users' expectation on the experienced service quality.

We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider's request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.

Network slicing is one of the key components allowing to support the envisioned 5G services, which are organized in three different classes: Enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and Ultra-Reliable and Low-Latency Communication (URLLC). Network Slicing relies on the concept of Network Softwarization (Software Defined Networking - SDN and Network Functions Virtualization - NFV) to share a common infrastructure and build virtual instances (slices) of the network tailored to the needs of different 5G services. Although it is straightforward to slice and isolate computing and network resources for Core Network (CN) elements, isolating and slicing Radio Access Network (RAN) resources is still challenging. In this paper, we leverage a two-level MAC scheduling architecture and provide a resource sharing algorithm to compute and dynamically adjust the necessary radio resources to be used by each deployed network slice, covering eMBB and URLLC slices. Simulation results clearly indicate the ability of our solution to slice the RAN resources and satisfy the heterogeneous requirements of both types of network slices.

In contrast to the current host-centric architecture, Information-Centric Networking (ICN) adopts content naming instead of host address and in-network caching to enhance the content delivery, improve the data distribution, and satisfy users' requirements. As ICN is being incrementally deployed in different real-world scenarios, it will exist with IP-based services in a hybrid network setting. Full deployment of ICN and total replacement of IP protocol is not feasible at the current stage since IP is dominating the Internet. On the other hand, re-designing TCP/IP applications from ICN perspective is a time-consuming task and requires a careful investigation from both business and technical point of view. Thus, the coexistence of ICN and IP is one of the suitable solutions. Towards this end, we propose a simple yet efficient coexistence solution based on Network Function Virtualization (NFV) technology. We define a set of communication regions and control virtual functions. A gateway node is used as an intermediate entity to fetch and deliver content over regions. The simulation results show that the proposed approach is valid and allow content fetching and delivering from different ICN and/to IP regions in an efficient manner.

The Radio Network Information Service (RNIS) is one of the key services provided by a Multi-access Edge Computing Platform (MEP), as specified in the relevant ETSI MEC standards. It is responsible for interacting with the Radio Access Network (RAN), collecting RAN-level information about User Equipment (UE) and exposing it to mobile edge applications, which can in turn utilize it to dynamically adjust their behavior to optimally match the RAN conditions. Putting the provision of RNIS in the context of the emerging MEC-in-NFV environment, where the components and services of the MEC architecture, including the MEP itself, are integrated in an NFV environment and are delivered on top of a virtualized infrastructure, we present our standards-compliant RNIS implementation based on OpenAirInterface and study critical performance aspects for its provision as a virtual function. Since the RNIS design and operation follows the publish-subscribe model, we provide alternative implementations using different message brokering technologies (RabbitMQ and Apache Kafka), and compare their use and performance in an effort to evaluate their suitability for providing RNIS in an as-a-service manner.

IEEE 802.11ac defines Multi-User MIMO (MU-MIMO) in the downlink that allows the Access Point (AP) to transmit multiple spatial streams to different receivers, simultaneously. However, MU-MIMO requires a periodic channel calibration which incurs a significant overhead. On the other hand, a Single User (SU) transmission may be used in both downlink and uplink. It allows the sender to transmit one (i.e. SISO) or multiple streams (i.e. SU-MIMO) to the same receiver. We note that SU transmissions do not require the channel calibration. In this paper, we compare the throughput of SISO, SU-MIMO and MU-MIMO, analytically, in an ideal condition (i.e. no frame losses) to understand which technique is more efficient and when. We measure the throughput for different channel widths, data rates, A-MPDU lengths, channel calibration rates and network sizes. Our results show that SU-MIMO is typically more efficient when the receiver supports the same number of spatial streams than the AP. But if the receivers support a single stream, MU-MIMO improves the network efficiency for a limited number of stations. Besides, the MU-MIMO scalability depends on the channel calibration rate and the number of receivers.

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

The trend towards the cloudification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. Follow-Me Cloud applies a Markov-decision-process-based algorithm for cost-effective performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either software defined networking technologies or the locator/identifier separation protocol. Numerical results from our analytic model for follow-me cloud, as well as testbed experiments with the two alternative follow-me cloud implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the extended virtual sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.

One of the requirement of 5G is to support massive number of connected devices, considering many use-cases such as IoT and massive Machine Type Communication (MTC). While this represents an interesting opportunity for operators to grow their business, it will need new mechanisms to scale and manage the envisioned high number of devices and their generated traffic. Particularity, the signaling traffic, which will overload the 5G core Network Function (NF) in charge of authentication and mobility, namely Access and Mobility Management Function (AMF). The objective of this paper is to provide an algorithm based on Control Theory allowing: (i) to equilibrate the load on the AMF instances in order to maintain an optimal response time with limited computing latency; (ii) to scale out or in the AMF instance (using NFV techniques) depending on the network load to save energy and avoid wasting resources. Obtained results via computer system indicate the superiority of our algorithm in ensuring fair load balancing while scaling dynamically with the traffic load.

The upcoming mobile core network, which will be based on Virtual Network Functions (VNF), will face an increase of data traffic on both data and control planes. This is due to the increase of the number of connected devices and the newly 5G supported-services like IoT, Connected Health Care etc. Therefore dynamic and accurate scalability techniques should be envisioned in order to answer the needs, in term of resource provisioning, without degrading the Quality Of Service (QoS) already offered by hardware based core networks. Although provisioning new resources is easier as it is a matter of software deployment, the strategy to use (when to scale and how much to scale) remains complex. In this paper we propose scaling techniques based on neural networks to forecast the upcoming load. Hence scheduling the resource provisioning should be in a manner that all the needed resources will be deployed and active when the load increases. In the same way, it will scale-in the unneeded resources when the traffic load decreases. The proposal is tested via discrete event simulations using a traffic load dataset provided by a Network Operator. The results show clearly the robustness of our proposal compared to a threshold-based scaling technique.

Multi-access Edge Computing (MEC) is gaining momentum as it is considered as one of the enablers of 5G ultra-Reliable Low-Latency Communications (uRLLC) services. MEC deploys computation resources close to the end user, enabling to reduce drastically the end-to-end latency. ETSI has recently leveraged the MEC architecture to run all MEC entities, including MEC applications, as Virtual Network Functions (VNF) in a Network Functions Virtualization (NFV) environment. This evolution allows taking advantage of the mature architecture and the enabling tools of NFV, including the potential to apply a variety of service-tailored function placement algorithms. However, the latter need to be carefully designed in case of MEC applications such as uRLLC, where service access latency is critical. In this paper, we propose a novel placement scheme applicable to a MEC in NFV environment. In particular, we propose a formulation of the problem of VNF placement tailored to uRLLC as an optimization problem of two conflicting objectives, namely minimizing access latency and maximizing service availability. To deal with the complexity of the problem, we propose a Genetic Algorithm to solve it, which we compare with a CPLEX implementation of our model. Our numerical results show that our heuristic algorithm runs efficiently and produces solutions that approximate well the optimal, reducing latency and providing a highly-available service.

IEEE 802.11n/ac are two recent enhancements that increase the data rates of WLANs significantly thanks to the use of channel bonding, spatial multiplexing, an additional short guard interval, and new modulation and coding schemes. They offer a maximum transmission rate of 600 Mbps for 802.11n and 7 Gbps for 802.11ac. Due to regulatory power constraints, the sender may be obliged to divide its transmission power over different sub-channels and spatial streams. This allows the sender to respect the regulatory requirements but reduces the range of wide channels and multiple streams compared to narrow channels and single stream. Besides, the use of spatial multiplexing does not allow the receiver to take a full advantage of the diversity gain. This is another factor that reduces the range of Multiple Input Multiple Output (MIMO) transmissions. So increasing the channel width and the number of spatial streams reduces the communication range significantly. Therefore, legacy 20 MHz channels with a single stream transmission may offer higher throughput than wide channels with multiple spatial streams. This affects the performance of rate adaptation algorithms. In this paper we introduce the power constraints that should be respected in WLANs and their impact on the range of 802.11n/ac data rates. We show that increasing the channel width and the number of spatial streams reduces the transmission range. Then we define a rate ordering scheme that selects the best data rates among those available. Our scheme intends to improve most rate adaptation algorithms, such as MinstrelHT. Finally we show, using simulation that our method enhances the throughput and the stability of MinstrelHT.

3D Video games are considered as one of the most complex applications in the market, due to their real-time constraint and high computational requirements. Compared to dedicated gaming boxes (e.g., Xbox and PlayStation), mobile devices, including smartphones and tablets, still fail to achieve interactive rendering rates, even with low gaming requirements. Two solutions are offered to mobile gamers, leveraging resourceful platforms. The first one is the cloud gaming; wherein the entire game engine is hosted on dedicated servers in the cloud. The servers render the frames and stream back an encoded video to the player. The latter interacts with the servers through Human Interface Device (HID), and uses a decoder to display the streamed video. The second solution is known as computation offloading that consists in migrating parts of the game engine tasks (the most resource consuming) to a remote powerful computer hosted in the cloud or at the network edge (using the concept of Edge computing). After the successful execution, the results are sent back to the mobile device for integration with the rest of the application inside the mobile device. In this paper, we unveil an offloading solution to improve the performance of one of the most popular game engines in the market, namely "Unity 3D". Using a testbed composed of a smartphone and a server, we evaluate and compare the performance of the proposed solution by report to the classical solution (i.e., running all the game on the smartphone); particularly focusing on the ability to improve the capacity of the smartphone to run complex games.

Envisioned low-latency services in 5G, like automated driving, will rely mainly on Multi-access Edge Computing (MEC) to reduce the distance, and hence latency, between users and the remote applications. MEC hosts will be deployed close to mobile base stations, constituting a highly distributed computing platform. However, user mobility may raise the need to migrate a MEC application among MEC hosts to ensure always connecting users to the optimal server, in terms of geographical proximity, Quality of Service (QoS), etc. However, service migration may introduce: (i) latency for users due to the downtime duration; (ii) cost for the network operator as it consumes bandwidth to migrate services. One solution could be the use of service replication, which pro-actively replicates the service to avoid service migration and ensure low latency access. Service replication induces cost in terms of storage, though, requiring a careful study on the number of service to replicate and distribute in MEC. In this paper, we propose to compare service migration and service replication via an analytical model. The proposed model captures the relation between user mobility and service duration on service replication as well as service migration costs. The obtained results allow to propose recommendations between using service migration or service replication according to user mobility and the number of replicates to use for two types of service.

New generation radio equipment, used by soldiers and vehicles on the battlefield, form ad hoc networks and specifically, Mobile Ad hoc NETworks (MANET). The battlefields where these equipment are deployed include a majority of coalition communication. Each group on the battleground may communicate with other members of the coalition and establish inter-MANET links. These interMANET links are governed by routing policies that can be summarized as Allowed or Denied link. However, if more than two groups form a coalition, blocked multihop communications and non-desired transmissions due to these restrictive policies would appear. In this paper, we present these blocking cases and theoretically evaluate their apparition frequency. Then, we present two alternatives to extend the binary policies and decrease the number of blocking cases. Finally, we describe an experimental scenario containing a blocking case and evaluate our propositions and their performance.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.

Many ongoing research activities relevant to 5Gmobile systems concern the virtualization of the Evolved Packet Core (EPC) elements aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual Evolved Packet Core (vEPC) will principally rely on some key technologies, such as Network Function Virtualization (NFV), Software Defined Networking (SDN) and Cloud Computing, for enabling the concept of Mobile Carrier Cloud. The key idea beneath this concept, known also as EPC as a Service (EPCaaS), consists in deploying virtual instances (i.e., Virtual Machines or Containers) of key core network functions (i.e., Virtual Network Functions - VNF), such as the Mobility Management Entity (MME), Serving GateWay (SGW), and Packet Data network gateWay (PGW) over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the Quality of Service (QoS) while reducing the deployment cost. Our contribution, in this paper, is to devise an algorithm that derives the optimal number and locations of vEPC's virtual instances over the federated cloud. The proposed algorithm is based on coalition formation game, wherein the aim is to build optimal coalitions of Cloud Networks (CNs) to host the virtual instances of the vEPC elements. The obtained results clearly indicate the advantages of the proposed algorithm in ensuring QoS given a fixed cost for vEPC deployment, while maximizing the profits of cloud operators.

Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC)/5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC/5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC/5G core deployment, while maximizing the profits of cloud operators.

We address the fundamental tradeoff between deployment cost and service availability in the context of on-demand content delivery service provision over a telecom operator's network functions virtualization infrastructure. In particular, given a specific set of preferences and constraints with respect to deployment cost, availability and computing resource capacity, we provide polynomial-time heuristics for the problem of jointly deriving an appropriate assignment of computing resources to a set of virtual instances and the placement of the latter in a subset of the available physical hosts. We capture the conflicting criteria of service availability and deployment cost by proposing a multi-objective optimization problem formulation. Our algorithms are experimentally shown to outperform state-of-the-art solutions in terms of both execution time and optimality, while providing the system operator with the necessary flexibility to balance between conflicting objectives and reflect the relevant preferences of the customer in the produced solutions.

Mobile edge computing (MEC) emerges as a promising paradigm that extends the cloud computing to the edge of pervasive radio access networks, in near vicinity to mobile users, reducing drastically the end-to-end access latency to computing resources. Moreover, MEC enables the access to upto-date information on users' network quality via the radio network information service (RNIS) application programming interface (API), allowing to build novel applications tailored to users' context. In this paper, we present a novel framework for offloading computation tasks, from a user device to a server hosted in the mobile edge (ME) with highest CPU availability. Besides taking advantage of the proximity of the MEC server, the main innovation of the proposed solution is to rely on the RNIS API to drive the user equipment (UE) decision to offload or not computing tasks for a given application. The contributions are two-fold. First, the design of an application hosted in the ME, which estimates current value of round trip time (RTT) between the UE and the ME, according to radio quality indicators available through RNIS API, and provide it to the UE. Second, the elaboration of a novel computation algorithm which, based on the estimated RTT coupled with other parameters (e.g., energy consumption), decide when to offload UE's applications computing tasks to the MEC server. The effectiveness of the proposed framework is demonstrated via testbed experiments featuring a face recognition application.

We focus on the problem of optimal compute resource allocation and placement for the provision of a virtualized Content Delivery Network (CDN) service over a telecom operator's Network Functions Virtualization (NFV) infrastructure. Starting from a Quality of Experience (QoE)-driven decision on the necessary amount of CPU resources to allocate to satisfy a virtual CDN deployment request with QoE guarantees, we address the problem of distributing these resources to virtual machines and placing the latter to physical hosts, optimizing for the conflicting objectives of management cost and service availability, while respecting physical capacity, availability and cost constraints. We present a multi-objective optimization problem formulation, and provide efficient algorithms to solve it by relaxing some of the original problem's assumptions. Numerical results demonstrate how our solutions address the trade-off between service availability and cost, and show the benefits of our approach compared with resource placement algorithms which do not take this trade-off into account.

IEEE 802.11ac is a recent amendment that enhances the throughput of WLANs. It uses spatial diversity, new modulation and coding schemes (MCS), and channel bonding to increase the data rate. The channel bonding allows 802.11ac stations, also called Very High Throughput (VHT) stations, to operate on channels wider than the legacy 20 MHz channel in the 5 GHz band. Particularly, a VHT station may support up to 160 MHz transmissions. Increasing the channel width enhances the data rate but reduces the number of non-overlapping channels. For example, the 5 GHz spectrum in Europe offers either 19 nonoverlapping 20 MHz channels or only two 160 MHz channels. In dense WLAN deployment environments, the use of channel bonding increases the number of networks and stations sharing the same medium, and may increase the collisions rate. In this paper we show that the spectrum utilization increases when it is divided into multiple narrow channels instead of fewer wide channels. This increase is very significant when the frame aggregation is disabled. We show that 8 × 20 MHz channels may offer 252 Mbps compared to only 51 Mbps in a 160 MHz channel.

Mobile Edge Computing (MEC) consists of deploying computing resources (CPU, storage) at the edge of mobile networks; typically near or with eNodeBs. Besides easing the deployment of applications and services requiring low access to the remote server, such as Virtual Reality and Vehicular IoT, MEC will enable the development of context-aware and context-optimized applications, thanks to the Radio API (e.g. information on user channel quality) exposed by eNodeBs. Although ETSI is defining the architecture specifications, solutions to integrate MEC to the current 3GPP architecture are still open. In this paper, we fill this gap by proposing and implementing a Software Defined Networking (SDN)-based MEC framework, compliant with both ETSI and 3GPP architectures. It provides the required data-plane flexibility and programmability, which can on-the-fly improve the latency as a function of the network deployment and conditions. To illustrate the benefit of using SDN concept for the MEC framework, we present the details of software architecture as well as performance evaluations.

We present the design and implementation of a content-delivery-network-as-a-service (CDNaaS) architecture, which allows a telecom operator to open up its cloud infrastructure for content providers to deploy virtual content delivery network (CDN) instances on demand, at regions where the operator has presence. Using northbound REST APIs, content providers can express performance requirements and demand specifications, which are translated to an appropriate service placement on the underlying cloud substrate. Our architecture is extensible, supporting various different CDN flavors, and, in turn, different schemes for cloud resource allocation and management. In order to decide on the latter in an optimal manner from an infrastructure cost and a service quality perspective, knowledge of the performance capabilities of the underlying technologies, and compute resources is critical. Therefore, to gain insight which can be applied to the design of such mechanisms, but also with further implications on service pricing and SLA design, we carry out a measurement campaign to evaluate the capabilities of key enabling technologies for CDNaaS provision. In particular, we focus on virtualization and containerization technologies for implementing virtual CDN functions to deliver a generic HTTP service, as well as an HTTP video streaming one, empirically capturing the relationship between performance and service workload, both from a system operator and a user-centric viewpoint.

Mobile gaming is an emerging concept wherein gamers are using mobile devices, like smartphones and tablets, to play best-seller games. Compared to dedicated gaming boxes or PCs, these devices still fall short of executing newly complex 3D video games with a rich immersion. Three novel solutions, relying on cloud computing infrastructure, namely, computation offloading, cloud gaming, and client-server architecture, will represent the next generation of game engine architecture aiming at improving the gaming experience. The basis of these aforementioned solutions is the distribution of the game code over different devices (including set-top boxes, PCs, and servers). In order to know how the game code should be distributed, advanced knowledge of game engines is required. By consequence, dissecting and analyzing game engine performances will surely help to better understand how to move in these new directions (i.e., distribute game code), which is so far missing in the literature. Aiming at filling this gap, we propose in this article to analyze and evaluate one of the famous engines in the market, that is, “Unity 3D.” We begin by detailing the architecture and the game logic of game engines. Then, we propose a test-bed to evaluate the CPU and GPU consumption per frame and per module for nine representative games on three platforms, namely, a stand-alone computer, embedded systems, and web players. Based on the obtained results and observations, we build a valued graph of each module, composing the Unity 3D architecture, which reflects the internal flow and CPU consumption. Finally, we made a comparison in terms of CPU consumption between these architectures.

To enhance video streaming experience for mobile users, we propose an approach towards Quality-of-Experience (QoE) aware on-the-fly transcoding. The proposed approach relies on the concept of Mobile Edge Computing (MEC) as a key enabler in enhancing service quality. Our scheme involves an autonomic creation of a transcoding service as a Virtual Network Function (VNF) and ensures dynamic rate switching of the streamed video to maintain the desirable quality. This edge-assistive transcoding and adaptive streaming results in reduced computational loads and reduced core network traffic. The proposed solution represents a complete miniature content delivery network infrastructure on the edge, ensuring reduced latency and better quality of experience.

The switch in video delivery from traditional medium to Over-The-Top (OTT) based services has been a game changer for stakeholders of the media delivery ecosystem. It has changed the value chain enabling Content Delivery Networks and Content Providers to take the lion's share at the expense of Internet/Network Service Providers. Even if establishing collaboration between them naturally appears as the key to provide good Quality of Service to the end-users, they struggle in finding efficient and fair ways to do so. This paper directly tackles this problem and proposes a new model for content delivery actors to collaborate over a Virtualized Infrastructure, fairly balancing the revenue stream created. We list the main challenges and the new technical opportunities to solve them, among which the deployment of a distributed Network Function Virtualization (NFV) platform at the edge of the Internet Service Provider's network where a virtual Content Delivery Network (vCDN) is proposed to be deployed. Furthermore, we apply a game-theoretic analysis to study different ISP-CDN collaboration models and identify optimality conditions for our proposed CDN-as-a-Virtual-Network-Function approach.

To integrate Software Defined Networking (SDN) in the envisioned 5G system, a separation of the control and user data plane functions of the Evolved Packet Core (EPC) is required. This separation will impact mainly the functions available at the Serving GateWay (SGW) and Packet data GateWay (PGW) elements, and will result in two new entities; i.e. the S/PGW-C and S/PGW-U (PGW-C and PGW-U). The S/PGW-C integrates all the control plane functions (such as signaling and tunnel creation), while S/PGW-U contains only forwarding functions. The S/PGW-C will control the S/PGW-U in order to forward the UE traffic to the appropriate destinations by enforcing rules e.g., using the Openflow protocol. Usually, the S/PGW-C will run as a Virtual Network Function (VNF) running on a Virtual Machine or Container instantiated over a federated cloud. In this paper, we focus on the problem of the SGW-C placement, where a tradeoff is needed between reducing the SGW relocation frequency and balancing the traffic load among the underlying SGW-C VNFs. We formulate this problem using optimisation models, and a fair solution (i.e. Pareto optimal) is derived using Nash Bargaining game and the threat point.

We focus on the provision of Content-Delivery-Network-as-a- Service (CDNaaS) functionality for video distribution and, in particular, on how to appropriately decide on the amount of computing resources to allocate to a CDNaaS instance to satisfy Quality of Experience (QoE) and resource capacity constraints. Our work is put in the context of a telco cloud environment, which is open for content providers to request the dynamic deployment of a virtual CDN infrastructure on top of it, to cover a target user demand with a desired service quality at the regions where the telecom operator has presence. We perform extensive testbed experiments to quantify the dependence of user experience on the load of a virtualized video service, and apply our measurement results to drive a QoE-aware virtual CPU resource allocation algorithm. Using simulation, we show our algorithm to optimally address the quality-cost tradeoff, demonstrating the benefits of QoE-awareness.

Typically, maintaining static pool of cloud resources to meet peak requirements with good service quality makes the cloud infrastructure costly. To cope with this, this paper proposes an approach that enables a cloud-infrastructure to automatically and dynamically scale-up or scale-down resources of a virtualized environment aiming for efficient resource utilization and improved quality of experience (QoE) of the offered services. The QoE-aware approach ensures a truly elastic infrastructure, capable of handling sudden load surges while reducing resource and management costs. The paper also discusses the applicability of the proposed approach within the ETSI NFV MANO framework for cloud-based 5G mobile systems.

Internet Service Providers are becoming more involved in the audiovisual content delivery chain. One manifestation of this trend is the emergence of telco CDNs, i.e., content delivery networks operated by telecom service providers. In this work, we make the case for opening the telco CDN infrastructure to content providers by means of network function virtualization (NFV) and cloud technologies. We design and implement a CDN-as-a-Service architecture, where content providers can lease CDN resources on demand at regions where the ISP has presence. Using open northbound RESTful APIs, content providers can express performance requirements and demand specifications, which can be translated to an appropriate service placement on the underlying cloud substrate. To gain insight which can be applied to the design of such service placement mechanisms, we evaluate the capabilities of key enabling virtualization technologies by extensive testbed experiments.

New generation military equipment, soldiers and vehicles, use wireless technology to communicate on the battlefield. During missions, they form an ad hoc network, namely Mobile Ad hoc NETwork (MANET). Since the battlefield includes coalition, each group may communicate with another group, and inter-MANET communication may be established. Inter-MANET (or interdomain MANET) communication should allow communication, but maintain a control on the exchanged information. Several protocols have been proposed in order to handle inter-domain routing for tactical MANETs. In this paper, we review these protocols and highlight the general issues they solve. Then, we compare the behavior of a simulator (NS3), an emulator (CORE) and a real platform (using laptops) on simple network characteristics at Network and Data Link layers level. We show that there are behavioral differences among these three validation tools, and particularly those based on softwares (NS3 and CORE), which create problems that does not exist in reality. Consequently, most existing protocols are more complex than they should be. Based on this analysis and real behavior, we propose some preconization to design Inter-domain protocols for MANET.

In this paper we address the problem of Software Defined Networking (SDN) controller placement in large networks. Indeed, to solve the scalability issue raised by the centralized architecture of SDN, multi-controllers deployment (or distributed controllers system) is envisioned. However, the number and the location of controllers in large networks remain an issue. In this context, several works have been proposed to find the optimal placement of SDN controllers. Most of them consider latency among SDN controllers and switches as the main metric. In this work, we go beyond the state of art by proposing a solution that considers at the same time three critical objectives for the optimal placement of controllers: (i) the latency and communication overhead between switches and controllers; (ii)the latency and communication overhead between controllers; (iii) the guarantee of load balancing between controllers. We then solve the system by using Bargaining Game in order to find a fair trade off between these objectives. Simulation results clearly demonstrate the effectiveness of the proposed solution in finding the optimal placement of controllers that enforces this trade-off.

In Long Term Evolution (LTE) networks, User Equipments (UE)s should proceed Random Access CHannel (RACH) procedure to attach to the Base Station and access the channel. One limitation of this procedure is the congestion that may appear when high number of UEs are simultaneously trying to attach to the channel. Such use-case happens when high number of Machine Type Communication (MTC) devices are deployed in one LTE cell. In order to evaluate the RACH performances, in terms of success, collision and idle probabilities, when the traffic load is high, accurate models are needed. However, most of existing models ignore one important constraint, which is the fact that the eNB can knowledge only a limited number of UEs in each RACH round, leading to a mis-formulation of these metrics in the context of LTE, and especially in the presence of high number of devices competing for the channel access. In this paper, we tackle the above-mentioned issue by devising a new model for the RACH procedure taking in consideration this constraint. Computer simulation demonstrates that unlike the existing models, our proposed model achieve high accuracy to estimate the performance of the RACH procedure, whatever the traffic load.

Small Cells (SCs) are considered as a key enabling technique for future 5G cellular networks; whether they are deployed for Macro Cells networks densification or ensuring a standalone broadband access service. However, one of the critical challenges facing SCs deployments is a stable and an economical backhaul network. Particularly, if we consider SC for green-field deployments where operator transport infrastructure is bad or absent. Indeed, the question that arises in this specific case: what is the backhaul design that best meets economic constraints and end users Quality of Service (QoS) requirements? In this paper, we propose a novel method to design a cost-efficient SCs backhaul for green field deployment, while respecting linking technologies constraints. In fact, we formulate the problem of backhaul planning as a Mixed Integer Linear Programming (MILP), wherein the objective is to minimize SCs-Core Network connection cost while providing necessary access to operator mobile broadband by using a set of different technologies. Implementation results corroborate the benefit of wireless backhaul over wired one, and give to mobile operator insightful guidelines for designing a SCs backhauls for green field deployment.

The emerging 5G systems are envisioned to support higher data volumes and a plethora of different services with diverse QoS demands. To accommodate such service requirements, a cost efficient and flexible network architecture considering different service types is desired. The adoption of C-RAN can reduce infrastructure costs especially for dense deployments while at the same time centralize and hence optimize certain operations related with the control and data plane of the associated cells. This paper investigates such C-RAN approach in the context of TDD networks enabling a cell-less experience for users residing within overlapping areas. In particular, users are allowed to utilize selected sub-frames from different cells forming, in this way, a customized cell-less frame in a flexible manner. A queueing model and analysis is provided for optimizing power control and delay targets. A simulation study shows that our cell-less proposal significantly advances the state of the art both in terms of application and system performance.

Recent studies foresee that there would be roughly 50 billion of machine type communication (MTC) devices by 2020. Coping with the massive signaling overhead expected from these devices in 5G network is an important hurdle to tackle. In this paper, we have proposed two optimal solutions that use Device-to-Device (D2D) communications to lightweight the overhead of MTC devices on 5G network. Each scheme has a specific objective, and aims to manage the communications between devices and eNodeBs to achieve its objective. The proposed solutions nominate the devices that should communicate through D2D communication fashion and those that should directly communicate with eNodeBs. The first solution aims to reduce the energy consumption, whereas the second one aims to reduce the data transfer delay at the eNodeBs. The performance of the proposed schemes is evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals.

High smartphone penetration and Average Revenue Per User (ARPU) growth imply a quick and good broadband services delivery. In this context, Mobile Networks Operators (MNOs) have to satisfy current subscribers and gain new ones. Small Cells (SCs) are a promising alternative to MNOs to reach emerging markets and meet the rising mobile broadband demand. However, most of those competitive markets have to be served with wireless transport infrastructures for economic reasons; yet wireless links have limited data rates. SCs backhaul should be carefully planned. For this purpose, SCs backhaul dimensioning must take into account required end users traffic flows. As those throughputs vary according to different users traffic profiles, what are the necessary backhaul links capacities to satisfy them and don't exceed MNO budget. In this paper, we analyze UEs activity effect on issued traffic flows on a SC logical interfaces (S1 and X2) by using a Markov chain. We make difference between user and control plane throughputs. Numerical results showed how UEs activity increases generated traffic on S1 interface, but its impact is barely noticed on X2 interface traffic. It is due to the fact that data exchanges are primarily done on S1 interface, whereas signalization flows are minority parts in both S1 and X2 interfaces.

This paper introduces the concept of elastic bearer in Evolved Packet System (EPS), which allows, on one hand, the users to enhance on-demand the performance of certain applications and on the other hand, it permits the network to efficiently manage the resource allocation considering the application type. In particular, the paper introduces a set of mechanisms to trigger and support bearer elasticity in EPS based on Quality of Experience (QoE) perceived by users or based on feedback from Radio Access Network (RAN). Bearer elasticity can be attained through potential Packet Data Network/Serving Gateway (PDN/S-GW) relocation to eventually improve QoE within and beyond the mobile network operator premises. The paper also introduces a set of methods to identify and cope with a “storm” of requests for particular applications at densely populated areas.

Next generation cellular networks (5G) have to deal with massive deployment of machine-type-communication (MTC) devices, expected to cause congestion and system overload in both the radio access network (RAN) and the core network (CN). Moreover, not only would the network suffer from the system overload, but also the MTC devices would experience high latency to access the channel and high power consumption due to the retransmission attempts. Indeed, power consumption is a critical issue in MTC, as the devices are not plugged into the electrical supply, e.g., in the case of sensor devices. To alleviate system overload (caused by the massive MTC deployment), the 3GPP proposed the group paging (GP) method. However, its performances dramatically decrease when increasing the number of MTC devices being paged. In this paper, we devise a novel method, named further improvement-traffic scattering for group paging (FI-TSFGP), which aims to improve the performance of GP when the number of MTC devices is high. FI-TSFGP scatters the paging operation of the MTC devices over a GP interval instead of letting all of the devices start the channel access procedure at nearly the same time. By doing so, FI-TSFGP achieves high-channel access probability for MTC devices, leading to the reduction of both the channel access latency and power consumption. Compared to GP and two other schemes, simulation results clearly demonstrate the high performance of FI-TSFGP in terms of: success and collision probabilities, average access delay, average number of preamble transmissions, and ultimately energy conservation.

To cope with the tremendous growth in mobile data traffic on one hand, and the modest average revenue per user on the other hand, mobile operators have been exploring network virtualization and cloud computing technologies to build cost-efficient and elastic mobile networks and to have them offered as a cloud service. In such cloud-based mobile networks, ensuring service resilience is an important challenge to tackle. Indeed, high availability and service reliability are important requirements of carrier grade, but not necessarily intrinsic features of cloud computing. Building a system that requires the five nines reliability on a platform that may not always grant it is, therefore, a hurdle. Effectively, in carrier cloud, service resilience can be heavily impacted by a failure of any network function (NF) running on a virtual machine (VM). In this paper, we introduce a framework, along with efficient and proactive restoration mechanisms, to ensure service resilience in carrier cloud. As restoration of a NF failure impacts a potential number of users, adequate network overload control mechanisms are also proposed. A mathematical model is developed to evaluate the performance of the proposed mechanisms. The obtained results are encouraging and demonstrate that the proposed mechanisms efficiently achieve their design goals.

One important objective of 5G mobile networks is to accommodate a diverse and ever-increasing number of user equipment (UEs). Coping with the massive signaling overhead expected from UEs is an important hurdle to tackle so as to achieve this objective. In this paper, we devise an efficient tracking area list management (ETAM) framework that aims to find optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them to UEs, with the objective of minimizing two conflicting metrics, namely paging overhead and tracking area update (TAU) overhead. ETAM incorporates two parts (online and offline) to achieve its design goal. In the online part, two strategies are proposed to assign in real time, TALs to different UEs, while in the offline part, three solutions are proposed to optimally organize TAs into TALs. The performance of ETAM is evaluated via analysis and simulations, and the obtained results demonstrate its feasibility and ability in achieving its design goals, improving the network performance by minimizing the cost associated with paging and TAU.

An important requirement on 5G mobile systems is to accommodate massive numbers of wireless devices and users. Heterogeneous networks are expected to play a crucial role in meeting this requirement. In this vein, small cells are expected to become an integral part of these heterogeneous networks. However, their success would not last longer unless they offer services at a quality similar to that currently ensured by the macro cellular networks. Mitigating congestion of the backhaul links to small cell networks is a crucial factor. With this regard, this paper proposes an admission control that makes decisions to redirect IP flows, fully or partially, to the macro or small cell networks, or to reject the incoming flows. The decision mechanism is based on predictions of users' Quality of Experience (QoE). It is modeled as a Markov decision process (MDP), whereby the aim is to derive the optimal policy (i.e. reject or accept flows in the macro or the small cell) that maximizes users' QoE. Through computer simulations, we evaluate the performance of the proposed admission control and compare it against a random policy decision. We also numerically illustrate its optimal policies in different scenarios under different traffic load conditions.

User generated content (UGC)-based applications are gaining lots of popularity among the community of mobile internet users. They are populating video platforms and are shared through different online social services, giving rise to the so-called mobile social media applications. These applications are characterized by communication sessions that frequently and dynamically update content, shared with a potential number of mobile users, sharing the same location or being dispersed over a wide geographical area. Since most of UGC content of mobile social media applications are exchanged through mobile devices, it is expected that along with online social applications, these content will cause severe congestion to mobile networks, impacting both their core and radio access networks. In this paper, we address the challenges introduced by these applications devising a complete framework that 1) identifies such applications/sessions and 2) initiates multicast-based delivery (or offload through WiFi) of the relevant content. The proposed framework leverages the network function virtualization (NFV) paradigm to dynamically integrate its functionalities to the operators' service function chaining (SFC) process, allowing fast deployment and lowering both capital and operational expenditures (CAPEX and OPEX) of the mobile operators. The performance of the proposed framework is evaluated through mathematical analysis and computer simulations, taking Twitter-like social applications as an example.

One of the key enablers of the upcoming IEEE 802.11ax standard is the inclusion of uplink multi-users (UL MU) transmission model, which is performed using multiple-input multiple-output (MIMO) and beamforming techniques. So far, UL MU-MIMO has not been standardized in any of IEEE 802.11 amendments, because of technical issues facing its definition. One of these significant issues is the scheduling of UL MU-MIMO transmissions, which refers to selection rules and resource allocation procedures performed before simultaneous data transmissions. Indeed, simultaneous transmitters and receiver should exchange some information to correctly transmit the parallel data frames. However, this information exchange adds overheads, leading to reduce network performance. In this regard, we have proposed, in a previous work, a novel 802.11ax medium access control protocol aiming at reducing elapsed time in managing the establishment of an UL -MU communication called Wait-to-Pick-As-Available (W2PAA). Our contribution in this paper is twofold. First, we introduce an analytical model based on semi-Markov Chains to evaluate the performance of W2PAA; taking into a detailed behavior of the backoff counter (i.e. freezing period). Second, we propose an enhanced version of W2PAA aiming at improving both system and user-oriented performances. Aiming at validating the analytical model and comparing the performance of both versions of W2PAA, we used computer simulation. Obtained results, validate the analytical model in one hand, and clearly indicated the gain of both W2PAA versions by report to the basic UL-Single User on the other hand. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose a Proxy Mobile IPv6 (PMIPv6)-based architecture for Follow Me Cloud (FMC), a concept whereby mobile users are always connected to their optimal Data Centers via their optimal data anchor gateways, enabling not only data but also services to follow users. The approach envisioned in this paper consists of two parts: (i) a PMIPv6-based inter-domain mobility management support based on Distributed Mobility Management and (ii) a control plane based on Software Defined Networking - SDN/OpenFlow, which exploits the mobility information delivered by the PMIPv6 inter-domain mobility management approach to decide on triggering the migration of services/sessions within the cloud. Integrating SDN/Openflow rules with PMIPv6 permits removing the complexity and workload associated with tunneling in mobility management. Results obtained via analysis are encouraging and show the advantages of PMIPv6-based FMC in comparison to the state of the art mobility management protocols, such as Inter-domain PMIP (I-PMIP) and DMM-based PMIP (D-PMIP).

Machine-Type-Communication (MTC) is a key enabler for a variety of novel smart systems, such as smart grid, eHealth, Intelligent Transport System (ITS), and smart city, opening the area of the cyber physical systems. These systems may require the use of a huge number of MTC devices, which will put a great pressure on the whole network, i.e. Radio Access Network (RAN) and Core Network (CN) parts, resulting in the shape of congestion and system overload. Aiming at better evaluating the network performance under the existence of MTC traffic and also the effectiveness of the congestion control methods, the 3rd Generation Partnership Project (3GPP) group has proposed two traffic models: Uniform Distribution (over 60 s) and Beta Distribution (over 10 s). In this paper, a recursive operation-based analytical model, namely General Recursive Estimation (GRE), for modeling the performance of RACH procedure in the existence of MTC with Beta traffic is proposed. In order to show the effectiveness of our analytical model GRE, many metrics have been considered, such as the total number of MTC devices in each Random Access (RA) slot, the number of success MTC devices in each RA slot, and the Cumulative Distribution Function (CDF) of preamble transmission. Numerical results demonstrate the accuracy of GRE. Moreover, our model GRE could be used to model the performance of RACH procedure with any type of traffic.

One important objective of 5G mobile networks is to accommodate a diverse and ever-creasing number of user equipment (UEs). Coping with the massive signaling overhead expected from UEs is an important hurdle to tackle to achieve this objective. In this paper, we propose three solutions that aim for finding optimal distributions of tracking areas (TAs) in the form of TA lists (TALs) and assigning them to UEs, with the objectives of minimizing two conflicting metrics, namely paging overhead and tracking area update (TAU) overhead. Two solutions favors one objective than the other. The third one incorporates a novel scheme, dubbed Fair and Optimal TAL Assignment (FOTA), based on Nash bargaining game theory. FOTA improves overall network performance minimizing overhead due to both paging and TAU messages, taking into account the behavior and mobility features of UEs. The performance of proposed schemes are evaluated via simulations and the obtained results demonstrate their feasibility and ability in achieving their design goals, improving network performance by minimizing cost associated with paging and TAU.

Machine-Type-Communication (MTC) is a promising service of the envisioned 5G mobile networks. However, deploying a massive number of MTC devices in these networks remains a challenge due to the overload that may appear at the Radio Access Network (RAN), hence degrading the Quality of Services (QoS) for both MTC and Non-MTC devices. One of the methods used to address the congestion's problem in RAN is Group Paging (GP), wherein a single message is used to activate a group of devices. Whilst the GP method has several advantages, its performance quickly decreases when the number of MTC devices increases. In this paper, we devise a new method, namely Traffic Scattering For Group Paging (TSFGP) to improve the performance of the GP method for massive deployment of MTC devices. Numerical results demonstrate that TSFGP highly improves the performance of GP in terms of several performance metrics, such as success probability, collision probability, and access delay.

Cloud offerings represent a promising solution for mobile network operators to cope with the surging mobile traffic. The concept of carrier cloud has therefore emerged as an important topic of inquiry. For a successful carrier cloud, algorithms for optimal placement of Virtual Network Functions (VNFs) on federated cloud are of crucial importance. In this paper, we introduce different VNF placement algorithms for carrier cloud with two main design goals: i) minimizing path between users and their respective data anchor gateways and ii) optimizing their sessions' mobility. The two design goals effectively represent two conflicting objectives, that we deal with considering the mobility features and service usage behavioral patterns of mobile users, in addition to the mobile operators' cost in terms of the total number of instantiated VNFs to build a Virtual Network Infrastructure (VNI). Different solutions are evaluated based on different metrics and encouraging results are obtained.

IEEE 802.11ac is one of the ongoing Wireless Local Area Network (WLAN) standard aiming to support Very High Throughput (VHT) with data rate up to 7 Gbps below the 6 GHz band. This new generation of 802.11 WLANs is expected to support Down-Link Multi-User Multiple-Input Multiple-Output (DL-MU-MIMO) transmission, which is a promising technique to greatly increase the spectral efficiency by simultaneously transmitting to multiple users. In this paper, we propose to enhance the TXOP Sharing mechanism, introduced in the 802.11ac amendment, to achieve efficient DL-MU-MIMO transmission. At first, we give new definitions about both events of successful and failed DL-MU-MIMO transmission. Then, we devise a revised Backoff procedure for the primary Access Category (AC) in order to improve the DL-MU-MIMO. Simulation results demonstrate the benefits of the enhanced TXOP Sharing Mechanism in terms of channel utilization and achieved throughput.

Follow Me Cloud (FMC) concept has emerged as a promising technology that allows seamless migration of services according to the corresponding users' mobility. Meanwhile, Software Defined Networking (SDN) is a new paradigm that permits to decouple the control and data planes of traditional network, and provides programmability and flexibility, allowing the network to dynamically adapt to changing traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane, however, is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for mobile cloud computing systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to centralized approach with single SDN controller, our approach permits to distribute the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a global controller G-FMCC, and second level with several local controllers L-FMCC(s). Thanks to our control plane framework and Network Function Virtual-ization concept (NFV), the L-FMCC(s) are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performances maintaining and network resources preservation.

Uplink multi-users (UL-MU) transmissions are the key solution for the next IEEE 802.11ax to fully benefit from spatial resources. In this context, it is important to bring optimized solutions to address the technical challenges associated with UL-MU, particularly, issues associated to the management of multiple packets reception in the MAC layer. In this paper, we propose a novel 802.11ax MAC protocol aiming at reducing elapsed time in managing the establishment of an UL-MU communication, which would enhance system performance. Further, an analytical model, based on semi-Markov chains is proposed to evaluate the performance of the proposed protocol. Obtained results show notable system performance compared to Single User (SU) transmission.

Machine-type-Communication (MTC) is seen as a major service in next generation cellular mobile networks. However, the forecasted very large number of MTC devices may overload the RAN (Radio Access Network) part of the network, which may impact Non-MTC communications. Group paging is considered as one of the most efficient mechanisms proposed to alleviate the problem of the RAN overload. In this paper, we introduce a new solution to improve the performance of the current group paging method and overcome its disadvantages. The proposed solution is intended for MTC devices in the RRC CONNECTED OUT OF SYNC state, in which MTC devices have an RRC context without being synchronized with the network. Numerical results demonstrate that the proposed solution highly improves the performance of existing group paging mechanisms.

The Follow-Me Cloud (FMC) concept enables service mobility across federated data centers (DCs). Following the mobility of a mobile user, the service located in a given DC is migrated each time an optimal DC is detected. The detailed criterion for optimality is defined by operator policy, but it may be typically derived from geographical proximity or load. Service migration may be an expensive operation given the incurred cost in terms of signaling messages and data transferred between DCs. Decision on service migration defines therefore a tradeoff between cost and user perceived quality. In this paper, we address this tradeoff by modeling the service migration procedure using a Markov Decision Process (MDP). The aim is to formulate a decision policy that determines whether to migrate a service or not when the concerned User Equipment (UE) is at a certain distance from the source DC. We numerically formulate the decision policies and compare the proposed approach against the baseline counterpart.

This paper describes a device triggering optimization technique for controlling system overload when deploying massive Machine Type Communication (MTC) devices in 3GPP-based cellular networks. Triggering a large number of MTC devices can dramatically overload the underlying transport network system and incur congestion in both the Radio Access Network (RAN) and the Evolved Packet Core (EPC). The proposed solution aims at controlling the rate of device trigger requests that MTC servers can generate in order to reduce the system overload. For this purpose, we propose that the Mobility Management Entity (MME), or an alike core network node, computes the device trigger rate that alleviates congestion, and communicates this value to the MTC-Interworking Function (MTC-IWF) element that enforces MTC traffic control, via admission control or data aggregation, on the device trigger request rate received from the different MTC servers. The proposed solution is evaluated through computer simulations and encouraging results are obtained.

This paper proposes LOCARN: an alternative network architecture providing a packet connectivity layer, which is able to self-adapt its routing paths to both the effective traffics fluctuations and network resources changes. Moving close to a global maximization of available resources usage and assuming high resiliency under failures, this radical architecture focuses on architectural components coupling simplicity and plug-and-play guidance. Through analysis and computer simulation, several performance metrics focusing on scalability are evaluated.

Deploying a massive number of MTC (Machine - Type - Communication) devices in the current cellular mobile networks represents a great challenge as they may cause congestion and system overload for both RAN (Radio Access Network) and CN (Core Network) parts. To address this issue, we propose a novel algorithm, named Multi-Channel Slotted ALOHA-Optimal Estimation (MCSA-OE), which estimates the network status (the number of active devices), and thus better controlling the RAN access. Unlike most existing methods that consider only one channel, MCSA-OE uses the statistics of all the channels in order to estimate the number of arrivals (UE and MTC devices) in each RA (Random Access) slot. Simulation results demonstrate that MCSA-OE well tracks the number of arrivals as long as they are smaller than ln(RR), where R is the number of channels. Moreover, we propose to use MCSA-OE estimation to dynamically adjust the acbBarringFactor of the Access Class Barring (ACB) mechanism. Again, simulation results show that the behavior of our proposition merely tends to that of the best acknowledgment case, i.e. when the number of arrivals in each RA slot is well known.

Carrier Cloud is a promising concept towards the decentralization of mobile networks, to, in turn, alleviate mobile traffic load and reduce mobile operator cost. Carrier cloud is enabled by two main approaches, namely virtualization of the mobile network functions and networking over federated cloud. For intelligent carrier cloud dimensioning, the placement of mobile network functions over federated cloud is of vital importance. In this vein, this paper argues the need for adopting service/application type and requirements as metrics for (i) creating virtual instances of the Packet Data Network Gateways (PDN-GW) and (ii) selecting adequate virtual PDN-GWs for User Equipment receiving specific application type. After modeling this procedure as a nonlinear Optimization Problem and proving it as a NP-hard problem, we propose three solutions to solve it. The proposed solutions are evaluated through computer simulations and encouraging results are obtained.

Whilst Machine Type Communication (MTC) represents an important business opportunity for mobile operators, mobile operators fear the congestion that could come with the deployment of billions/trillions of MTC devices. In this paper, we present a new mechanism that anticipates system overload due to MTC signaling messages in 3GPP networks. This mechanism proactively avoids system congestion by compacting the signaling message content for a group of MTC devices sharing redundant Information Elements (IE) by creating a profile ID for this group. Furthermore, along with this solution, we propose a dynamic grouping solution, which groups MTC devices with common subscription features in order to control the MTC signaling traffic when the network is overloaded. Simulation results show that compared to the Access Class Baring (ACB) mechanism, introduced by 3GPP, our proposed solution can avoid system overload without dropping MTC signaling messages, which is highly beneficial for MTC applications requiring service reliability.

This paper introduces an analytical model for the Follow-Me Cloud (FMC) concept whereby service mobility is enabled across data centers following the mobility of a mobile user. Given a network and cloud setup and a mobility pattern of a mobile user, the proposed analytical model provides the performance of the FMC concept related to: (i) the user experience with the service (such as: UE average distance from the optimal DC, average end-to-end delay, service disruption duration); and (ii) to the cloud/mobile operator (such as the service migration cost). Obtained results are encouraging. They confirm the advantage of the FMC concept, but stress the need for careful consideration when triggering the service migration.

Emerging social media applications are expected to cause severe congestion to mobile networks, both mobile core network and mobile radio access network. These social media applications are characterized by the fact that they involve sessions with frequently and dynamically updated content, shared with a potential number of mobile users sharing the same location, or being dispersed over a wide area. A method to dynamically identify such applications/sessions and initiate multicast based delivery of the relevant content is proposed. The performance of the proposed method is evaluated through computer simulations, taking Twitter as an example. Encouraging results are obtained.

Existing gateway selection mechanisms base their selection on gateway load and/or geographical proximity of users to the gateways. In this paper, we mainly argue the need for other metrics to improve the gateway selection mechanisms in distributed mobile networks. We therefore propose considering the end-to-end connection and the service/application type as two important additional metrics in the selection of data anchor gateways in the context of the Evolved Packet System (EPS). To enable this, two solution variants are proposed. Simulations were also conducted to evaluate the performance of the proposed solutions and encouraging results are obtained.

Building mobile networks, on demand and in an elastic manner, represents a vital solution for mobile operators to cope with the modest Average Revenues per User (ARPU), on one hand, and the ever-increasing mobile data traffic, on the other hand. An important research problem towards this vision of carrier cloud pertains to the development of adequate technologies and methods for the on-demand and dynamic provision of a decentralized and elastic mobile network as a cloud service over a distributed network of cloud-computing data centers, forming a federated cloud. An efficient mobile cloud cannot be built without efficient algorithms for the placement of network functions over this federated cloud. In this vein, this paper argues the need for avoiding or minimizing the frequency of mobility gateway relocations and discusses how this gateway relocation avoidance can be reflected in an efficient network function placement algorithm for the realization of mobile cloud. The proposed scheme is evaluated through computer simulations and encouraging results are obtained.

Scalable Video Coding (SVC) in media streaming enables dynamic adaptation based on device capabilities and network conditions. In this paper, we investigate deployment options of SVC for Dynamic Adaptive Streaming over HTTP (DASH) with a special focus on scalability options, which are relevant for dynamic adaptation, especially in wireless and mobile environments. We evaluate the performance of SVC with respect to spatial and quality scalability options and compare it to non-scalable Advanced Video Coding (AVC). Performance evaluations are performed for various encoder implementations with high-definition (1080p) content. We show that a hybrid approach with multiple independent SVC bitstreams can have advantages in storage requirements at comparable rate-distortion performance.

This paper designs a complete framework that anticipates QoS/QoE (Quality of Experience) degradation and proactively defines policies for LTE-connected cars (UEs) to select the most adequate radio access out of WiFi and LTE. For a particular application, the proposed framework considers the application type, the mobility feature (e.g., speed, user mobility entire/partial path, user final/intermediate destination), and the traffic dynamics over the backhauls of both LTE and WiFi networks in order to predict and allow the UE to select the best network that maximize user QoE throughout the mobility path. Simulations are conducted to evaluate the performance of the proposed framework in achieving its design objectives and encouraging results are obtained.

One of the most important problems posed by cellular-based machine type communications is congestion. Congestion concerns both the radio access network and the mobile core network, impacting both the user data and the control planes. In this paper, we address the problem of congestion in machine type communications. We propose a congestion-aware admission control solution that selectively rejects signaling messages from MTC devices at the radio access network following a probability that is set based on a proportional integrative derivative controller reflecting the congestion level of a relevant core network node. We evaluate the performance of our proposed solution using computer simulations. The obtained results are encouraging. In fact, we succeed in reducing the amount of signaling, to reach a target utilization ratio of resources in the core network.

Perceived speech quality, or Quality of Experience (QoE), is the key criteria for evaluating VoIP service. Most of the existing solutions are intrusive, in a sense where they require both the original and the transmitted audio sequences. These solutions give good estimation of the QoE, but they cannot be used in real-time. In fact, Service Provider and Network Provider are highly interested on automatic QoE estimation tool (without intrusion) in order to monitor and control the perceived quality of their VoIP service. In this paper, we present a new perceived speech quality estimation tool, named ALICANTE1Pseudo Subjective Quality Assessment (A_PSQA) for two widely VoIP codecs, iLBC and Speex. A_PSQA is a non-intrusive method, which relies on Random Neural Network (RNN) approach to learn the nonlinear relation between network parameters and the perceived user QoE. Furthermore, to avoid costly and time-consuming subjective tests, we used a well-known intrusive method ITU-T's Perceptual Evaluation Quality (PESQ) to estimate the MOS. Obtained results show that A_PSQA is able to estimate the MOS like PESQ, while being non-intrusive. Besides, A_PSQA's results are compared with two non-intrusive (IQX and E-Model) methods, where A_PSQA shows the highest correlation with PESQ estimation than all others methods.

Due to their numerous advantages, current trends show a growing number of femtocell deployments. However, femtocells would become less attractive to the general consumers if they cannot keep up with the service quality that the macro cellular network should provide. Given the fact that the quality of mobile services provided at femtocells depends largely on the level of congestion on the backhaul link, this paper introduces a flow mobility/handover admission control method that makes decisions on layer-three handovers from macro network to femtocell network and/or on entire or partial flow mobility between the two networks based on predicted QoS taking into account metrics such as network load/congestion indications and based on predicted QoE metrics. The performance of the proposed admission control is evaluated via simulations and encouraging results are obtained.

The area-based localization algorithms use only the location information of some reference nodes, called anchors, to give the residence area of the remaining nodes. The current algorithms use triangle, ring or circle as a geometric shape to determine the sensors' residence area. Existing works suffer from two major problems: (1) in some cases, they might issue wrong decisions about nodes' presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error. In this paper, we deal with the localization problem by introducing a new way to determine the sensors' residence area which shows a better accuracy than the existing algorithms. Our new localization algorithm, called HSL (Half Symmetric Lens based localization algorithm for WSN), is based on the geometric shape of half-symmetric lens. We also uses the Voronoi diagram in HSL to mitigate the problem of unlocalizable sensor nodes. Finally, we conduct extensive simulations to evaluate the performance of HSL. Simulation results show that HSL has better locatable ratio and location accuracy compared to representative state-of-the-art area-based algorithms.

Minimizing energy consumption is becoming more and more crucial in today's mobile terminals communications. Reducing its use pass necessarily by exploiting low power design techniques and by adopting novel energy-aware applications and protocols. The focus in this paper mainly concerns the Voice over IP over Wireless Local Area Network (VoWLAN) application, which is beyond doubt the most popular application in mobile terminals. The real-time nature of this application makes the conventional approaches inefficient, as they conserve energy by either: (i) switching the wireless device from the active mode to the sleep mode when no data is buffered for transmission or reception, which completely block the mobile terminal in the active mode (i.e. waste of energy), or (ii) forcing the switching to the sleep mode, which is clearly a detrimental behavior for such delay-constrained application. The idea of the proposed paper is to wisely determine the optimal schedule between the Sleep and Wakeup periods based on the application requirements. Hence, we propose in the following a quality of experience (QoE)-aware protocol, maximizing the sleep mode duration, for VoIP application. The performance of the proposed protocol is performed through computer simulation. The obtained results clearly demonstrate the superiority of the protocol in conserving energy while keeping the QoE at a desired level.

In the objective of streaming the video to the end-user with the best possible quality, in this paper we propose an approach that couples the SNR scalable video coding (SVC) extension of H264/AVC, with the path diversity provided by video distribution network (VDN). Our method adapts to the heterogeneity of end-users using the scalable video coding. Moreover, it adapts to network bandwidth fluctuation by observing the changes of the available bandwidth over the multiple overlay paths, and updating the streaming strategy accordingly.

DVB-T2 is offering a new way for broadcasting value-added services, like HDTV and 3D TV, to either fix or mobile end users. Thanks to the advances made in digital signal processing, and specifically in channel coding, DVB-T2 brings a new flexibility in services' broadcasting with an increased transfer capacity of 50%, in contrast with the first generation of the DVB-T standard. On the other hand, SVC video coding is an emerging technique that uses scalability to encode video content in a hierarchical video streams (i.e. layers). Indeed, the SVC supports three types of video scalability: spatial, temporal and quality; which allow to handle users' heterogeneity in term of capacity and bandwidth. In this paper, we first propose to support SVC over DVB-T2 networks, by associating the layering architecture of both technology, in order to tackle users' mobility. This association allows mobile receivers with good physical channel to decode all the SVC layers and benefit from high video quality. Meanwhile, users with worst channel condition can at least decode the base layer and benefit from acceptable video quality. Secondly, we introduce a novel QoE-based adaptive mechanism for SVC layers decoding. The proposed approach selects dynamically the number of layers to decode, at the receiver side, so as to maximize the users' perceived quality. Simulation results show clearly the enhancement achieved by the proposed solution in term of user Quality of Experience (QoE).

The scalable extension of H.264, known as Scalable Video Coding (SVC), is recently finalized and adapted by the Joint Video Team. Scalability is achieved in the temporal, spatial, quality (SNR), or any combination of those domains. One example of using video scalability is in saving bandwidth when the same media content is required to be sent simultaneously at different resolutions to support heterogeneous devices and networks. Meanwhile, Quality of Experience (QoE) is the key criteria for evaluating the video service such as SVC. Unlike QoS metrics (such as bandwidth, delay, jitters, etc.), QoE is more accurate to reflect the user experience as it considers Human Visual System and its complex behavior towards distortions in the displayed video sequence. In order to evaluate QoE, objective assessment tools may not correlate well with the human perceived video quality and at same time, subjective quality assessment methods are costly and time consuming. In this paper, we design an automatic QoE monitoring tool for SVC video coding mechanism. The module is based on PSQA (Pseudo Subjective Quality Assessment tool), which is a hybrid (objective/subjective) assessment tool. PSQA uses RNN (Random Neural Network) to capture the non-linear relation between the video coding as well as network parameters affecting the video quality, and QoE. The results show clearly that our module can accurately estimate QoE for SVC video streams.

Quality of Experience (QoE) is the key criteria for evaluating the Media Services. Unlike objective Quality of Service (QoS) metrics, QoE is more accurate to reflect the user experience as it considers human visual system and its complex behavior towards distortions in the displayed video sequence. In this paper, we present a new QoE tool solution, named ALICANTE Pseudo Subjective Quality Assessment (A_PSQA). It relies on a No-Reference QoE measuring approach, hybrid between subjective and objective methods fully functional in Future Media Internet context. To validate this approach, we deployed a video streaming platform, and considered different video sequences having different characteristics (low/high motion, quality, etc.). We then compared the results of A_PSQA with two Full-References methods (SSIM and PSNR) and two No-References approaches. Obtained results demonstrate that A_PSQA shows a higher correlation with subjective quality ratings (MOS) than all others methods.

In the domain of video streaming, Scalable Video Coding (SVC) comes as a solution that adapts to network bandwidth fluctuation and to terminals heterogeneity. The multilayer-coding criterion of SVC has increased its correlation with the streaming over multiple paths. However, an important challenge that we still need to face is how to maximize the overall video quality in a way that satisfies the end-user, without greedily consuming the network resources. To this purpose, in this paper we aim at providing a powerful system that is based on the Quality of Experience (QoE) evaluations, to adjust the streaming of SVC video flows over multiple paths. Our method dynamically selects the best overlay paths using the available bandwidth estimations. Keeping/updating the selected paths is then automatically done based on the feedback of the quality as it is perceived by the end-user. For evaluating the QoE at the recipient, we use an SVC-compatible module of the hybrid Pseudo-Subjective Quality assessment (PSQA) tool. Results show how our method can guarantee the deliverance of the best possible video quality, while keeping the balance between end-user requirements and network bandwidth wise consumption.

Deployment of next-generation network (4G) begins to spread throughout the world. With variety of network technologies and QoS restrictions on emerging applications; it becomes difficult for a user to select the best access network to request for connection. Even though many schemes have been proposed in the literature but very few of them take into account quality of experience (QoE) perceived by user for making decision. As QoE represents perception experienced by the user, it is thus an essential indicator for network evaluation, especially with multimedia communications nowadays. Therefore, in this paper we propose a novel network selection mechanism that takes quality of experience into consideration for decision making. It is a user-baaed and network-assisted approach thus a compromise solution between user and network benefit. The main idea is to use quality of experience of ongoing users in candidate networks as an indicator to select the best network for connection. We have implemented and tested our mechanism in network simulator NS-2. The obtained results illustrate that with a QoE-aware mechanism we can significantly improve user experience of mobile node and load balancing between networks.

With widespread use of multimedia communication, quality of experience (QoE) progressively becomes an important factor in networking today. Besides, multimedia applications can be supported under various technologies, including wired and wireless networks. Among them, Universal Mobile Telecommunications System (UMTS) is one of the most popular technology thanks to its support on mobility. Improved with a new access method (High Speed Downlink Packet Access or HSDPA), it can provide higher bandwidth and enable wider range of services including multimedia applications. In UMTS, different categories of traffic are specified along with their characteristics. Best effort traffic has been specified with less priority because it has fewer constraints. On the other hand, real-time multimedia traffic such as streaming video or VoIP are more sensitive to network condition changes, hence special treatment (e.g. QoS scheduler) is needed in order to achieve user satisfaction. According to the literature, most of scheduling mechanisms mainly take into account signal quality and fairness and do not consider user perception. In this paper, we propose a novel approach, QoE-aware scheduler that takes quality of experience into account when making scheduling decisions. Compared to other existing schedulers, QoE-aware approach has reached a profitable performance in terms of user satisfaction, throughput, and fairness.

Nowadays, wireless networks have been deploying everywhere, with IEEE 802.11 as the most popular standard. However, wireless resources are scarce and wireless condition varies often. These limitations are crucial for applications with tight QoS requirements such as video or voice over IP. To cope wi

The deployment of real-time and multimedia applications over wireless LAN has gained a growing interest in this last decade. These applications, e.g. video streaming, require tight guarantee on quality of service (QoS). Also, they use multicast communication in order to reduce the bandwidth consumption. However, in WLAN, multicast packets are sent with the basic rate (lowest rate); which results in capacity wasting because of longer channel occupancy. Moreover the lack of feedback mechanism makes it difficult to deal with reliability or service quality. In this paper, we propose to rely on the WLAN multi-rate capability, in order to transmit multicast packets with higher and dynamic rate than the basic rate. Unlike other existing protocols that use a static-threshold to decide when to change transmission rate, we propose a novel dynamic rate-adaptation mechanism based on quality of experience (QoE), namely Q-DRAM. According to the clients' feedback on QoE, we adapt the multicast rate: (i) when users had bad QoE we reduce the multicast transmission rate; (ii) when users had good QoE, we increase the multicast transmission rate. Simulation results show that Q-DRAM increases the wireless channel utilization and maximizes users' QoE, compared to existing solutions as well as to the IEEE 802.11 standard.

For the last decade, Voice Over IP (VoIP) has been a great concern leading to consider it as the ldquokilling applicationrdquo. Deploying such applications in the Wireless Local Area Networks (WLAN) is still in progress, and further investigations are needed. Unlike wired networks, wireless networks such as WLAN, have their own characteristics, which introduce many challenges that prohibit the support of real-time applications like VoIP. To support VoWLAN, it is commonly accepted that cross-layer cooperation is required. It involves an exchange of information between the MAC and the application layer aiming at better adapting the voice coder to the network conditions. In this paper, we propose to adapt the voice coder's rate regarding the MAC layer feedbacks that reflect the network state in term of network load and physical rate. Simulation results show that compared to the case when using only G.711 coder, our crosslayer solution ensures high VoIP quality by maintaining low end-to-end delays and minimum PLR (Packet Loss Rate).

Widespread use of wireless networks nowadays raises many challenging issues to be explored. With increasing of multimedia traffic, quality of experience (QoE) needs to be satisfied at users while overall performance needs to be maintained at networks. In order to achieve these goals, the use of network selection mechanism is inevitable. When several access points are present, user should select the best available network while trying to keep load balanced between access networks. In this paper, we present a user-based and network-assisted scheme for network selection in wireless IEEE 802.11 technology. By providing users in decision making process with relevant information about the networks, the proposed solution keeps compromising advantage for both user and network. Our mechanism is based on a technique called pseudo-subjective quality assessment (PSQA), which is used basically to measure quality of experience perceived by users. We propose a new scheme where PSQA tool is used to assist in terminal-centric network selection. We explain the scheme and illustrate its efficient performance compared to a signal-based mechanism.

Widespread use of wireless networks nowadays raises many problems for service providers in managing their resources. These problems are caused mainly by restricted bandwidth and variable radio condition in this type of network. Moreover, with the emergence of multimedia traffic and its requirements in terms of quality, admission control is hence an inevitable choice to optimize network resources while maintaining high service quality at users. In this paper, we propose an admission control mechanism based on quality of experience (QoE) perceived by users. The human QoE is obtained by a tool called pseudo subjective quality assessment (PSQA), which is based on statistic learning using random neural network (RNN). Instead of relying on technical parameters such as bandwidth, loss, or latency, which do not correlate well with human perception, our scheme is based on mean opinion score (MOS) but without interaction from real humans. The simulation results demonstrate the better performance of our proposition compared to the loss-based approach regarding user satisfaction evaluated by achieved QoE at user and bandwidth utilization of the network evaluated by good put.

Most of QoS-capable IEEE 802.11 MAC protocols are unable to deliver sustained quality of service while maintaining high network utilization, particularly under congested network conditions. The problem often resides in the fact that flows belonging to the same service class are assigned the same MAC parameters regardless theirs respective bitrate, which leads to throughput fairness rather than perceived QoS fairness. Harmonizing MAC parameters of traffic classes's flows may further lead to sub-optimal situations since certain network configurations (in terms of per class traffic load) can not be accommodated without reassigning the basic MAC parameters. In this paper, we propose a new cross-layer MAC design featuring a delay-sensitive backoff range adaptation along with a distributed flow admission control. By monitoring both MAC queue dynamics and network conditions, each traffic class reacts based on the degree to which application QoS metrics (delay) are satisfied. Besides, we use a distributed admission control mechanism to accept new flows while protecting the active one. Simulation results show that compared to the enhanced distributed coordination function (EDCA) scheme of 802.11e, our protocol consistently excels, in terms of network utilization, bounded delays, and service-level fairness.

The ISM free-licence band is highly used by wireless technologies such IEEE 802.11, Bluetooth as well as private wireless schemes. This huge utilisation increases dramatically the interferences (high Bit Error Rate) leading to lowering the reliability of such networks. Among these wireless-based technology, IEEE 802.11 suffers particularly from these high interferences since the wireless sender is confused by loss’ origins (noise or collision). In fact, the contention resolution mechanism (known as Backoff Exponential Binary) used by 802.11 assumes that each loss in the network is caused only by collision and hence acts to overcome this situation by delaying the retransmission of the packet lost. However, this mechanism is not efficient when the wireless channel is deployed in noisy environments, where collisions are mixed with high BER-caused errors. In this paper, we adapt the RTS/CTS handshake mechanism to the noisy channel through two possible mechanisms. After that, we model the proposed mechanisms by a two-dimension Markov model and validate these models by NS2 -based simulations.

Actually, the quality of service (QoS) provisioning in IEEE 802.11-based wireless network is assured by the enhanced coordination channel access (EDCA) mechanism, which lacks the aptitude to ensure: (i) an intra-QoS differentiation between the access class (AC), where flows belonging to the same service class are assigned the same MAC parameters regardless theirs respective bit rate, which leads to throughput fairness rather than perceived QoS fairness; (ii) an admission control mechanism that manages the network traffic. In this paper we propose a new MAC protocol featuring a dynamic reservation of the wireless channel (by using the TXOPlimit parameter) along with a distributed admission control mechanism. By monitoring the MAC queue, each flow computes at runtimes the TXOPlimit's value that satisfies the application requested data rate. Meanwhile we specify a fully distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the EDCA scheme of 802.1 Ie, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state

Multimedia streaming over wired network, such as the Internet, has been popular now for quite some time. Further, as the bandwidth of wireless technologies have increased, consideration has only recently turned to delivering video over wireless networks. Meanwhile, an emerging new video compression standard namely H.264 is widely accepted. The H.264 standard outperforms all previous video compression, and can deliver high video quality at low rate. However, due to wireless channel characteristics, it is insufficient to merely deliver video data; instead delivery must consider also an adaptation of the video data to the networks specific and particularly network friendliness. In this paper, we address H.264 wireless video transmission over IEEE 802.11 wireless LAN by proposing a robust cross layer architecture which exploits: (i) the new standard IEEE 802.11e MAC protocol; (ii) the H.264 error resilience tools, namely data partitioning. The performances of the proposed architecture are extensively investigated by simulations. Results obtained indicate that our architecture can reliably transport the H.264 video stream, while the actual IEEE 802.11 standard in the same conditions, degrades considerably the transmitted video.

The increase of IEEE 802.11's bandwidth led to a deployment of many multimedia applications over wireless networks. Nevertheless, these applications impose stringent constraints in QoS. In this context, a lot of works have been proposed in order to enhance the QoS-capable IEEE 802.11e MAC protocol. However, they settle for maintaining only an inter-QoS differentiation between the traffic classes, and neglect the intra-Qos differentiation. In fact, the flows belonging to the same service class are assigned the same MAC parameters regardless of their data rate, which leads to throughput fairness rather than perceived QoS fairness. On the other hand, the proposed schemes exhibit performance degradation when the number of flows increases. In this paper, we propose a new MAC protocol based on the reservation of the wireless channel through the use of transmission Opportunity (TXOPlimit) parameter. Each traffic class monitors the MAC queue and computes at runtime the TXOPlimit's value. Thus based on the class' priority and flow's data rate, we can ensure both intra and inter QoS differentiation. Additionally, we specify a distributed admission control mechanism that regulates the network load and protects the admitted flows from the new ones. Simulation results show that compared to the Enhanced Distributed Channel Access (EDCA) scheme of 802.11e, our protocol excels, in terms of network utilization and ability to maintain intra-QoS data rate differentiation. Further when introducing the admission control mechanism, we ensure high protection to the admitted flows, and maintain the network in steady state.

With the widespread IEEE 802.11 networks use, strong needs to enhance quality of service (QoS) has appeared. The IEEE 802.11 medium access control (MAC) protocol provides a contention-based distributed channel access mechanism that allow for wireless medium sharing. This protocol involves a significant collision rate as the network gets fairly loaded. Although the contention window (CW) is doubled after each collision, active stations may randomly select a backoff timer value smaller than the preceding one. This is obviously sub-optimal since the backoff values should rather increase after each collision in order to further space between successive transmissions and thus absorbing the growing contending flows. In this paper, we propose a novel backoff mechanism, namely "determinist contention window algorithm" (DCWA), which further separates between the different backoff ranges associated to the different contention stages. Instead of just doubling the upper bound of the CW, DCWA increases both backoff range bounds (i.e., upper and lower bounds). On the other hand, after each successful transmission the backoff range is readjusted by taking into account current network load and past history. Simulation results show that DCWA outperforms both the distributed coordination function (DCF) and the slow decrease (SD) scheme in terms of responsiveness to network load fluctuations, network utilization, and fairness among active stations.

Many works have recently addressed the IEEE 802.11 QoS issues by proposing different monitoring-based contention window (CW) differentiation techniques. In network saturation, however, it is difficult to guarantee firm services differentiation while achieving high network exploitation. Particularly, most existing QoS-capable MAC protocols rely on a backoff interval sampled from a dynamic range [0 CW/sub i/]. In order to ensure more deterministic service differentiation, we propose a new MAC protocol featuring a sliding contention window (SCW) for each network flow. The different flows are now able to select backoff intervals from different (separated) CW ranges. The SCW dynamically adjusts to changing network conditions, but remains within a per-class predefined range, in order to maintain a separation between different service classes. Simulation results show that compared to the EDCA scheme of 802.11e, SCW consistently excels, in terms of network utilization, strict service separation, and service-level fairness.

Today the great challenge to the IEEE 802.11 wireless LAN is to support real time and multimedia applications. However, the characteristics of wireless channel (low and fluctuating bandwidth link and the large error rate) post different problems in supporting the requirements on QoS (bandwidth, delays, jitters) of these sensitive applications. We focus on the JVC H.26L video encoder, the wireless LAN and their interaction in QoS. The interaction is made by the guarantee of a reliable H.26L video transport over the IEEE 802.11 wireless LAN. In this context, we propose a novel cross layer architecture. This architecture is based on a marking scheme, which exploits the new propositions of the IEEE 802.11 E group for supporting QoS.