The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.

With its disaggregated and open interfaces, the Open Radio Access Network (O-RAN) architecture promises flexibility, innovation, and cost-effectiveness for future wireless networks. However, the increased reliance on software and open interfaces also introduces new security challenges. As the threat of quantum computing looms, the traditional cryptographic algorithms used in O-RAN will become vulnerable to potential attacks. This paper proposes the integration of Post-Quantum Cryptography (PQC) into O-RAN to enhance its security against post-quantum adversaries. We discuss the specific vulnerabilities of current O-RAN security mechanisms to quantum attacks and identify key areas where PQC can be applied, such as key exchange, authentication, and data protection. We present a framework for evaluating the suitability of different PQC algorithms for O-RAN and outline potential challenges and implementation considerations. By proactively integrating PQC into O-RAN, we aim to ensure the long-term security and resilience of this emerging network architecture in the era of quantum computing.

Kubernetes plays an important role nowadays in enabling cloud-native telecom and microservices management in 5G/6G networks. It provides a platform for deploying, scaling, and managing cloud-native micro-services but also 5G/6G core functionalities. In this work, we demonstrate the importance of misbehavior detection in Kubernetes clusters paired with suitable mitigation measures as a solid baseline to minimize disruption of 5G service delivery. We evidence the impact of two types of DoS attacks, blackholing and network DoS, and results of detection and mitigation in Kubernetes.

Novel networking paradigms based on programmability and softwarization are deeply transforming the architecture and end-to-end management of next-generation mobile networks. This demonstration provides a step beyond in this transformation process by showcasing the full cloud-native deployment of a disaggregated and distributed 5 G mobile network from core to Radio Access Network (RAN) using open-source software. The distinguishing features of the proposed approach are that the orchestration process includes the use of a cloud-based Distributed Unit (DU) communicating through O-RAN Split 7.2 with a Radio Unit (RU) performing over-the-air transmission, the end-to-end configuration of slices with on-demand activation of core network function components, and the dynamic configuration of transport network flows to differently route the traffic of active slices.

Novel networking paradigms based on cloudification and open interfaces are making mobile networks more agile to adapt and meet the requirements of emerging use cases. Based on an automated end-to-end cloud-native open-source deployment of a 5G mobile network with over-the-air capabilities, this demonstration features the inclusion of a cloud-native monitoring system to follow the radio access network (RAN) performance evolution. By just defining the number of gNBs, this monitoring system automatically configures independent data sources and dashboards representing the evolution of received information while such gNBs are progressively created on-demand in a distributed environment.

The inherent virtualization characteristics of 5G, 6G, and subsequent generations (xG) facilitate the modularity of components and their interfaces, leading to an increase in the number of stakeholders and necessitating more complex administrative relationships. In this demonstration, we present an open-source Decentralized Application (DApp) integrated with smart contracts deployed on a live Polygon testnet to support trustworthy Federated Learning (FL). FL enables clients to conduct local training and updates, while a central aggregation server processes these inputs for global FL training. We introduce a blockchain-based reputation system within such FL model to facilitate collaborative training of Machine Learning (ML) models. The deployed smart contracts on the Layer 2 Polygon blockchain calculate and store reputation scores on-chain for each FL client based on their contribution. This demonstration presents a visualization of the blockchain network parameters during the execution of blockchain-enabled smart contracts. The DApp offers an interactive interface for client registration, performance parameter submission, and reputation score calculation. We use Alchemy's dashboard for real-time monitoring of blockchain transactions and smart contract interactions. The open-source implementation is publicly available11https://github.com/farhanajaved/bc-fl-demo-l2 and the video of the demo is available at22https://youtu.be/B9J1gxjoKdM.

The architecture and management of nextgeneration mobile networks are integrating novel networking paradigms to support challenging scenarios. This paper presents the design, implementation, and evaluation of a fully cloud-native, end-to-end mobile network integrating O-RAN functionality. Built on top of open-source software, our proposed framework develops a set of cloud-native deployment artefacts-such as Helm Charts and Open Source MANO packages-also released as open-source. This framework supports highly disaggregated and distributed deployments of key network functions, including the$\mathbf{5 G}$core, gNodeB (with decoupled Centralised and Distributed Unit entities), near-RT RIC, and xApps, enabling agile, portable, and flexible instantiations. Experiments show that the full cloudnative mobile network can be instantiated in less than five minutes, achieving a throughput comparable to bare-metal setups. This work highlights significant improvements in deployment efficiency and adaptability, contributing towards automated and autonomous cloud-native Beyond 5G/6G networks.

This tutorial deals with the integration of data engineering with network management and orchestration in telecommunication networks. It provides participants with a comprehensive insight into the use of data engineering to improve the efficiency and performance of telecommunication systems, especially through the use of Artificial Intelligence (AI)/ Machine Learning (ML) technologies in network infrastructures. Practical applications are also demonstrated using relevant case studies to illustrate the implementation of these concepts.

Integrating edge computing with 5G/6G, bolstered by standards such as ETSI Multi-access Edge Computing (MEC), becomes indispensable, particularly in addressing end-to-end latency-sensitive vertical applications. Thus, it is imperative to delve into infrastructure and network considerations, including the relocation of server workloads and optimization of user data planes. Notably, 3GPP facilitates third-party vertical providers’ access to user equipment (UE) metrics and data plane capacities through the Network Exposure Function (NEF). This paper showcases a demonstration that focuses on the seamless coordination of cloud-native workload relocation and user plane function (UPF) selection within the 5G network. Here, the interaction between a vertical provider and the 5G core enables informed decisions aimed at ensuring minimal latency by triggering UPF selection and relocation of containerized servers to the cloud or the edge, illustrating the anticipated trend of increased automation within 6G networks.

In this paper, we propose an innovative integration of Quantum Key Distribution (QKD) and Blockchain-based Self-Sovereign Identity (SSI) within the Open RAN (O-RAN) framework for 6G networks to address the critical need for enhanced security and robust identity management. We first present a general architecture that takes a multi-layered approach and is carefully designed to leverage the different capabilities of quantum security and blockchain technology. The architecture ensures seamless and secure operation across different layers of the O-RAN, focusing on the Distributed Identity Management (DIM) and Management & Orchestration layers, and explains the interactions between these layers to improve the security and operational efficiency of the network. We also investigate detailed case studies and applications that demonstrate the practicality and transformative potential of integrating QKD-secured blockchain identity management systems in real-world 6G scenarios. We also address the inherent challenges and limitations of such integration and propose viable solutions to overcome them. Finally, we provide insights into future research and implementation directions and highlight the critical role of quantum-secured blockchain systems in the evolution of telecommunication networks toward a more secure, decentralized, and user-centric paradigm.

Novel networking paradigms, such as virtualization, are transforming mobile networks to support challenging use cases and scenarios. This demonstration presents an on-demand, distributed, and fully cloud-native deployment of a 5G mobile network (including core and RAN) with over-the-air capabilities using open-source software. This deployment strategy provides the flexibility and dynamicity required to manage and orchestrate forthcoming B5G/6G networks, thereby empowering mobile networks to adapt and scale effectively.

Novel networking paradigms are enabling the introduction of innovative vertical use cases and new business relations in the B5G/6G mobile ecosystem. For instance, a use case may require the coordination of domains owned by different operators to provide service continuity and keep offering a vertical network service (NS) in similar conditions after a cross-border situation. This demonstration presents a procedure to perform a proactive service relocation in such a multi-administrative domain scenario considering an automotive use case. This demonstration proposes a cloud-native solution combining multiple enablers to manage the life-cycle of virtualised automotive NSs. During run-time, and upon registration, an AI/ML-based enabler decides proactively on the service relocation moment based on the collected vehicle’s positions and triggers an Integration Fabric enabler following ETSI ZSM guidelines to start a new instance of such automotive NS at the associated ETSI NFV management and orchestration stack present in the neighbouring administrative domain.

Effective and real-time management of data planes becomes of paramount importance to support the ever-increasing challenging use cases and scenarios foreseen in 5G and beyond. In this demonstration, we present a comprehensive showcase of a cloud-native open-source 5G core deployment, realised as a disaggregated non-public network (NPN) spanning multiple locations. Ranging from cloud to edge points of presence, the demonstration establishes an end-to-end experimental platform with over-the-air transmission capabilities, specifically highlighting the on-demand creation and deletion of User-Plane Functions (UPFs). This dynamic deployment approach harnesses the advantages offered by edge locations, empowering the mobile network to adapt and scale as per its specific requirements. Furthermore, our demo elucidates how to use Open Source MANO (OSM) for easing the management operations of network administrators.

The H2020 Distributed management of Network Slices in beyond 5G(MonB5G) project aims to provide zero-touch management and orchestration to support network slicing at scale to reduce the management burden on mobile operators by leveraging distribution of operations along with advanced data-driven Artificial Intelligence (AI)-based mechanisms. However, while this approach shows promise and large companies with abundant data and ML expertise are developing powerful MLdriven services, a critical aspect that remains to be analyzed is its business case. The vast majority of potentially valuable ML services, such as predictive maintenance, Quality of Service (QoS) optimization, network security enhancements, remain stuck at the idea or prototype stage. This paper delves into an analysis of how the MonB5G solutions in particular the tuples (Monitoring System (MS), Analytics Engine (AE), Decision Engine (DE) and Actuator (ACT) could be applied within the network management and orchestration market while investigating various business models and value chains. Numerical results based on experimental data have also been performed to evaluate the OpEX (Operational Expenditure) benefits associated with different network management techniques, for centralized and distributed systems.

Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.

This paper demonstrates an implementation of Federated Learning (FL) for streaming applications using cloud-native technology. Compared to a centralized management, by adopting a decentralized approach, the FL method improves convergence time, reduces communication overhead, and increases network energy efficiency. The cloud-native FL architecture presented comprises three sites, each with its own Kubernetes (K8s) cluster. The edge sites run FL Analytical Engines (AEs)/clients for local training and updates, and the central site runs the aggregation server for FL training. Some other relevant workloads deployed at the clusters are the video streaming server, the orchestrator, and monitoring components. As for the RAN, we showcase a multi-gNB setup from which we obtain monitoring data via custom sampling functions. Following the description of the testbed infrastructure and setup, this demonstration presents the real-time visualization of network parameters during FL training, and the enhancement of video streaming through proactive Central Processing Unit (CPU) scaling, made possible by the resource forecasting.

There has been a rapid increase in the number of connected vehicles with a huge amount of data exchange between these vehicles that needs to be communicated, processed and analyzed reliably and efficiently. For secure and decentralized authentication, self-sovereign identity (SSI) management in vehicular networks have attracted attention in recent years. Hierarchical deployment frameworks, on the other hand, can provide secure and efficient knowledge sharing for vehicular networks with heterogeneous and geographically distributed vehicles and infrastructure in 6G networks. In this paper, we explore the joint use of hierarchical federated learning, as a collaborative machine learning framework, and hierarchical SSI management in vehicular networks, highlighting its advantages, limitations. At the end of the paper, we also provide two illustrative use cases.

This poster presents a use case for smart contract-based inter-provider agreements and Service Level Agreement (SLA) monitoring for 6G networks. We use chainlink oracle and InterPlanetary File System (IPFS) to monitor SLA data logs. We also provide experimental evaluations of two approaches: raw data log access in IPFS and chainlink-based log access. To understand the performance and feasibility of the proposed approaches on a public blockchain, the proposed framework is deployed on the Ethereum and Polygon testnets to measure the cost and latency for both approaches. We measure the latency as well as the total cost for comparison purposes. The maximum cost observed for the first approach is ≈ 1.4 USD, and the maximum latency observed with the first approach is ≈4 seconds in the Polygon testnet and 12 ~ 14 seconds in the Ethereum testnet. However, the second approach's latency is 30 ~ 60 seconds.

The automation and flexibility introduced in the management and orchestration of B5G/6G mobile networks are allowing the creation of innovative vertical use cases considering the coordination of multiple provider domains owned by different operators and/or service providers. The first step to enable this automation is the onboarding of network service artefacts prior to enabling its instantiation in such challenging scenarios. This demonstration focuses on the automatic onboarding operation in a multi-administrative domain scenario. We propose a cloud-native solution deployed at each administrative domain made of an artefact registry management system coupled with an Integration Fabric element following ETSI ZSM guidelines. These elements synchronize between them autonomously and with the associated ETSI NFV management and orchestration stacks ensuring a consistent catalogue of vertical services at the different administrative domains.

The expected complexity of shared 5G/6G cloud and network infrastructures requires a functional management and orchestration (MANO) architecture to automatically provision distinct vertical services. These are mapped as generic network services (NSes) specifying their computing and networking needs: Virtual Network Functions (VNFs) and Virtual Links. We focus on a hierarchical MANO implementation where a Resource Layer orchestrator handles the configuration of the physical infrastructure and exposes an abstract view of it to the upper-layer Service Orchestrator. Two different abstraction philosophies are adopted, namely the Infrastructure Abstraction, which pre-calculates the resource allocations before advertising them, and the Connection Service Abstraction, which exposes potential connectivity services without an actual resource allocation. The resulting abstracted infrastructure becomes the input of the Service Orchestrator to make (placement) decisions fulfilling the NS demands. Thus, a novel placement algorithm is devised which, unlike previous works, can handle NSes with arbitrary VNF topology demands. The performance of the MANO functions is experimentally evaluated by dynamically creating/terminating heterogeneous NSes in terms of: NS blocking, blocked bandwidth/VNF ratio, bandwidth occupancy, and VNF distribution per cloud site. From the results, Connection Service Abstraction does attain a more efficient use of resources and in general, performs better than the Infrastructure Abstraction approach.

With their transparency and smart contract features, blockchain and Distributed Ledger Technologies (DLT) can pave the way toward a decentralized marketplace for multi-administrative domains. An open and decentralized marketplace can aid 6G use cases such as inter-provider agreements, where multi-administrative domains can lease or buy resources to meet the needs of consumers. These agreements between multi-administrative domains can be performed using blockchain, increasing trust and transparency for Service Level Agreement (SLA) management, penalty, or billing. In this demonstration, we present the use of the Ethereum smart contract for a use case where the consumer can buy resources from a provider. However, Ethereum suffers from high transaction costs and latency; therefore, we aim to leverage IOTA Tangle to reduce the cost of transactions on the Ethereum blockchain and minimize the latency. In particular, we show the process of domains registered on an Ethereum blockchain network, consumers selecting a service from the list of available services to buy, and finally, transferring the agreed amount after the services are delivered.

Blockchain and Distributed Ledger Technologies (DLT) can create an agreement that enables stakeholders in the telco world to interact and share services while maintaining trust. As a result, the multiple 6G network stakeholders can perform a role in a marketplace as service providers or consumers. Blockchain and DLT have immutability, transparency, and decentralized ledgers, making them critical in a future-generation network where various administrative domains are controlled by separate operators and resource providers to share resources (virtual network functions, services, or slices). Similarly, an auction-based approach based on the blockchain can eliminate intermediaries while ensuring transparency to increase competitiveness in such an environment. Therefore, in this paper, we propose a solution based on blockchain, considering both approaches (i.e., marketplace and auction). Furthermore, we use IOTA Tangle and IPFS to reduce the transaction and gas costs, which is one of the challenges for Ethereum-based use cases. We also provide experimental evaluations of our use case to analyze the results. It is observed that both options expand the consumer's possibilities to choose, and creating smart contracts adds trust. Moreover, the potential advantages of the proposed solutions are demonstrated through emulation results as a comparison to the traditional ways of doing such agreements between inter-provider domains. We measure transaction latency and the maximum latency observed in our proposed approach is ≈ 25ms. Similarly, the average response time for the Ethereum-based approach is double our proposed approach's average response time. Consequently, the services are completed relatively fast with less latency and faster response time. Moreover, it is observed that in ≈ 2000s, up to 30 services can be completed in a marketplace approach and up to 20 ~ 25 services in the auction. Lastly, the results also explain that by using IOTA-EVM, we can have feeless transactions, and IPFS helps reduce the storage cost up to ≈ 80%.

Online Network Resource Allocation (ONRA) for service provisioning is a fundamental problem in communication networks. As a sequential decision-making under uncertainty problem, it is promising to approach ONRA via Reinforcement Learning (RL). But, RL solutions suffer from the sample complexity issue; i.e., a large number of interactions with the environment are needed to find an efficient policy. This is a barrier to utilize RL for ONRA as on one hand, it is not practical to train the RL agent offline due to lack of information about future requests, and on the other hand, online training in the real network leads to significant performance loss because of the sub-optimal policy during the prolonged learning time. The performance degradation is even higher in non-stationary ONRA where the agent should continually adapt the policy with the changes in service requests. To address this issue, we develop a general resource allocation framework, RADAR, using model-based RL for a class of ONRA problems with known immediate rewards. RADAR improves sample efficiency via exploring the state space in background and exploiting the policy in decisiontime using synthetic samples by the model of the environment, which is trained by real interactions. Applying RADAR on the multi-domain service federation problem to maximize profit via selecting proper domains for service requests deployment, shows its continual learning capability and up to 44% performance improvement w.r.t. the standard model-free RL solution.

Slices are deployed to continuously fulfill requirements from vertical industries. Thus, the management of service level agreements (SLAs) is fundamental. Composite network services (NSs), made up of multiple nested NSs, can be exploited to build slices. Generally, slices may span multiple domains and the different nested NSs may be deployed by different providers, in what is referred to as federated NS. Previous work focused on the architectural design and workflows for the functional deployment of federated services without considering the continuous SLA management. Herein, we cover this gap by presenting a complete workflow, the required interface extensions, and the operation profiling over an experimental testbed to automatically scale nested NSs deployed in a federated domain. Evaluation results show that such scale out/in operations can be performed in tens of seconds (39/31 s, respectively), where the allocation/release of underlying resources account for most of the profiled scaling time (more than 80%).

Heterogeneity is one relevant characteristic of next generation mobile networks. This term embraces not only different kind of infrastructure resources or transmission technologies (e.g., networking vs computing, wireless vs optical), but it also applies to different ways of implementing the network functions (NFs) composing the network services (NSs). This allows the definition of hybrid NSs combining different kind of components, such as physical, virtual, and cloud-native NFs. This demonstration shows the enhancements introduced in the 5Growth platform to manage physical NFs, hence increasing the capabilities of this platform to cope with more heterogeneous hybrid NSs in multi-site scenarios. In particular, we show the deployment of an NS constituted by physical, virtual, and cloud-native NFs implementing an end-to-end service covering access, mobile core and application functionalities.

5G marks the beginning of a revolution in the mobile network ecosystem, transitioning to a network of services to satisfy the demands of new telecom players, the vertical industries. This revolution implies a redesign of the overall mobile network architecture to provide it with the automation and programmability capabilities to handle the envisioned heterogeneity, flexibility, and dynamicity. Software Defined Networking (SDN), Network Function Virtualization (NFV), and Network slicing are key enabling concepts to provide such capabilities. They are complementary, but they are still in its infancy and the synergies between them must be exploited to realise the mentioned vision. This thesis contributes to its development and integration in next generation mobile networks by designing an end-to-end (E2E) network service orchestration architecture. This proposed architecture, which is aligned with some guidelines and specifications provided by main standardization bodies, goes beyond current management and orchestration (MANO) platforms to fulfill network service lifetime requirements in heterogeneous multi-technology network infrastructures shared by concurrent instances of diverse network services. Other relevant contribution of this work is the multi-administrative domain capabilities of the proposed architecture, going beyond high-level considerations done in existing literature that prevent its effective realisation. The resulting architecture, workflows, and interfaces are validated and assessed through practical implementations in experimental infrastructures with network service definitions close to real vertical use cases (e.g., automotive and eHealth), which help bridging the gap between network providers and vertical industries stakeholders.

6G is expected to play a crucial role in future communication evolution, which will address the current challenges 5G faces. Blockchain and Distributed Ledger Technologies (DLT) have gained significant attention due to their decentralization, transparency, security, and emerging smart contract-based applications. In telecom sectors, the mismatch between the requirements of increasing use-cases and 5G network capabilities steered the demand for decentralized blockchain-based 6G architecture. Smart contracts can automate an inter-provider agreement that allows stakeholders or actors of a platform or a marketplace to transact and exchange services while maintaining trust. The characteristics of DLT are critical in a future-generation network where separate operators control various administrative domains and resource providers share resources (virtual network functions, services, or slices). This study aims to provide a framework for a smart contract-based inter-provider agreement leveraging IOTA Tangle, IPFS, and chainlink to minimize the overall cost of use-cases of 6G networks (such as inter-provider agreements). The proposed framework is emulated on the testnet to analyze the results. Moreover, the potential advantages of the proposed solutions are demonstrated through emulation results. When establishing the smart contract using IOTA, the transaction cost drops since IOTA supports fee-less transactions. In addition, the overall cost of the storage by using IPFS is roughly reduced by more than 80%. Furthermore, we reduce the overall data access cost by approximately 50% using chainlink in our approach.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.

Network service federation in 5G/B5G networks enables service providers to extend service offering by collaborating with peering providers. Realizing this vision requires interoperability among providers towards end-to-end service orchestration across multiple administrative domains. Smart admission control is fundamental to make such extended offering profitable. Without prior knowledge of service requests, the admission controller (AC) either determines the domain to deploy each demand or rejects it to maximize the long-term average profit. In this paper, we first obtain the optimal AC policy by formulating the problem as a Markov decision process, which is solved through the policy iteration method. This provides the theoretical performance bound under the assumption of known arrival and departure rates of demands. Then, for practical solutions to be deployed in real systems, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show that learning approaches outperform the greedy policy and are capable of getting close to optimal performance. More specifically, R-learning always outperformed the rest of practical solutions and achieved an optimality gap of 3-5% independent of the system configuration, while Q-Learning showed lower performance and depended on discount factor tuning.

In 5G networks, heterogeneous vertical services with different requirements are rolled out over a common multi-technology infrastructure. A resource orchestrator entity automatically coordinates the operations and functions to support the service's lifecycle management (i.e., creation, update and termination). Moreover, it is essential that service needs are continuously assured even if transport network anomalies (e.g., link failures) occur. Herein, we present an implemented resource orchestrator architecture integrating monitoring capabilities to attain closed-loop operations for: i) gathering monitored information; ii) detecting transport network anomalies; and iii) triggering the required action (e.g., restoration) to keep the service continuity. When a link failure happens, several transport services may be disrupted requiring to be immediately restored. To this end, we propose a novel on-line restoration algorithm called as Global Concurrent Optimization (GCO). The GCO algorithm aims at attaining an enhanced restorability performance compared to a more traditional restoration algorithm (referred to as 1-by-1). Both algorithms are experimentally compared on top of the deployed resource orchestrator architecture. The evaluation is done upon both dynamic service arrival/departure and link failure generation using different performance metrics: the average restorability, the average network resource utilization, and the restoration computational time.

Next generation mobile networks base on the auto- mated, flexible, and dynamic orchestration of virtualised network services (NSs). These NSs are made up of Virtual Network Functions, which have hitherto mostly been implemented by means of virtual machines. The current trend is to include the use of containers, the so-called Cloud-native Network Functions, which may fit better the need of NS deployments embracing edge infrastructures having constrained resources. This ends up in the definition of NSs mixing both kinds of network functions (NFs) satisfying the needs of network operators and vertical industries and the characteristics of available infrastructures. We refer to the use of both kinds of NFs in a single NS as Hybrid NS. This demonstration presents the extensions done in the 5Growth management and orchestration platform to perform the deployment of such kind of NSs in a multi-site infrastructure, including the dynamic interconnection of the deployed NFs according to their nature.

Measuring several relevant metrics related to Network Function Virtualization (NFV) service lifecycle management in real time brings an enhanced monitoring of the operation of the network service orchestrator (SO) and the NFV infrastructure. In this demonstration, we integrate a complete data engineering pipeline in an operational management and orchestration stack (that of EU 5Growth project) to analyze lifecycle management metrics in real-time, in this case the network service instantiation time related metrics. In our demonstration, a data connection module instance continuously monitors the NFV SO log files and sends the log changes to the data ingestion layer, where log files are temporarily stored to be fetched by Apache Spark jobs. After utilizing Spark jobs to cleanse the log files and to obtain the service instantiation times, the metrics are sent back to the data ingestion layer to be transferred to the Elasticsearch (ELK) stack for data indexing and visualization purposes. Furthermore, the statistical information of network service instantiation (in total and its components) of studied metrics inside the network can also be profiled via a separate data analysis layer connected to the ELK stack.

In addition to CPRI, new functional splits have been defined in 5G creating diverse fronthaul transport bandwidth and latency requirements. These fronthaul requirements shall be fulfilled simultaneously together with the backhaul requirements by an integrated fronthaul and backhaul transport solution. In this paper, we analyze the technical challenges to achieve an integrated transport solution in 5G and propose specific solutions to address these challenges. These solutions have been implemented and verified with pre-commercial equipment. Our results confirm that an integrated fronthaul and backhaul transport dubbed Crosshaul can meet all the requirements of 5G fronthaul and backhaul in a cost-efficient manner.

Empowered bynetwork softwarization, 5G systems have become the key enabler to foster the digital transformation of the vertical industries by expanding the scope of traditional mobile networks and enriching the network service offerings. To make this a reality, we propose anautomationsolution for vertical services provisioning and hierarchical Service Level Agreement (SLA) management.Service scalingis one of the most essential operations to adapt the service deployments and resource allocations to ensure SLA fulfilment. Three different scaling levels are addressed in this work: application-, service- and resource-level. We have implemented our solution in a proof-of-concept of a virtualized mobile network platform, spanning over three geographically-distributed sites. To evaluate our solution, we leverage field tests, focusing onautomotive vertical servicescomprising a mission-critical application (collision-avoidance) and an entertainment one (video streaming). The results demonstrate the excellent performance of our solution, and its ability to automatically deploy vertical services and ensure their SLAs through different levels of service scaling.

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive “greedy” approach in a realistic scenario.

The composition of NFV-network services (NSs) helps 5G networks to provide the required flexibility and dynamicity to satisfy the needs of vertical industries. The mentioned capabilities are required not only at instantiation time but also during operation time to maintain the corresponding service level agreements in the presence of changing network conditions. In this demonstration, we present the capabilities of the 5Growth platform to handle the scaling of composite NSs under different situations. In particular, we show the scaling of a composite NS implying the update of the required deployed nested NS components and its interconnections but also how the scaling of a shared single NS entails updates on its associated composite NSs.

5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.

In this work, we propose an innovative approach to contrast the bufferbloat, one of the primary sources of queueing latency, in 3GPP mobile networks. Our purpose is to validate an all-software, in-node traffic-control infrastructure, similar to what the Linux operating system already employs for Ethernet and WiFi networks. We introduce the IPtraffic-control infrastructure in the RAN part of an LTE network, for both UE and eNB, that can perform packet scheduling on flows directed to the same Data Radio Bearer. The effectiveness of such strategy leverages on a reduced usable size of the in-node RLC buffer, thanks to the BQL algorithm, which we evaluate for the first time in the context of LTE. By using BQL, we can create a backlog of packets that stays in the Traffic Control layer, on which it is possible to perform packet scheduling without bloating the RLC buffer. We compare the performance of BQL to another algorithm, called DynRLC, proposed to dynamically size the RLC buffer eNB side. The preliminary evaluation of the proposed approach is done on the ns-3 network simulator and it shows that the proposed approach with BQL significantly improves the flow delay, by controlling the queueing latency, as well as enhancing the flow isolation.

5G mobile networks need flexilibity, dynamicity and programmability to satisfy the needs of vertical industries. In such context, network services can be designed as integral units to be dynamically grouped among them to create tailored complex composite services or to allow the combination of services in a context of network slicing. In this demonstration, we present the service composition capabilities of the 5G-TRANSFORMER platform. In particular, we will show the instantiation of a composite network service using a previously instantiated service. When terminating the composite network service, the initial instantiated network service resumes its operation without disruption.

Future 5G networks will rely on heterogeneous deployment of micro eNodeBs, and is expected that wireless transport interfaces, deployed together with each eNodeB, will provide high-speed backhaul connectivity through the possibility to create an ad-hoc wireless mesh between the deployed micro eNodeBs. Therefore, the backhaul routing strategy will play a fundamental role in exploiting the backhaul resources. In this context, we aim to investigate the advantages and limitations of qualitatively different backhaul routing strategies as UEs download TCP and UDP content. In this paper, we compare a backpressure-based strategy that takes per-packet or per-flow basis (BP-MR) and traditional single path routing (OLSR). Ns-3 simulations show that the per-packet BP-MR variant enhances UDP performance by circumventing congestion but degrades TCP because of reordering. The per-flow BP-MR variant minimizes retransmissions by limiting the excessive reordering, and outperforms OLSR performance for both UDP backhaul traffic and TCP traffic.

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.

In recent years, the Linux kernel has adopted an algorithm called TCP Small Queues (TSQ) for reducing queueing latency by controlling buffering in the networking stack. This solution consists of a back-pressure mechanism that limits the number of TCP segments within the sender TCP/IP stack, waiting for packets to actually be transmitted onto the wire before enqueueing further segments. Unfortunately, TSQ prevents the frame aggregation mechanism in the IEEE 802.11n/ac standards from achieving its maximum aggregation, because not enough packets are available in the queue to build aggregates from, which severely limits achievable throughput over wireless links. This paper demonstrates this limitation of TSQ in wireless networks and proposes Controlled TSQ (CoTSQ), a solution that improves TSQ so that it controls the amount of data buffered while allowing the IEEE 802.11n/ac aggregation logic to fully exploit the available channel and achieve high throughput. Results on a real testbed show that CoTSQ leads to a doubling of throughput on 802.11n and up to an order of magnitude improvement in 802.11ac networks, with a negligible latency increase.

The 5G New Radio (NR) access technology defines multiple numerologies to support a wide range of carrier frequencies, deployment scenarios, and variety of use cases. In this paper, we consider a resource allocation problem to efficiently support multiple numerologies simultaneously. We assume frequency division multiplexing (FDM) of numerologies in a time division duplex (TDD) system with a self-contained slot format. We focus on optimizing the numerology subband (SB) configuration, as well as the duplexing ratio between downlink (DL) and uplink (UL) directions within each SB. The optimization problem minimizes the weighted sum of the normalized load (NL) for each SB in each direction. We prove that our optimization problem is convex and, furthermore, we derive the optimal closed-form expressions for the numerology SB configuration and the DL-UL duplexing ratio per SB. The effectiveness of the proposed resource allocation is validated through an end-to-end ns-3 based simulator, which shows how the optimization of the NLs is translated into an improved throughput and delay performance.

The virtualization of mobile network functions constitutes one of the main blocks for addressing the high flexibility requirements of fifth generation (5G) communication systems. Reconfigurable hotspots are expected to be massively deployed to enable on-demand services and dynamically adapt the network capacity according to traffic requirements. In this paper, we present the extensions and modifications of the long term evolution (LTE) module of the ns-3 simulator (LENA) to include a software defined radio (SDR) physical layer implementation. These extensions combine the native flexibility of the simulator with the SDR features of a real-time prototype. Moreover, the framework was designed to distribute the communication functions across different elements of the network with the possibility of adjusting several transmission parameters as in a network function virtualization (NFV) paradigm. Thanks to an emulated full network protocol stack, the prototype allows the experimentation of novel 5G solutions and the evaluation of relevant key performance indicators (KPIs) from the lower layer protocols up to application level. To this aim, we present the experimental evaluation of the KPIs of energy, latency, throughput and reconfiguration time in relevant scenarios.

License Assisted Access (LAA) is the technology introduced by the Third Generation Partnership Project (3GPP) that enables the deployment of LTE networks in the unlicensed 5 GHz spectrum. To ensure a fair coexistence of LAA in the unlicensed spectrum with other technologies, e.g., with Wi-Fi, 3GPP has standardized the use of Listen Before Talk (LBT) as the default channel-access scheme for LAA. However, the performance of Wi-Fi when coexisting with LAA mainly relies on how the LBT parameters are configured by the LAA. In this paper, we focus on the Contention Window (CW) size parameter of LBT in LAA. We propose a Neural Network (NN) based scheme that adapts the CW size based on the predicted number of Negative Acknowledgments (NACKs) for all the subframes in a Transmit Opportunity (TXOP) of LAA. In particular, our proposed scheme learns from the past experience how many NACKs per subframe of a TXOP were received under certain channel conditions. The performance evaluation shows that our proposed scheme, when compared to the state-of-the-art approaches, provides the best trade-off between the fairness to Wi-Fi and the LAA performance in terms of both throughput and latency.

As Mobile Network Operators (MNOs) are shifting towards Cloud- Radio Access Network (C-RAN), they have to upgrade their infrastructure to not only support higher processing capacities but also to be more resilient. We consider the problem where a MNO is faced with the choice of selecting virtualized Baseband Units (BBUs) from various cloud service providers, that are each characterized with distinct failure probabilities and prices. We propose to solve the BBU selection problem, formulated as an Integer Linear Program (ILP) subject to BBU capacity and virtualization cost using the Branch- and- Price algorithm. We present several schemes depicting which optimization goal the MNO can foster the most: BBU processing power minimization, resiliency, traffic handling or all. Simulation results demonstrate the good performance of our algorithm to solve the BBU selection problem for all schemes, while also emphasizing the advantages of a particular one that can realize more than 10% in virtualization cost savings.

Planning of current and future mobile networks is becoming increasingly complex due to the heterogeneity of deployments, which feature not only macrocells, but also an underlying layer of small cells whose deployment is not fully under the control of the operator. In this paper, we focus on selecting the most appropriate Quality of Service (QoS) prediction techniques for assisting network operators in planning future dense deployments. We propose to use machine learning as a tool to extract the relevant information from the huge amount of data generated in current 4G and future 5G networks during normal operation, which is then used to appropriately plan networks. In particular, we focus on radio measurements to develop correlative statistical models with the purpose of improving QoS-based network planning. In this direction, we combine multiple learners by building ensemble methods and use them to do regression in a reduced space rather than in the original one. We then compare the QoS prediction accuracy of various approaches that take as input the 3GPP Minimization of Drive Tests (MDT) measurements collected throughout a heterogeneous network and analyse their trade-offs. We also explain how the collected data is processed and used to predict QoS expressed in terms of Physical Resource Block (PRB)/ Megabit (MB) transmitted. This metric was selected because of the interest it may have for operators in planning, since it relates lower layer resources with their impact in terms of QoS up in the protocol stack, hence closer to the end-user.

Future dense mobile networks will imply much higher costs both in access and backhaul. This paper analyzes the effect on wireless mesh backhaul routing performance when energy saving policies are present at the radio access network (RAN). We consider an heterogeneous two-tier network where small cells (SC) with energy harvesting capabilities extend the capacity of the macro base stations (MBS), and can autonomously switch on-off in order to increase the energy efficiency of the network based on a Q-learning (QL) algorithm. Instead of calculating new routes for each SCs activation pattern, we propose to agnostically adapt to the RAN traffic demands using a non-route-based backpressure routing policy for the wireless mesh backhaul to even the network resource usage amongst SCs. We used the ns-3 simulator to integrate the different mobile network segments: RAN, wireless mesh backhaul, and evolved packet core (EPC). Simulation results show an achieved reduction of the %37% of the RAN energy consumption while satisfying traffic demands with an improvement of up to a factor of 10 of delay performance in the backhaul during peak hours.

This paper presents a machine learning based handover management scheme for LTE to improve the Quality of Experience (QoE) of the user in the presence of obstacles. We show that, in this scenario, a state-of-the-art handover algorithm is unable to select the appropriate target cell for handover, since it always selects the target cell with the strongest signal without taking into account the perceived QoE of the user after the handover. In contrast, our scheme learns from past experience how the QoE of the user is affected when the handover was done to a certain eNB. Our performance evaluation shows that the proposed scheme substantially improves the number of completed downloads and the average download time compared to state-of-the-art. Furthermore, its performance is close to an optimal approach in the coverage region affected by an obstacle.

In the coming years, planning future mobile networks will be infinitely more complex than nowadays. Future networks are expected to present multiple Network Management (NM) challenges to operators, such as managing network complexity in terms of densification of scenarios, heterogeneous nodes, applications, Radio Access Technologies (RAT), among others. In this context, the exploitation of past information gathered by the network is highly relevant when planning future deployments. In this paper we present a network planning tool based on Machine Learning (ML). In particular, we propose an approach which allows to predict Quality of Service (QoS) offered to end-users, based on data collected by the Minimization of Drive Tests (MDT) function. As a QoS indicator, we focus on Physical Resource Block (PRB) per Megabit (Mb) in an arbitrary point of the network. Minimizing this metric allows serving users with the same QoS by consuming less resources, and therefore, being more cost-effective. The proposed network planning tool considers a Genetic Algorithm (GA), which tries to reach the operator targets. The network parameters we desire to optimise are set as the input to the algorithm. Then, we predict the QoS of the network by means of ML techniques. By integrating these techniques in a network planning tool, operators would be able to find the most appropriate deployment layout, by minimizing the resources (i.e., the cost) they need to deploy to offer a given QoS in a newly planned deployment.

After a disaster, it is unlikely to expect current terrestrial infrastructures to provide the coverage and the Quality of Service required by emergency operators. Currently, PPDR operators go towards a portable LTE infrastructure that provides an access network to field operators, backhauled by a wireless mesh network that can route traffic from and towards Internet through a satellite gateway. We firstly propose a new per-flow strategy for a backpressure-based routing protocol, BP-MR, and then in the aforementioned environment we analyse the original BP-MR per-packet, the proposed per-flow, and OLSR as routing protocol for the mesh network coupled with three TCP congestion control (Cubic, Hybla, Vegas) performing a typical HTTP transfer. We analysed the throughput, the latency, and the scalability over the number of the flows/operators. An extensive simulation set allows to conclude that employing BP-MR per-flow in the mesh network coupled with TCP Vegas for performing the end-to-end transfer improves the performance for PPDR professionals.

The deployment of dense networks of Small Cells (SC) is one of the key components of 5G mobile networks and will pose several challenges to the backhaul network. Software-Defined Networking (SDN) is a key technique to cope with the increased management complexity of such heterogeneous deployments, helping in the task of achieving a system-wide network management, which includes the 5G mobile wireless backhaul. Therefore, it is fundamental to devise the tools for the evaluation of 5G backhauling SDN architectures. This paper presents WiseHAUL: an SDN-empowered Wireless Small Cell Backhaul testbed. We present the design considerations of WiseHAUL, deployed in the form of an hypercube mesh that combines heterogeneous wireless technologies with the final goal of conducting SDN research and experimentation. Preliminary experimental results serve to identify the main research challenges faced by canonical SDN management techniques in an all-wireless backhaul deployment, hence acting as key drivers for further redesign and testbed development towards hybrid SDN models.

The backhaul of upcoming dense 5G Small Cell (SC) networks needs a full redesign, but there is no clear consensus on how to deploy such infrastructure. A low-cost backhaul solution is that provided by a wireless mesh network. Additionally, Software Defined Networking (SDN) is being considered as an alternative to distributed approaches to lower network infrastructure costs while enabling programmability and flexibility mostly for wired networks. This paper evolves the canonical SDN model by presenting a service-based hybrid SDN (hSDN) model that alleviates the problems caused by the unreliability of the in-band control channel formed by a wireless mesh backhaul between the SDN controller and the SCs. At the infrastructure level, we propose a wireless mesh backhaul combining sub-6GHz and millimeter wave links with long range microwave links. This architecture pursues the coexistence of network services located on top of a centralized SDN controller, with distributed network services, such as routing. To show the robustness of our proposed model, we compare a service-based hSDN model and a canonical SDN model under perfect control plane communication channel. In our service-based hSDN model, we further compare two distributed routing schemes used as fallback control plane mechanisms when the SDN controller is unreachable. Simulation results with ns-3 show improvements of up to 1.5x and 6x in terms of throughput and latency, respectively.

This paper presents Backpressure for sparse small cell deployments (BS), a transport network layer solution aimed to carry backhaul traffic under sparse topologies. BS leverages queue backlog and geographic information to take forwarding decisions on a per-packet basis, without incurring into excessive path lengths and queueing latencies. Simulations were carried out in ns-3 under diverse traffic loads. Our scheme shows similar performance to that of an idealized zero-overhead shortest path routing protocol (ISPA) under light traffic conditions, while outperforming a state of the art geographic routing scheme (i.e., GPSR). Under more severe traffic conditions, BS shows better performance than ISPA and GPSR. In particular, BS attains a reduction of up to 85% in latency regarding GPSR. Overall, this shows how a (quasi-)stateless low overhead scheme can scale to be able to serve the realistic backhaul topologies of interest in future massive small cell deployments.

In this paper we explore the concept of self-organized location management in large-scale networks of femtocells (NoFs). A NoF is a mesh network that provides multi-hop connectivity amongst femtocells and, in turn, to the Evolved Packet Core. One of the most challenging issues in NoFs is location management, i.e., the process of determining the location of a User Equipment (UE) in the network. Standard 3GPP location management mechanisms are not well suited for large-scale NoFs due to the overhead generated by frequent handovers and cell reselections. In this paper we propose a self-organized Tracking Area List (TAL) mechanism that seamlessly monitors the mobility state of each UE in order to adjust the size of its individual TAL dynamically. Analytical results show that our mechanism reduces location signalling traffic in the cellular network compared to standard 3GPP schemes. In addition, the self-organized TAL mechanism is fully compliant with 3GPP LTE Technical Specifications, which facilitates its implementation in a commercial scenario.

In our previous work on distributed backpressure routing for WiFi mesh networks (WMN), we showed that backpressure routing when combined with geographic information results in a (quasi-)stateless low-overhead (hence scalable) routing solution. Specifically, the Lyapunov drift-plus-penalty optimization framework allows trading off between 1) routing decisions for maintaining queue backlogs under control (and hence, the network stable) and 2) those that try to get close to the optimal value of an objective performance metric. Such framework offers a non-negative parameter (V) for weighting both components. However, fixed-V policies cannot efficiently handle WMN traffic dynamics in practical setups, as they will lead to queue overflows and degradation of the objective metrics. In this paper, we propose the first practical distributed variable-V algorithm that takes routing decisions aiming at achieving ideal objective metric values, yet not incurring into queue overflows. Furthermore, we compare variable- and fixed- V policies under several network setups. The simulation results obtained with ns-3 show that the variable-V policy is not only able to obtain the same throughput as that of the best fixed- V policy, but also to remarkably reduce the variability and maximum values of packet delay.

This paper studies practical backpressure routing strategies in WiFi-based Mesh Networks (WMN). It is practical in the sense that, unlike previous theoretical centralized algorithms, we present a distributed implementation of the algorithm with low queue complexity (i.e., one finite data queue at each node) to deal with any-to-any communications. To our knowledge, this is the first practical study based on Neely's Lyapunov optimization framework for WMNs. In fact, we propose a scalable and distributed routing policy that takes control actions based on Lyapunov's drift-plus-penalty minimization combinining local queue backlog and 1-hop geographic information. We characterize its strengths and weaknesses against network performance metrics such as throughput, delay, and fairness. By means of ns-3 simulations under different configuration setups, we study the impact of the weight of the penalty function on the network performance metrics. In addition, we show the influence of the location of the source-destination pairs in these configuration setups. Finally, we evaluate the objective function-backlog trade-off that characterizes Lyapunov optimization frameworks.

The increasing demand of mobile data traffic is starting to stress the networks of mobile network operators (MNOs). Techniques for offloading traffic (partially or totally) from the network of the MNO are currently being designed and deployed at various points of the network, which depend on the goal of each MNO. Such techniques divert traffic to offloading networks, such as Wi-Fi access networks, femtocells, etc., or directly to the Internet. This paper presents a generic analytical approach for studying the impact that offloading techniques might have on the networks of MNOs. The model is generic in the sense that it is independent of the specific offloading technology used and may be of use to provide bounds on network dimensioning. Based on previous measurements found in the literature, our model assumes that user activity periods and periods characterizing offloading are heavy-tailed. We model them as strictly alternating independent ON/OFF processes. Therefore, the non-offloaded traffic (i.e., that traffic still being served by the MNO on a regular basis) is modeled as the product of these two processes. We prove that the resulting process is long-range dependent, with heavy-tailed ON/OFF-period durations, and its characteristic parameters can be derived from those of the initial processes. We also evaluate the network resources required to serve the traffic resulting from the aggregation of many such sources. We conclude that offloading does not always mean reduction of resource consumption, and that the distribution of offloading periods turns out to be the main design parameter to deploy effective offloading strategies in the networks of MNOs. Extensive simulations, in which the Hill's estimator was used to obtain the main parameters of the resulting process, confirm the results of our analytical study.

This paper evaluates the applicability to WMNs of dynamic backpressure routing strategies, which theoretically maximize throughput but can result in high end-to-end delays. We present a distributed and (quasi-)stateless routing protocol called DiPUMP (forwarDIng of Packets for distribUted resource consuMPtion). Unlike the theoretical form of the backpressure algorithm, DiPUMP does not require a centralized entity computing the routes for the whole network at any instant. Therefore, its distributed operation enables its implementation and deployment in practical WMNs. By taking routing decisions on a per-packet basis, DiPUMP is able to substantially improve aggregated throughput, while maintaining acceptable end-to-end delays. We compare DiPUMP with a tree-based protocol, which is taken as a representative protocol for topology-based strategies. NS-3 simulation results show that DiPUMP and tree-based routing behave similarly under light loads. However, under medium and high loads, DiPUMP provides better results in terms of aggregated throughput, delay, and fairness.

In this paper we explore the concept of an all-wireless Network of Femtocells (NoF). A NoF can be seen as a wireless mesh network providing multi-hop connectivity amongst femtocells and, in turn, to the Evolved Packet Core (EPC). As specified by 3GPP Technical Specifications (TS), IP networks are used to transport data through any interface and, in particular, through the S1 interface in the Evolved Packet System (EPS). More specifically, the transport network layer of the NoF acts as transport for the control- and user-plane data through the S1-MME and S1-U interfaces, respectively. This novel scenario requires specific algorithms for the optimal routing of packets through the S1 interface within the NoF. In order to do so, we propose DiPUMP, a distributed and stateless routing protocol based on backpressure theory. Up to our knowledge, DiPUMP is the first protocol proposed to support control- and user-plane data routing in an all-wireless NoF. We also compare DiPUMP to AODV-ST, a tree-based routing protocol taken as representative for routing protocols commonly found in data networking scenarios that are equivalent to NoFs. Our evaluation results show that, on average, DiPUMP is able to transfer up to a 30% more traffic load than AODV-ST. Finally, we describe the implications of the NoF paradigm and DiPUMP in the 3GPP architecture.

This paper explores the possibility to use the medium utilization as a metric to be included in Radio Environment Maps (REMs) of WLAN networks. The main objective is to build REMs that go beyond the spectrum use and that can be used to improve deployment and operation of WLANs. Specifically, the paper focuses on, first, exploring the possibility to approximate wireless channel utilization as a spatial random field. Second, the paper proposes an approach to obtain distributed measurements of medium utilization and spatial characterizations of to build utilization maps. And, third, the paper studies the performance of regular spatial interpolation techniques when used to build utilization maps. We have used a WLAN network testbed to support the study for which we obtain and analyze practical maps. Our experimental results show that medium utilization of any location of the area under study can be estimated with average errors of 10\% by only using a limited number of measurement points.

Call Admission Control (CAC) is recognized as one of the key strategies to achieve satisfactory QoS support for VoIP over IEEE 802.11 WLANs. However, most of the prior CAC solutions for VoIP over WLAN are centralized, and the few distributed CAC schemes proposed so far do not account for issues such as the loss of channel time due to medium contention and the coexistence of VoIP traffic with background traffic such as TCP data flows. In this paper, we describe a distributed CAC scheme which aims at addressing these issues by leveraging on channel monitoring techniques, thus being readily implementable in today's consumer devices. The proposed scheme is thoroughly evaluated by means of testbed experiments as well as network simulations in different scenarios, and it is shown to yield a better CAC performance with respect to prior solutions.

This paper presents the experimental performance evaluation of the distributed Virtual Home Region Multi-Hash Location Service (VIMLOC). This is, up to our knowledge, the first location management scheme specifically designed and implemented for medium/large-scale wireless mesh networks (WMNs,) which are seen as a cost-effective solution to provide broadband wireless access in large areas. Therefore, if the use of geographic routing is assumed for scalability reasons, such a location management is needed to map between the identifier of a node and its current position in the network (i.e., location address). Furthermore, this paper also presents what is, up to our knowledge, the first experimental performance comparison over a WMN testbed of three different location management schemes, namely proactive, reactive, and VIMLOC. All three schemes have been implemented in the Click modular router framework. For the scenario under test, the quantitative results show that the VIMLOC protocol has comparable (or better) success rate to that of the proactive and reactive schemes. Additionally, VIMLOC outperforms these schemes in terms of overall overhead, state volume, and efficiency parameters, in general.

Nowadays, mobile users wish to use their multi-interface mobile devices to access the Internet through network points of attachment (PoA) based on heterogeneous wireless technologies. They also wish to seamlessly change the PoAs during their ongoing sessions to improve service quality and/or reduce monetary cost. If appropriately handled, multihomed mobile nodes offer a potential solution to this issue. In this paper, we present an improvement of SIP mobility (pre-call plus mid-call mobility) to support seamless mobility of multihomed mobile nodes in heterogeneous wireless networks. Pre-call mobility is extended to associate user identifier (i.e. SIP URI) and interface identifiers (i.e. IP addresses). The multiple addresses of a mobile device are weighted by the user to create a priority list in the SIP server so as to guarantee resilient reachability of mobile nodes and to avoid unnecessary signaling through wireless links, thus saving radio resources. Then, three variations of mid-call mobility, called hard, hybrid and soft procedures, are also proposed. Their main aim is to minimize, or even avoid, packet losses during interface switching at the mobile node. The proposed solutions have been implemented in a wireless heterogeneous testbed composed of 802.11 WLAN plus 3.5 cellular network, which are fully controlled and configurable. The testbed has been used to study the performance and the robustness of the three proposed mid-call mobility procedures.

WLAN devices based on CSMA/CA access schemes have become a fundamental component of network deployments. In such wireless scenarios, traditional networking applications, tools, and protocols, with their built-in measurement techniques, are usually run unchanged. However, their actual interaction with the dynamics of underlying wireless systems is not yet fully understood. A relevant example of such built-in techniques is bandwidth measurement. When considering WLAN environments, various preliminary studies have shown that the application of results obtained in wired setups is not straightforward. Indeed, the contention for medium sharing among multiple users inherent to CSMA/CA access schemes has remarkable consequences on the behavior of and results obtained by bandwidth measurement techniques. In this paper, we focus on evaluating the effect of CSMA/CA-based contention on active bandwidth measurement techniques. As a result, it presents the rate response curve in steady state of a system with both FIFO and CSMA/CA-based contending cross-traffic. We also find out that the distribution of access delay shows a transient regime before reaching a stationary state. The duration of such transient regime is characterized and bounded. We also show how dispersion-based measurements that use a short number of probing packets are biased measurements of the achievable throughput, the origin of this bias lying on the transient detected in the access delay of probing packets. Overall, the results presented in this paper have several consequences that are expected to influence the design of bandwidth measurement tools as well as to better understand the results obtained with them in CSMA/CA links.

This paper presents a reconfigurable framework used to analyze the performance of different technologies in WLAN environments. This framework incorporates state-of-art experimentation tools into a single control paradigm that allows an easy reconfiguration and repetition of experimentation trials. The paper describes the main features of the testbed together with some of the technical solutions adopted. Also, an illustrative experimentation case is described, step-by-step, to show the performance of this tool. Specifically this experimentation case studies the performance of VoIP applications across different wireless scenarios. It reveals some patterns of the behaviour of this type of applications that would be difficult to observe in other more specific experimentation platforms.

This paper analyzes the call quality perceived by Skype users in both fixed and mobile environments. Specifically, LAN, WLAN, ADSL and UMTS network connections are evaluated by means of both PCs and mobile terminals. The end- to-end delay is below 150 ms in all cases, so it does not degrade the call quality (in the case of no echo loss in the path). The traffic patterns of Skype were also studied for uplink/downlink, private/public addresses, which node initiates the call, and what kind of terminal is used. Additionally, PESQ measurements reveal that Skype calls are above the minimum acceptable quality when using PC-based nodes, whereas quality is not acceptable when using mobile terminals. The effect of the CPU load of the mobile terminal on the perceived call quality has also been analyzed.

This paper experimentally analyzes the handover performance of VoIP sessions in a wireless overlay of 802.11 WLANs and GPRS/UMTS networks when mobility is handled at the application layer by SIP. It also assesses the impact of handovers on the VoIP call quality perceived by the user by means of the extended E-model. The study reveals that good performance values are achieved when handing over from GPRS/UMTS to WLAN. Additionally, acceptable quality is obtained for handovers from WLAN towards UMTS. On the other hand, unacceptable values are achieved when moving towards GPRS. The main reason is the delay experienced by SIP messages when traversing the cellular network.

This paper presents an experimental assessment of the subjective quality perceived by end-users of mobile VoIP calls in Mobile IPv6 and SIP mobility scenarios. This assessment is done using the ITU-T E-model. The study shows that the tested implementations of MIPv6 and SIP mobility are not good enough at providing acceptable voice quality (i.e. R-factor of the E-model ⩾ 70.) However, the detailed evaluation of the factors that contribute to the total handoff latency allows identifying various performance enhancements. Various solutions are then proposed to adapt the MIPv6 and SIP mobility implementations to provide mobility to VoIP clients with acceptable quality.

This paper assesses the performance of wireless sniffers built using off-the-shelf hardware and software. Such an analysis tool is commonly found in wireless testbeds given its low cost. The goal of this study is to provide a framework to assess the performance of measurements obtained using this popular tool. Among various contributions, the study shows the importance of precisely checking the behavior of the hardware used for communications and for monitoring in order to validate the traces obtained. It also presents a methodology to assess the performance of wireless sniffers based on obtaining a correlation factor between traces captured by independent devices. The paper reveals, in addition, a new type of loss in sniffer captures, the saturation loss, that completes previous work on this topic. Finally, the paper also studies the behavior of architectures using several sniffers in a single computer. It reveals how, again in this case, the hardware chosen is critical to obtain desired performance.

In this paper we evaluate the performance of a wireless IEEE 802.11-based node multi-hop mesh backbone for the provision of VoIP services, in the cases when nodes are provided with just a single NIC and when they are equipped with more than one NIC and different codecs are used. In particular, we show that the hard delay and packet loss constraints demanded by real time services constrain the size of the wireless backhaul to a few nodes when single-NIC nodes are used. Furthermore, this constraint depends on the link rate. This limitation is due to overlapping carrier sensing ranges that generate additional delay due to contention, which results in a strong degradation of the overall quality of the conversation. For the scenarios tested, the utilization of multi-NIC nodes overcomes these problems by allowing keeping the delay and losses within acceptable values while permitting larger wireless backhauls in terms of number of nodes. In this way, the MAC 802.11 policy keeps on playing a prevalent role in the very actual trend to build `all-wireless' architectures in spite of the limitations experienced in the single-NIC case

Ever increasing network complexity and/or lack of network managers in a given organization justify the need for autoconfiguration mechanisms. IPv6 already provides mechanisms for autonomous address configuration of end-hosts. However, router address autoconfiguration is still lacking. This paper addresses this issue by studying the no administration protocol (NAP) distributed mechanism and comparing it to centralized mechanisms. Particularly, the evaluation is focused on the time it takes to distribute prefix information to all the nodes when powering up the network. A study on the dependency of this time on some characteristics of the network is also presented. Results show that, for the simulated scenarios, NAP performs better than centralized mechanisms, though, in some cases, the difference is not very important.

VC merge has been presented as the most likely implementation to offer multicast forwarding in MPLS environments when ATM is used as data link layer. But it presents some characteristics that may not be desirable, like extra buffering and delay, and modification of traffic characteristics. Other types of mechanisms are presented in the literature to solve these problems. Compound VC (CVC) is one of these mechanisms. This paper presents a general comparison of VC merge and CVC. It finally discusses the implementation issues of a CVC switch. Though its implementation is more complex, it is shown that CVC forwarding could be carried out by using state-of-the-art ATM hardware.

One of the problems to deal with when offering multicasting at the ATM level is forwarding. When using ATM adaptation layer 5 (AAL5), the cell-interleaving problem must be solved to be able to properly reassemble AAL5 packets at end-systems. Compound VC (CVC) is one of the solutions that have been proposed. Its main characteristic is the utilization of dynamically assigned packet identifiers to allow multiplexing. The convenience of this approach is validated through simulation. This paper also presents some preliminary results on the behavior of the packet loss probability due to running out of identifiers. The curves obtained will help in providing some dimensioning rules for the number of identifiers required for a given multicast group according to its traffic characteristics and a negotiated packet loss probability.