The growing complexity of cloud-native, edge, and IoT infrastructures has made manual configuration, fault remediation, and lifecycle management increasingly unsustainable. Traditional automation techniques—such as rule-based logic or bespoke machine learning pipelines—struggle with adaptability and explainability in dynamic environments. Recent advances in Large Language Models (LLMs), however, have introduced new opportunities for autonomous, intent-driven infrastructure control. In this work, we present a closed-loop framework that integrates LLM agents for automated Root Cause Analysis (RCA) and mitigation of faults within cloud-edge and IoT systems. When SLA violations are detected, the agent identifies likely root causes and selects corrective actions—such as pod rescheduling, scaling, or configuration updates—executed via a Model Context Protocol (MCP) server exposing management tool functionalities through an API. This RCA-plus-mitigation loop enables fault handling that is both explainable and adaptive. We evaluate our system on a cluster running synthetic IoT workloads under emulated stressors using a reproducible benchmarking setup. Results show that the agent identifies SLA violations with 52.9% accuracy and mitigates 70.7% of them successfully. Notably, the agent incorporates validation steps to ensure system stability after interventions. These findings highlight the feasibility of LLMs for real-time infrastructure healing and their potential role in future AIOps workflows.

Fully autonomous, zero-touch systems emphasizing on energy efficiency, high reliability, and ultra-low latency will be possible with the introduction of 6G networks. But with more devices and services, energy usage is expected to rise, necessitating sustainable solutions. A Decision Engine (DE) based on Hierarchical Reinforcement Learning (HRL) is presented in this research to improve the deployment of Service Function Chains (SFCs) based on Cloud-Native Functions (CNF) in dynamic contexts. The goal of the framework is to lower energy consumption while improving scalability and flexibility in the cloud, far-edge, and edge domains. By simulating actual 6G situations, we demonstrate that the HRL-based DE improves resource allocation, reduces latency by 80%, and considerably reduces energy usage by 60% compared to the flat RL. By assisting in self-optimizing network management, our method presents a viable route to intelligent, sustainable 6G networks.

In the realm of modern telecommunication ecosystems, resource-aware management is critical in ensuring quality of service (QoS) while enabling proactive, zero-touch operations. Furthermore, recent advances in software defined networking (SDN) have significantly contributed to beyond 5G (B5G) network optimization, offering innovative solutions to tackle their growing complexity. Moreover, the inherently non-convex nature of service provisioning challenges presents critical obstacles to delivering a seamless user experience. This paper introduces a novel framework that integrates Dueling Double Deep Q-Networks (Dueling DDQN) within a cloud-native edge infrastructure, enabling zero-touch service scaling. By harnessing the advanced capabilities of deep reinforcement learning (DRL), the proposed method autonomously identifies and addresses scaling demands, thereby improving overall network capabilities. The effectiveness of our approach is validated through experiments conducted on a multi-node Kubernetes testbed, demonstrating its ability to alleviate performance bottlenecks in distributed environments.

The emergence of the 6thgeneration (6G) of networks introduces new challenges in meeting users’ unparalleled demands for high performance and reliability. To meet these escalating requirements, the efficient orchestration of the network resources becomes essential. In this work, we propose a Spatio-Temporal Graph Neural Network (STGNN)-based approach for predicting the users’ resource demand targeting to enhance the resource management and in parallel to guarantee Service Level Agreement (SLA) compliance. Our approach mitigates key challenges in resource orchestration, such as under-provisioning, under-utilization, and user blocking by integrating relational and temporal information into the prediction process. The evaluation of the proposed framework incorporating these predictions demonstrates a relative reduction of up to 34% in SLA violations through proactive resource allocation. These results underscore the potential of STGNNs to enable intelligent resource management in next-generation mobile networks.

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.

In recent years, network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks. In such a situation, AI-driven zerotouch network automation should present a high degree of flexibility and viability, especially when deployed in live production networks. However, centralized controllers suffer from high data communication overhead due to the vast amount of user data, and most network slices are reluctant to share private data. In federated learning systems, selecting trustworthy clients to participate in training is critical for ensuring system performance and reliability. The present paper proposes a new approach to client selection by leveraging an XAI method to guarantee scalable and fast operation of federated learning based analytic engines that implement slice-level resource provisioning at the RAN-Edge in a non-IID scenario. Attributions from XAI are used to guide the selection of devices participating in training. This approach enhances network trustworthiness for users and addresses the black-box nature of neural network models. The simulations conducted outperformed the standard approach in terms of both convergence time and computational cost, while also demonstrating high scalability.

Although the next generations of wireless networks are anticipated to be enabled by Artificial Intelligence (AI), the development of practical, scalable, and efficient system models and network orchestration strategies remains a significant open challenge that requires thorough investigation. In this research work, we study a system model including two parts of AI-driven management and orchestration and AI-enabled infrastructures consisting of multiple automated domains. While the network orchestration manages the autonomous domains, the intra-domain resource allocation and network slicing are also automated and AI-enabled. Consequently, we propose an efficient and scalable strategy for automating network edge domains. This approach reduces the complexity and, thus, increases the scalability and energy efficiency by implementing one Double Deep Q-Learningbased controller in each domain that can perform network slicing for multiple service types. One agent provides dynamic network services by utilizing unsupervised discovered assignment skills in an offline phase. We implement the domain controller with multiple algorithms to find the best performance and efficiency. Finally, we compare the reliability of the algorithms, and we select the algorithm that offers the best trade-off between complexity and performance.

The 6thgeneration (6G) of mobile networks are anticipated to provide significant benefits such as high data-rates, low-latency and seamless connectivity to a huge number of users. The realization of these networking potentialities is anchored in the innovative design and development of the networking operations with purpose to deal with complex environments in a efficient manner. In particular, principal networking operations such as the resource management will be mainly conducted with the aid of the Deep Learning (DL) models. The DL models are characterized by their efficiency in dealing with intense tasks but they lack of transparency and trust. For this reason, we propose a framework that incorporates both a methodology for uncertainty quantification (UQ) of the DL models and a trafficengineering model targeting on improving the trustworthiness in the resource allocation procedures. It is shown that the exploitation of trustworthy DL models significantly improves both the system's resource utilization and the system's service provisioning capability to the users.

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.

This research introduces an advanced Explainable Artificial Intelligence (XAI) framework designed to elucidate the decision-making processes of Deep Reinforcement Learning (DRL) agents in O-RAN architectures. By offering networkoriented explanations, the proposed scheme addresses the critical challenge of understanding and optimizing the control actions of DRL agents for resource management and allocation. Traditional methods, both model-agnostic and model-specific approaches, fail to address the unique challenges presented by XAI in the dynamic and complex environment of RAN slicing. This paper transcends these limitations by incorporating intent-based action steering, allowing for precise embedding and configuration across various operational timescales. This is particularly evident in its integration with xAPP and rAPP sitting at near-real-time and non-real-time RIC, respectively, enhancing the system's adaptability and performance. Our findings demonstrate the framework's significant impact on improving Key Performance Indicator (KPI)-based rewards, facilitated by the ability to make informed multimodal decisions involving multiple control parameters by a DRL agent. Thus, our work marks a significant step forward in the practical application and effectiveness of XAI in optimizing O-RAN resource management strategies.

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.

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.

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks.

Cell- Free (CF) massive Multiple- Input- Multiple-Output (mMIMO) has recently gained significant research attention as a promising technology for future wireless networks. Since the conventional CF mMIMO has been considered unscalable and impractical, user-centric CF systems were proposed to improve its flexibility. While Access Point (AP) clustering has been studied in many research works as a challenging task in Radio Access Network (RAN), the limitations of the connecting links to the servers in a real scenario have not been taken into account. In this work, we consider the innovative combination of Open RAN (ORAN) and CF-RAN architecture that aims to improve the CF network limitations related to the connecting links. Then, we propose two control loops for ORAN Radio Unit (ORU) clustering and ORAN Distributed Unit (O-DU) assignment procedures that are conducted by Reinforcement Learning (RL) agents. Numerical results show that the proposed approach can successfully provide the user's requirements while the network is balanced.

This paper addresses how to handle slice resources for 6G networks at different time scales in an architecture based on an open radio access network (ORAN). The proposed solution includes artificial intelligence (AI) at the edge of the network and applies two control-level loops to obtain optimal performance compared to other techniques. The ORAN facilitates programmable network architectures to support such multi-time scale management using AI approaches. The proposed algorithms analyze the maximum utilization of resources from slice performance to take decisions at the inter-slice level. Inter-slice intelligent agents work at a non-real-time level to reconfigure resources within various slices. Further than meeting the slice requirements, the intra-slice objective must also include the minimization of maximum resource utilization. This enables smart utilization of the resources within each slice without affecting slice performance. Here, each xApp that is an intra-slice agent aims at meeting the optimal quality of service (QoS) of the users, but at the same time, some inter-slice objectives should be included to coordinate intra- and inter-slice agents. This is done without penalizing the main intra-slice objective. All intelligent agents use deep reinforcement learning (DRL) algorithms to meet their objectives. We have presented results for enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and massive machine type communication (mMTC) slice categories.

The advent of the Fifth Generation (5G) and Beyond (B5G) mobile communications has led to the emergence of novel vertical services, necessitating the reconfiguration of network architecture to address stringent Quality of Service (QoS) demands, while simultaneously supporting reliable Vehicle-to-everything (V2X) communications. In this paper, our primary focus lies in enhancing the QoS of V2X applications utilizing Open Radio Access Network (O-RAN) architecture. To this end, we propose a novel optimization framework that creates dynamic RAN slicing covering tailored functional split selection per slice, and Multi-access Edge Computing (MEC) server placement within O-RAN architecture. In addition, Packet Duplication (PD) techniques are used in the proposed work to enhance V2X application reliability. First, we define the proposed problem as convex and linear, and then we useBendersa distributed technique that accelerates performance while ensuring the global optimal solution. The approach is validated through simulations, which demonstrate its capability to meet the QoS requirements of V2X applications and outperform existing methods. According to the results, there is a trade-off between the achieved throughput and the extra overhead due to replicated packets which incurs extra resources such as radio and computation to the network. For example, our approach can reach up to 42% increases in the served demand at the expense of 6% more spectrum usage when compared to the literature.

The OPTIMIST project is positioned into one of the most challenging and societal impactful innovation paths, by contributing to the design and implementation of a Multi-access Edge Computing-empowered service platform enabling the optimized delivery of the most popular type of traffic in the forthcoming 5G networks: mobile video. Advanced prediction tools (e.g. data analytics), MEC-enabled service provisioning, 5G networking, QoE-driven and model-based service optimization using stochastic modeling and algorithmic optimization, are major components toward a holistic system that combines analytical tools and close-to-market technologies. In addition, despite the considerable amount of valuable academic research conducted in the area of MEC- enabled service provisioning, the vast majority of obtained results remains hardly compatible (in terms of system models and implementation platforms) and are often limited to the scope of academic research with no envisaged market transfer plans. OPTIMIST fosters fruitful collaboration of academia and industry targeting at developing an innovative platform for fully personalized delivery of 5G mobile video content.

Network Virtualization (NV) has proved a promising technology that allows multiple heterogeneous Virtual Networks (VNs) to operate simultaneously on the same infrastructure. Dynamic Virtual Network Embedding (NVE) has emerged as an enabler of elasticity and scalability in the VN deployment and resource allocation of the physical infrastructure. However, the key challenge in realizing NV in a sustainable way is how to dynamically embed VNs efficiently into physical network, which is defined as the VNE problem. To address this challenge, this paper proposes an approach that leverages Multi-Agent Reinforcement Learning (MARL) to solve the dynamic VNE of VNs for 5G/6G communication systems. The proposed framework consists of multiple horizontally distributed RL agents that co-operate to devise temporally dynamic VNE placements. The key contributions of this work are introducing a novel dynamic VNE orchestration framework for multi-domain networks based on Distributed RL, providing a scalable VNE framework targeted to Ultra-Reliable Low-Latency Communication (URLLC) services, evaluating and comparing the proposed algorithm with existing solutions in the state of the art. The paper concludes that there is a significant improvement in latency of 144.191% when compared to the baselines.

As commercial 5G networks become commercially available and 6G looms in the horizon, the adoption of this new technologies depends on the way current and yet-to-come vertical industries put them to use. This accelerates the process of adoption by the end users, which are the final consumers of these technologies. 5G networks have multiple use cases associated with its new features, being the Ultra-Reliable Low Latency Communications (URLLC) use case one of the most instrumental for verticals such as remote teleoperation, factory automation and autonomous driving. In this paper, we design a general purpose and low-cost end-to-end (E2E) 5G/Beyond-5G Experimental Platform for URLLC applications, supporting Platform-as-a-Service (PaaS) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities for online data analytics and automated decision making. The experimental evaluation of our platform demonstrates an average one-way latency on the DL and UL as low as 4.1 ms and 6.60 ms, respectively, 13.8 ms of end-to-end latency (E2EL), and a 31.7 ms E2EL between UEs for data frames of a teleoperation Web App with video feedback, demonstrating the capability of our platform in relation to other state-of-the-art testbeds for URLLC applications.

Swarm learning (SL) exploits the blockchain to realize a federated and decentralized learning, which is very suitable for internet of things (IoT). Different from FL using central server to update global parameter, SL using edge node (header) to do that. However, poisoning attack is also an unresolved problem to SL. Because if header is malicious, it can pollute global parameter more easily than edge nodes. Moreover, there are following important limitations in existing defense schemes for FL, which cannot be used in SL directly. First, existing defense schemes focus on building a whitelist, which obstructs the decentralization because it can just provide decentralization in honest nodes instead of all of nodes. Second, existing schemes just consider poisoning attacks from edge nodes, they cannot defend attacks from header. Third, most existing schemes will let server execute the defense algorithm, but in SL, malicious header can return wrong defense results to deceive managers. To address above challenges, in this paper, we propose a protection system that leverages the concept of zero-trust architecture for SL, which achieves continuous risk calculation, analysis of learning behavior and abnormal parameter detection based on Manhattan distance and accuracy difference of parameters. We also evaluate the performance in the presence of random and customized malicious edge nodes. Experimental results demonstrate that our scheme can achieve higher accuracy than the other existing schemes.

The evolution of the telecommunication networks towards their 6th generation has emerged new research directions in order to deal with the challenges that are posed by the high-complexity of the new infrastructure. In this new era, the structuring of the network management infrastructure is a complex endeavor that should efficiently control both communication and computational resources. In this paper, we present a set of strategies for managing the computational load in a cell-free-based, converged optical-wireless 6G network. The proposed approaches target to control the computational load of a cell-free network, by either compressing the load by considering load-thresholds, or offloading the load to the SDN controller of the fixed network. Both strategies are mathematically formulated by considering traffic-engineering formulas, while their performance is evaluated by comparing analytical results with corresponding results from a baseline scenario, where no load control strategies are applied. Moreover, the proposed analysis can be applied in order to determine the capacity of the network controllers that is required in order to guarantee pre-determined Quality of Service requirements.

In recent years, wireless networks are evolving complex, which upsurges the use of zero-touch artificial intelligence (AI)-driven network automation within the telecommunication industry. In particular, network slicing, the most promising technology beyond 5G, would embrace AI models to manage the complex communication network. Besides, it is also essential to build the trustworthiness of the AI black boxes in actual deployment when AI makes complex resource management and anomaly detection. Inspired by closed-loop automation and Explainable Artificial intelligence (XAI), we design an Explainable Federated deep learning (FDL) model to predict per-slice RAN dropped traffic probability while jointly considering the sensitivity and explainability-aware metrics as constraints in such non- IID setup. In precise, we quantitatively validate the faithfulness of the explanations via the so-called attribution-based log-odds metric that is included as a constraint in the run-time FL optimization task. Simulation results confirm its superiority over an unconstrained integrated-gradient (IG) post-hoc FDL baseline.

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.

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.

The industrial Internet of Things (IoT) system is integrated with the emerging artificial intelligence (AI) paradigms to empower industrial automation and self-evolving capabilities. AI-driven resource allocation across cyber-physical domains for mobile industrial IoT must consider its fundamental requirements and key characteristics such as high reliability, low latency, and environmental dynamics. The challenge is twofold. Industrial systems are fault-sensitive, which makes them intolerable of trial-and-error-based learning and optimization approaches. In addition, learning models cannot adapt to changing industrial IoT environment with dynamic communication noise and machinery disturbances. In this paper, we propose joint optimization for the nonorthogonal multiple access (NOMA) and multi-tier hybrid cloud-edge computing empowered industrial IoT that results in improved utilization of communication and computing resources. Second, we establish the fine-grained digital twin for industrial IoT (DT-IIoT) to simulate the changing industrial environment to support trial-and-error-based safe learning. Third, we leverage meta reinforcement learning (meta RL) to improve the generalization and fast adaptation of the learning models for DT-IIoT. Finally, the feasibility and efficiency of these schemes are evaluated through extensive experiments.

The evolution of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in the telecommunications industry have intensified the issues of network management at large scales. Dynamic service orchestration and adaptive resource allocation became a necessity for network operators to manage the rapid growth of users and data-intensive applications. The impact of network automation on energy consumption and overall operating costs is often overlooked. Guaranteeing strict performance constraints of Ultra-Reliable Low Latency Communication (URLLC) services while enhancing energy efficiency is one of the major critical problems of future communication networks, given the urgency to reduce carbon emissions and energy consumption. In this work, we study the problem of zero-touch Service Function Chain (SFC) orchestration for multi-domain networks, targeting the latency reduction of URLLC services while improving energy efficiency for beyond-5G networks. Specifically, we propose SCHE2MA, a Service CHain Energy-Efficient Management framework based on distributed Reinforcement Learning (RL), that can intelligently deploy SFCs with shared VNFs per se into a multi-domain network. Finally, we evaluate SCHE2MA through model validation and simulation while demonstrating its ability to jointly reduce average service latency by 103.4% and energy consumption by 17.1% compared to a centralized RL solution.

The success of Software-Defined Networking and Network Function Virtualization enabled providers to partially automate the decision-making of various network operating workflows. Network slicing is an innate feature of the Fifth Generation (5G) and Beyond-5G (B5G) networks, bringing many advantages but also increasing the complexity of managing such networks. In this context, automation tools based on Artificial Intelligence (AI) have become a preeminent instrument of automation used by the industry, as it would be impossible to manually provision and manage such a vast and dynamic infrastructure. Safe interaction of the AI agents with the network is one of the predominant challenges, especially when Reinforcement Learning (RL) is used in critical environments. This is particularly important when the RL agent actions have a high or irreversible impact on the network or service. Slice management is one of the major features that operators want to automate, to offer future services at large scales with manageable complexity to the operator. However, during the exploration phase, RL agents can cause significant performance degradation during operation and possibly introduce irreversible damage to the service being offered. To address this major challenge, we propose a multi-agent, modular, SafeRL architecture for distributed slice orchestration. We study the problem of zero-touch slice management and orchestration, in the context of Ultra-Reliable Low Latency Communication services for B5G networks. Our results demonstrate improved performance over competing solutions, while ensuring the safety of the performed actions during real-time slice orchestration.

With the evolution of 5G networks towards fully software, Network Slicing (NS) has emerged as a new paradigm to create a set of customised logical network instances on top of the same physical infrastructure. Telecom operators provide “slices” of their networks to different industries (e.g., automotive, Industrial Internet of Things (IIoT), and eHealth), targeting to increase the resource utilization and Return on Investment (ROI). However, as the service demand increases, slice management becomes particularly complex, especially in the Radio Access Network (RAN) section, since virtualized infrastructures are significantly more complex to manage compared to physical networks. In this paper, a novel 5G New Radio (5G NR) NS management framework is presented, which targets to define the optimal NS parameterization for the entire Fronthaul (FH) 5G network. Our solution is divided into three phases: i) definition of the 5G Downlink Hybrid Automatic Repeat Request (DL-HARQ) parameters (K0, K1), ii) the dynamic (inter-slice) partitioning of the radio resources among the slices, inline with the Service Level Agreement (SLA), and, iii) an intra-slice scheduling algorithm for the user prioritization, given real-time user's traffic Key Performance Indicators (KPIs) analysis. To the best of our knowledge, the proposed solution is the first attempt of a real-time NS management framework for the complete FH section, involving frame granularity service customization.

eration (6G)-enabled massive network slicing is a strong enabler for the expected pervasive digitalization of the vertical market. In such a context, artificial intelligence (AI)-driven zero-touch network automation should present a high degree of scalability and sustainability, especially when deployed in live production networks wherein the collected monitoring datasets at different points are non-independent and identically distributed (non-IID). This paper presents a new cloud-native service-level agreement (SLA)-driven stochastic policy to guarantee a scalable and fast operation of constrained federated learning (FL)-based analytic engines that perform statistical slice- level resource provisioning at RAN-Edge in a non-IID setup. Both simulated and cloud-native emulated scenarios are implemented to demonstrate the superiority of the solution in reducing SLA violation, convergence time and computation cost compared to different FL baselines, showcasing thereby a higher scalability.

Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home’s electricity cost and the residents’ thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.

Service providers highly rely on network softwarization for addressing the demanding use cases of the 5G verticals. In order for the 5G vertical scenarios to be seamlessly executed, where the coverage should be provided in large areas, the required infrastructure capacity and availability can only be supported by cross-operator cooperation. In this context, we employ a novel federated core-edge 5G architecture, where 5G vertical services are represented as service function chains (SFCs) and can be offered on the infrastructure owned either by the local operator or a foreign one. Furthermore, we propose two online SFC placement methods that aim to efficiently place the SFCs across the architecture, taking into account the deployment cost. A simulation of the optimal solution, based on an integer linear programming (ILP) approach, is evaluated against a hop-based heuristic placement method that runs on our experimental 5G platform. Our experimental results demonstrate that the optimized placement produces the most cost-effective solution. Our heuristic algorithm introduces a near-optimal solution, but with a higher deployment cost. Compared with the ILP approach, the heuristic solution provides lower complexity and execution time. Finally, in order to further augment the placement techniques, we propose enhanced lifecycle management methods that use live migration and scaling actions to further decrease the execution cost and adapt to real-time incoming traffic, respectively.

A key enabler for both scalability and sustainability in beyond 5G (B5G) network slicing consists on minimizing the exchange of raw monitoring data across different domains. This is achieved by bringing the analysis functions closer to the data collection points. To this end, we introduce in this paperstatistical federated learning(SFL) provisioning models that can learn over a live network non independent identically distributed (non-IID) datasets in an offline fashion while respecting slice-level service level agreement (SLA) long-term statistical constraints. Specifically, we consider three resource SLA metrics, namely,cumulative distribution function(CDF),$Q$-th percentileandmaximum/minimum bounds. These metrics are dataset-dependent and non-convex non-differentiable and, to sidestep the inaccuracy of settling only for surrogates, we propose a novel formulation that jointly considers the statistical objective and constraints as well as their smooth approximation using theproxy-Lagrangianframework, which we solve via a non-zero sum two-player game strategy. Numerical results on various slice-level resources show that SFL enables SLA enforcement while significantly reducing the overhead compared to both state-of-the-art FedAvg and centralized constrained deep learning schemes. Finally, we provide an analysis for the lower bound of the so-calledreliable convergence probabilityin the SFL setup.

Scalability and sustainability are the corner stones to unleash the potential of beyond fifth-generation (B5G) ultra-dense networks that are expected to handle massive and heterogeneous services. This implies that the transport of the underlying raw monitoring data should be minimized across the network, and urges to bring the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying users distributions, traffic profiles, and channel conditions. This makes the data collected across different points non independent and identically distributed (non-IID), which is challenging for FL tasks. To cope with this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This entropy is calculated using a clustering scheme based on a similarity matrix defined over both the features and the supervised output spaces, and is targeting classification as well as regression tasks. The FL aggregation server then uses the reported dataset entropies to devise i) an entropy-based federated averaging scheme, and ii) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to illustrate all these advantages.

As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.

Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.

In this paper, we address the concept of dynamic resource allocation for radio access network (RAN) slicing in beyond 5G (B5G) systems under service-level agreement (SLA). Using live network distributed key performance indicators (KPIs) mini-datasets, we introduce a new class of federated learning models that can capture the long-term cumulative distribution function (CDF) statistic—is usually used to define SLA—and enforce some preset constraints on it. Given that the CDF is also dataset-dependent and non-convex non-differentiable, we formulate the corresponding local optimization task using the proxy-Lagrangian framework and solve it via a non-zero sum two-player game strategy. Numerical results show that the proposed decentralized resource allocation approach enables SLA enforcement and significantly reduces the SLA violation rate for various slice-level KPIs.

The standard for 5G communication exploits the concept of a network slice, defined as a virtualized subset of the physical resources of the 5G communication infrastructure. As a large number of network slices is deployed over a 5G network, it is necessary to determine the physical resource demand of each network slice, and how it varies over time. This serves to increase the resource efficiency of the infrastructure without degrading network slice performance. Traffic prediction is a common approach to determine this resource demand.State-of-the-art research has demonstrated the effectiveness of machine learning (ML) predictors for traffic prediction in 5G networks. In this context, however, the problem is not only the accuracy of the predictor, but also the usability of the predicted values to drive resource orchestration and scheduling mechanisms, used for resource utilization optimization while ensuring performance. In this paper, we introduce a new approach that consists on including problem domain knowledge relevant to 5G as regularization terms in the loss function used to train different state-of-the-art deep neural network (DNN) architectures for traffic prediction. Our formulation is agnostic to the technological domain, and it can obtain an improvement of up to 61,3% for traffic prediction at the base station level with respect to other widely used loss functions (MSE).

To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic innovation and artificial intelligence (AI) in KB5G. We invoke a continuous model-free deep reinforcement learning (DRL) method to minimize energy consumption and virtual network function (VNF) instantiation cost. We present a novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU) to learn continuously to accumulate the knowledge learned in the past to minimize future NS costs. Finally, we present numerical results to showcase the gain of the adopted approach and verify the performance in terms of energy consumption, CPU utilization, and time efficiency.

NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide-area networks suitable for implementing massive machine-type communications. NB-IoT features include, e.g., extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput, and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly underestimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of the radio resource control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base station. Using two different commercial off the shelf NB-IoT boards and two mobile network operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of the RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors of our proposed model vary between 0.33% and 15.38%.

Future mobile networks will offer high data rates based on high-capacity fronthaul. Current fronthaul design has two main components that communicate via the common public radio interface and fiber links, i.e., remote units (RUs) that implement simple signal processing and centralized baseband units (CBBUs) in high power-consuming data centers that perform complex network functions. Various functional splits between CBBUs and RUs are feasible, inducing trade-offs between centralization gains and bandwidth demands. This design lacks in capacity and flexibility, motivating the use of converged fiber-wireless (Fi-Wi) fronthaul with high-bandwidth fiber and millimeter-wave links, and splits that move functionalities to RUs reducing the delay demands. Further flexibility is offered by analog radio-over-fiber fronthaul that supports dynamic functional splitting via software-defined networking (SDN). Ensuring acceptable delay for all RUs, i.e., minimizing fronthaul grade-of-service (GoS), requires selection of CBBUs, channel bandwidth and functional splits of RUs. The split type affects fronthaul power consumption determining which fronthaul components are active and their processing power. Using a simulated annealing-based dynamic fronthaul resource allocation (DFRA) scheme, we jointly optimize GoS and power consumption in a novel SDN Fi-Wi fronthaul. Our results show that DFRA minimizes GoS and power consumption for all load levels outperforming baseline approaches.

Telemedicine, or the ability granted to doctors to remotely assist patients has been greatly benefited by advances in IoT, network communications, Machine Learning and Edge/Cloud computing. With the impeding arrival of 5G, virtualized infrastructures and cloud-native approaches enable the execution of unprecedented procedures during such patient/doctor interactions, allowing medical professionals to e.g. request higher granularity metrics from patients' telemetry equipment, or perform on-demand data mining/processing of patient's stored data in order to provide a more educated diagnostic or prediction. In this work we coalesce the virtues of virtualized infrastructures and IoT into a solution able to satisfy increasing data processing demands for eHealth, e.g. for telemedicine applications, remote assistance or patient pre-screening procedures. The proposed platform, CURATE, leverages Network Functions Virtualisation Management and Orchestration (NFV MANO) for the on-demand instantiation of the required virtual resources on the operator's infrastructure, as well as the concept of 5G Network Slices to guarantee efficient resource allocation and tenant isolation. Results show the proposed platform is able to efficiently make use of the available hardware resources via Network Slices, as well as provide cost-effective service guarantees employing dynamic scaling operations.

As technology progresses, cars can not only be considered as a transportation medium but also as an intelligent part of the cellular network that generates highly valuable data and offers both entertainment and security services to the passengers. Therefore, forthcoming 5G networks are said to enhance Ultra-Reliable Ultra-Low-Latency that will allow for a new breed of services that will disrupt the industry as we know it today. In this work, we devise a unique fusion of Deep Learning based mobility prediction and Genetic Algorithm assisted service orchestration to retain the average service latency minimal by offering personalized service migration, while tightly packing as many services as possible in the edge of the network, for maximizing resource utilization. Through an extensive simulation based on real data, we evaluate the proposed mobility orchestration combination and we find gains in low latency in all examined scenarios.

The increasing demand for fast, reliable, and robust network services has driven the telecommunications industry to design novel network architectures that employ Network Functions Virtualization and Software Defined Networking. Despite the advancements in cellular networks, there is a need for an automatic, self-adapting orchestrating mechanism that can manage the placement of resources. Deep Reinforcement Learning can perform such tasks dynamically, without any prior knowledge. In this work, we leverage a Deep Deterministic Policy Gradient Reinforcement Learning algorithm, to fully automate the Virtual Network Functions deployment process between edge and cloud network nodes. We evaluate the performance of our implementation and compare it with alternative solutions to prove its superiority while demonstrating results that pave the way for Experiential Network Intelligence and fully automated, Zero touch network Service Management.

Network Slicing (NS) represents a key technology enabler for advanced connectivity and data processing tailored to customers' specific requirements. While significant progress has already been achieved for Core NS, Radio Access Network (RAN) slicing still presents limitations in terms of sharing infrastructure, Service Level Agreement (SLA) guarantees, isolation, resource scheduling and allocation. In this context, this paper firstly introduces a novel slices configuration framework for the 5G New Radio (5G NR) infrastructure able to dynamically migrates the radio resources among the slices, while preserving the Quality of Service (QoS) of the served users. Our solution is illustrated in detail and tested on top of a real case 5G scenario, using a software-based simulator. Finally, this paper investigates the flexibility, scalability, and real-time properties of the proposed method, as required in the future 5G cloud-based architectures.

Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-conFigure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.

In this paper, we investigate the concept of OPEX-limited resource provisioning as a key component in fifth generation (5G) radio access networks (RAN) slicing. The different RAN slices' tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources and backhaul capacity. To achieve this dynamic resource allocation, we rely on key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes. These datasets are used to train a new class of deep neural networks (DNNs) models where OPEX requirements, formulated as non-convex non-differentiable violation rate constraints, are also dataset-dependent. The designed constrained DNNs are then optimized via a non-zero sum two-player game strategy. In this respect, we highlight the effect of the different hyperparameters on the respect of the OPEX limitations, while ensuring a dynamic RAN resource orchestration that follows the slices' traffics trends.

The upcoming fifth generation (5G) mobile communications urge software-defined networks (SDNs) and network function virtualization (NFV) to join forces with the multiaccess edge computing (MEC) cause. Thus, reduced latency and increased capacity at the edge of the network can be achieved, to satisfy the requirements of the Internet of Things (IoT) ecosystem. If not properly orchestrated, the flexibility of the virtual network functions (VNFs) incorporation, in terms of deployment and lifecycle management, may cause serious issues in the NFV scheme. As the service level agreements (SLAs) of the 5G applications compete in an environment with traffic variations and VNF placement options with diverse computing or networking resources, an online placement approach is needed. In this article, we discuss the VNF lifecycle management challenges that arise from such heterogeneous architecture, in terms of VNF onboarding and scheduling. In particular, we enhance the intelligence of the NFV orchestrator (NFVO) by providing: 1) a latency-based embedding mechanism, where the VNFs are initially allocated to the appropriate tier and 2) an online scheduling algorithm, where the VNFs are instantiated, scaled, migrated, and destroyed based on the actual traffic. Finally, we design and implement an MEC-enabled 5G platform to evaluate our proposed mechanisms in real-life scenarios. The experimental results demonstrate that our proposed scheme maximizes the number of served users in the system by taking advantage of the online allocation of edge and core resources, without violating the application SLAs.

In this paper, we address the issue of resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV)-based fifth generation (5G) networks. The different slices’ tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices’ traffics predictor based on a soft gated recurrent unit (GRU). We then build—at each virtual network function—joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely,violation rate-based SLA andresource bounds-based SLA. This is achieved by integrating dataset-dependent generalized non-convex constraints into the DNN offline optimization tasks that are solved via a non-zero sum two-player game strategy. In this respect, we highlight the role of the underlying hyperparameters in the trade-off between overprovisioning and slices’ isolation. Finally, using reliability theory, we provide a closed-form analysis for the lower bound of the so-calledreliable convergence probabilityand showcase the effect of the violation rate on it.

In this work, we analytically study the performance of an in-band and self-fronthauling millimeter-wave Cloud-Radio Access Network (C-RAN). By considering a stochastic-geometry approach for the modeling of the position and number of Baseband Units (BBUs), Remote Radio Heads (RRHs), and mobile terminals (MTs), we provide the following three-fold contribution: i) We derive an analytical framework for the MT rate distribution for two types of wireless RRHs, namely half-duplex (HD) and full-duplex (FD); ii) Based on the derived framework, we prove that the maximum performance gain of the FD network over its HD counterpart is achieved for a substantially higher density of the wireless RRHs compared to the fiber-connected ones and an adequately small self-interference power level; iii) Finally, we compute an analytical expression of the total cost required to increase the density of the fiber-connected RRHs in a city that showcases the tradeoff between their density increase and the incurred cost. The aforementioned system-level trends are validated by means of Monte Carlo simulations.

The papers in this special section examine artificial intelligence (AI)-driven developments in 5G mobile communications for industrial automation applications from a Big Data perspective. With the recent advances in information and communication technologies, industrial automation is expanding at a rapid pace. This transition is characterized by “Industry 4.0”, the fourth revolution in the field of manufacturing. Industry 4.0, also called as “Industrial Internet of Things (IIoT)” or “Smart Factories”, is a reflection of new industrial revolution that is not only interconnected, but also communicate, analyze, and use the information to create a more holistic and better connected ecosystem for the industries.

As industries are under pressure for shorter business and product life cycles, there is an extensive effort from the research community for novel and profitable automation processes. This effort has given rise to the fifth-generation (5G) Tactile Internet, which is characterized by extremely low latency communication in combination with high availability, reliability, and security. In this paper, we discuss the key technologies to support the Tactile Internet characteristics in industrial environments and then showcase the implementation of a novel 5G network function virtualization enabled experimental platform. Given that ultra-reliable low-latency communication is crucial for the manufacturing process, we demonstrate that, in our setup, submillisecond end-to-end communication is attainable, proving the suitability of our platform for Tactile Internet industrial applications.

Day-by-day modern circular economy (CE) models gain ground and penetrate the traditional business sectors. The Internet of Medical Things (IoMT) is the main enabler for this interplay of CE with healthcare. Novel services, like remote sensing, assisting of elder people, and e-visit, enhance the people's health and convenience, while reducing the per-patient cost for the medical institutions. However, the rise of mobile, wearable, and telemedicine solutions means that security can no longer be examined within the neat, physical walls as it was considered before. The problem for a healthcare system further increases as the Bring Your Own Device (BYOD) reality, affects the way that the health services are accommodated nowadays. Both patients and healthcare staff utilize their personal devices (e.g. smart phones or tablets) in order to access, deliver, and process medical data. As the IoMT is materialized and the underlying devices maintain so valuable data, they become a popular target for ransomware and other attacks. In the CE case, the problem is further emerging as several of these assets can be used over-and-over by many actuators. However, medical users and vendors are less aware of the underlying vulnerabilities and spend less on the IoMT security. Nevertheless, the risk from exploiting vulnerabilities can be drastically reduced when the known and relevant controls are placed. This paper presents an overview of the core security and privacy controls that must be deployed in modern IoMT settings in order to safeguard the involved users and stakeholders. The overall approach can be considered as a best-practices guide towards the safe implementation of IoMT systems, featuring CE.

5G New Radio's (NR) spectrum expansion towards higher bands, although critical towards achieving the envisioned 5G capacity requirements, creates the need for installing a very large number of Access Points (APs), which asserts tremendous capital burden on the Mobile Network Operators. Current centralization solutions such as the Cloud Radio Access Network (C-RAN) alleviate partially the costs of densification by moving the majority of radio processing functionalities from the Remote Radio Heads (RRHs) to the central Base Band Unit (BBU), but still require very high-speed Point-to-Point links between the BBU and each RRH mainly due to the digitized Common Public Radio Interface (CPRI) that is excessively inefficient for hauling broadband signals. In this article, we present a novel architecture that employs an analog converged Fiber-Wireless scheme in order to create a very spectrally efficient Point-to-Multipoint network capable of interconnecting a large number of APs, while allowing compatibility with mature Ethernet-based low-cost equipment. Preliminary simulation results show very low end-to-end Ethernet packet delay, well below eCPRI's 100 μs mark, even for fiber lengths up to 10 km, indicating the suitability of our solution for employment in 5G NR large-scale fronthaul networks.

The 5G networks are expected to satisfy diverse use cases and business models with significant advancements in terms of capacity, reliability, and latency. The allocation and provisioning of network resources pose a challenge for this novel architecture to guarantee higher flexibility and quality of service. As a potential enabler, network slicing was proposed as an innovative approach for the control of the network resources. Although a static slicing approach can be suitable for the transport and core network, the stochastic behavior of the wireless channel requires fast and secure slicing techniques for resource allocation. In this paper, we propose a dynamic slicing approach for the radio access network, where the network resources are carefully assigned to guarantee the service level agreements and increase the number of served users. To prove the performance of our approach, we implemented a fronthaul testbed to emphasize the strength of our method in terms of throughput and resource utilization, compared to static slicing.

Advances in virtualisation technology have reached the mobile networking domain. Network Functions Virtualisation Management and Orchestration (NFV MANO) as proposed by ETSI and realised via Opensource MANO (OSM) allows sharing or partitioning a fairly generic pool of hardware into virtual compute, network and storage resources among differentiated services. A direct consequence of this is a dramatic reduction in CAPEX/OPEX, but also the possibility of instantaneously deploy network services across one or several Mobile Network Operators' (MNO) infrastructure. This work demonstrates how the upcoming fifth generation (5G) of mobile communications envisions NFV MANO for instantiating network services, including Core Network components, and wireless SDN controllers for enforcing end-to-end QoS policies via network slices that span from the radio access segment to the backend packet network.

Fifth Generation (5G) Cloud-Radio Access Networks (C-RANs) are about to exploit both optical and Millimeter Wave (mmWave) technology to meet the ever-increasing traffic demands. In this new type of converged Fiber-Wireless (FiWi) systems efficient Medium Transparent-Medium Access Control (MT-MAC) protocols should be designed, able to satisfy the very strict 5G service requirements. To this end, in this paper, we propose an MT-MAC protocol for mmWave Analog Radio-over-Fiber (A-RoF) C-RANs, which employs gated service, according to which users are granted transmission windows equal to the number of bytes contained in their buffer. An analytical model is also proposed for the mean packet delay, which is verified by means of simulation for different fiber length values, network load conditions and optical capacity values. Our results not only prove the accuracy of the proposed model but also the suitability of the proposed MT-MAC protocol to meet the sub-ms delay challenge of latency-critical 5G network requirements.

The unprecedented surge in mobile data traffic, along with the wide range of services and the corresponding different performance requirements, has raised the need for a new mobile network architecture. In that sense, 5G has been conceived as a software defined network able to provide service-tailored connectivity. Network slicing is a key mechanism to serve efficiently the diversified service requirements. In this paper, we formulate the joint RAN slicing and functional split with optimization of centralization degree (CD) and throughput. Our results show that even though in terms of CD we have more costs, we can better meet the service requirements and also have more throughput in the network.

Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to guarantee a fast radio resource management in both uplink (UL) and downlink (DL), while tackling the corresponding propagation imbalance that may arise in blockage situations. In this context, we introduce a semi-blind UL/DL decoupling concept where, after its initial activation, the central processing unit (CPU) gathers measurements of the Rician K-factor-reflecting the line-of-sight (LOS) condition of the user equipment (UE)-as well as the DL reference signal receive power (RSRP) for both 2.6 GHz and 28 GHz frequency bands, and then train a non-linear support vector machine (SVM) algorithm. The CPU finally stops the measurements of mmWave definitely, and apply the trained SVM algorithm on the 2.6 GHz data to blindly predict the target frequencies and access points (APs) that can be independently used for the UL and DL. The accuracy score of the proposed classifier reaches 95% for few training samples.

In this paper, we address the issue of automatic tracking areas (TAs) planning in fifth generation (5G) ultra-dense networks (UDNs). By invoking handover (HO) attempts and measurement reports (MRs) statistics of a 4G live network, we first introduce a new kernel function mapping HO attempts, MRs and inter-site distances (ISDs) into the so-called similarity weight. The corresponding matrix is then fed to a self-tuning spectral clustering (STSC) algorithm to automatically define the TAs number and borders. After evaluating its performance in terms of the Q-metric as well as the silhouette score for various kernel parameters, we show that the clustering scheme yields a significant reduction of tracking area updates and average paging requests per TA; optimizing thereby network resources.

We present a novel model for the optimal design and power management of a group of buildings with diverse load patterns that are able to exchange energy through a common dc bus. The determination of the optimal sizes of the photovoltaic arrays, energy storage systems and inverters, and the optimal scheduling of power exchanges are achieved through the formulation of a mixed integer linear programming problem. Furthermore, the Nash bargaining method is used in order to fairly distribute the cooperation profits among the participants. The proposed approach achieves the reduction of the microgrid's (MG's) cost and carbon emissions compared to noncooperative approaches, and promotes the enhancement of the MG's energy sufficiency by allowing energy exchanges among buildings with energy surplus and buildings with energy deficit. Our model also takes into account the case where additional buildings join the MG after the initial coalition establishment.

The upcoming generation of mobile networks is expected to serve numerous mobile users with high quality-of-service (QoS) demands, requiring high-capacity fronthaul. As the provision of fiber connections directly to the end users is not cost-efficient, the integrated fiber wireless (FiWi) fronthaul design based on wireless networking and passive optical networks (PONs) has been proposed. The FiWi design involves modern networking technologies that can accommodate the need for data rates in the Gb/s scale and low delay, such as the wavelength division multiplexing (WDM) in the optical domain and the multiple input multiple output (MIMO) communication over millimeter wave (mmWave) spectrum in the wireless domain. The co-existence of two network types requires resource management in a medium transparent manner, i.e., the sharing of the bandwidth in the wireless domain should allow the organization of the data packets in optical frames. As the traffic circulating in the FiWi fronthaul involves packets of different priorities, i.e., different QoS classes, the resource management scheme should support QoS differentiation. To this end, we propose a resource management scheme for FiWi fronthaul and we extensively study its performance in terms of experienced delay and throughput. Our simulation results demonstrate that the proposed scheme significantly reduces the delay of the high priority class.

Despite all the benefits 60 GHz networks bring about, such as high network bandwidth, effective data rates, etc., one of its main application scenarios, Line-of- Sight (LOS) communications, still has troubles in actual indoor environments due to its high directionality. Traditional beam training methods are inaccurate and time-wasting, leading to unstable and inefficient wireless networks. Therefore, in this paper, we attempt to address this problem from a new aspect, i.e., assisting the signal adaptation with human mobility prediction. A state-of-the-art long short-term memory (LSTM) model is adopted to analyze the past trajectories and predict the future position, which can serve as an important reference for the transmitters to proactively adjust their beams and provide seamless coverage. In addition, we also design an algorithm to optimize the beam selection problem and improve the network quality. To the best of our knowledge, this is the first work in the field to use deep learning models for the beam selection problem. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.

In Software Defined Networks (SDN) a Controller installs forwarding rules or queries the status of network devices through a separate control channel using the OpenFlow protocol. Such queries may ask for instantaneous metrics, like port status, packet error rate, or transmitted packets. In turn, the Controller uses these metrics, or context, to condition forwarding rules. As network conditions change over time, forwarding rules are set to expire if not used (or matched) during a configurable idle timeout. Consequently, they are erased from the flow table, forcing a query to the Controller every time a packet for which there is no forwarding rule arrives at the network device. This work presents experimental results showing the delay increase produced by timing out forwarding rules of periodic flows, such as the generated by sensors in IoT deployments. Furthermore, it proposes and experimentally evaluates a pro-active forwarding rule installation mechanism able to reduce such delay by 90%, easing the implementation of context-aware forwarding strategies for IoT.

Nowadays, over-the-top (OTT) applications can be accessed via Internet connections over cellular networks. Mobile network operators (MNOs) strive to accommodate mobile traffic through energy efficient shared networks. The OTT service providers (OSPs) need to interact with MNOs and require resources for offering desired quality- of-service (QoS) levels to users of different categories, with different QoS requirements, which imply prioritization of certain OTT flows. Resource scheduling should respect network neutrality, which forbids OSP prioritization. Furthermore, OSPs request resources periodically, according to their performance goals, causing delay in flows' accommodation due to i) the time required for information exchange between OSPs and MNOs, affected by network congestion, and ii) the time required for flows to receive resources, affected by the number of active flows. The intervention of OSPs may induce additional energy cost for the MNOs and affect the mobile network energy efficiency. Acknowledging the lack of OSP- oriented resource management approaches, we introduce a matching theoretic flow prioritization (MTFP) algorithm and investigate delay and energy efficiency through extensive simulations. Our study shows that MTFP improves both metrics comparing to the best effort approach, whereas its performance is affected by the OTT traffic level and the frequency of resource allocation process.

The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the widespread use of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a cooperative nonparametric statistical framework, namely COPS, for the mitigation of user misbehavior in network coding scenarios. Given that the behavior of adversaries cannot be characterized by certain probability distributions, the proposed scheme exploits two well-known nonparametric statistical methods, i.e., Kruskal-Wallis analysis and Conover-Iman multiple comparisons, for the detection and identification, respectively, of malicious users in the network. It is worth noting that the COPS framework does not require monitoring of the wireless channel and additional overhead, as its operation is based on the processing of the existing control packets. We assess the performance of the proposed scheme in various scenarios, showing that COPS is able to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.

Modern over-the-top (OTT) applications can be accessed via Internet connections over cellular networks, possibly shared and managed by multiple mobile network operators (MNOs). The OTT service providers (OSPs) need to interact with MNOs, requesting resources for serving users of different categories and with different quality-of-service requirements. For this purpose, OSPs need OTT application flow prioritization in resource allocation, while the network resource scheduling should respect network neutrality that forbids OSP prioritization. OSPs also need to request resources periodically, according to their performance goals, i.e., grade-of-service (GoS) level (blocking probability), causing delay in flows' accommodation due to: 1) the time required for information exchange between OSPs and MNOs, affected by network congestion, and 2) the time required for flows to receive resources, affected by the number of concurrently active flows. Acknowledging the lack of OSP-oriented resource management approaches, we: 1) introduce a novel matching theoretic flow prioritization (MTFP) algorithm that respects network neutrality and 2) design analytical models that enable the thorough investigation of the GoS and delay performance in various scenarios. Our results (analytical and simulation) show that MTFP improves both metrics compared to the best effort approach, whereas its performance is affected by the number of flows and the resource allocation frequency.

Emerging 5G communication paradigms, such as machine-type communication, have triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. Ensuring a reliable network operation under fading conditions is not straightforward, as the transmission schemes and the network topology, i.e., uniform or clustered deployments, affect the performance and should be taken into account. Moreover, as the number of nodes increases, exploiting natural energy sources and wireless energy harvesting (WEH) could be the key to the elimination of maintenance costs while also boosting immensely the network lifetime. In this way, zero-energy wireless-powered sensor networks (WPSNs) could be achieved, if all components are powered by green sources. Hence, designing accurate mathematical models that capture the network behavior under these circumstances is necessary to provide a deeper comprehension of such networks. In this paper, we provide an analytical model for the connectivity in a large-scale zero-energy clustered WPSN under two common transmission schemes, namely, unicast and broadcast. The sensors are WEH-enabled, while the network components are solar-powered and employ a novel energy allocation algorithm. In our results, we evaluate the tradeoffs among the various scenarios via extensive simulations and identify the conditions that yield a fully connected zero-energy WPSN.

The densification of mobile networks with small cells (SCs) is one promising solution to address the explosive increase in data traffic. Due to their financial implementation requirements, which could not be met by mobile service providers (MSPs), the emergence of third parties that deploy SC networks creates new business opportunities. In this paper, we study the Small Cell as a Service (SCaaS) business model, where MSPs lease the SC infrastructure from a small-cell operator (SCO) to offload their traffic. In SCaaS, the SCO requires a licensed spectrum for the SC operation, which must be provided by MSPs. We introduce a proportional fair auction scheme for the SC capacity distribution and the corresponding transactions. We examine the MSPs' profit and throughput for the dedicated and co-channel spectrum deployments, various SC deployments, and different levels of MSP competition in the auctions. We propose a learning mechanism that improves the bidders' strategies for realistic cases without information in the traffic and the auctions. Extensive simulations give insight into SCaaS's techno-economic aspects, the MSPs' strategies, and their impact on the system. Finally, our results prove our mechanism's efficiency, which performs similarly to the optimal social welfare-maximization optimal results.

The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of dense Heterogeneous Networks (HetNets). At the same time, the content providers' rise has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of HetNet investments. To this end, we consider a HetNet deployment where MNOs offer a multitude of services with diverse pricing. We propose a heuristic, joint QoE-aware resource allocation and dynamic pricing algorithm with overall user satisfaction constraints to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a state of the art algorithm. Moreover, we demonstrate the benefits of our dynamic pricing scheme and its applicability on other resource allocation algorithms.

As wearables provide new health-related functionalities, they can be employed in hospitals to monitor patients and notify the medical personnel regarding their status. However, in order to be approved by the medical community, wearables need to have reliable communication and high lifetime. In such scenarios, it is important to know the probability of correct notification which is affected mainly by the deployment of the wireless wearables and their energy supply. Typically, rooms in hospitals host multiple people and, thus, a clustered communication model should be adopted for more trustworthy results. Moreover, by employing wireless charging, it is possible to provide an uninterrupted operation with high reliability. Therefore, in this paper, we study the aforementioned probability in a clustered network while the wearable devices are wirelessly charged. We provide an analytical model for the wearables' ability to inform quickly the medical personnel and discuss different trade-offs via extensive simulations.

The present paper studies fair sharing possibilities of stored renewable energy (RE) in densely populated areas among multiple mobile network operators (MNOs). The main objective is to enable MNOs, who already share their infrastructure in terms of a green network operation, to further reduce their power consumption by fairly sharing (i) the solar power harvested during daylight and (ii) the harvested energy that is stored in storage devices, e.g. batteries, during night hours. To this end, a bankruptcy game is designed so as to capture the energy sharing interactions among MNOs. Taking into consideration the energy deficits of each MNO for a studied period of time, Shapley Value (SV) is employed to fairly determine the amounts of energy to be allocated to each MNO. Focusing on night hours, it is shown that the proposed scheme provides all cooperative MNOs with long store-energy-based network operation during night in comparison to baseline schemes. Moreover, we display that the proposed scheme outperforms its counterparts in terms of needed grid support and allocation fairness among the cooperative MNOs for different MNO traffic patterns and volumes.

The rise of Over The Top (OTT) content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. The applications' various Quality of Service (QoS) requirements as well as the use of multiple devices per user have increased the traffic heterogeneity, pressing the telecommunications industry to the deployment of 5G networks in 2020. At the same time, the rise of OTT providers has also led to the decrease of the Mobile Network Operators' (MNOs) revenues. Under these circumstances, the MNOs need to guarantee the users' Quality of Experience (QoE) requirements, while ensuring the sustainability of a 5G investment. To this end, we consider a 5G Heterogeneous Network (HetNet) deployment where MNOs use a QoE-based charging scheme. We propose a heuristic, QoE-aware user association algorithm to maximize the MNO profit, while providing high QoE. Simulation results show that the proposed algorithm can handle traffic heterogeneity by achieving substantial profit and QoE gains, compared to a baseline SINR-based scheme.

Facing the increasing energy demands associated with the perspective of fifth generation (5G) wireless networks, the Mobile Network Operators (MNOs) are motivated to gradually convert their traditional Radio Access Network (RAN) infrastructure to more flexible and power efficient centralized architectures, i.e., Cloud-RAN (C-RAN). Apart from their promising benefits in terms of management and network optimization, these new architectures further enable the sharing of spectrum and network elements, such as the Remote Radio Heads (RRHs) and the Baseband Units (BBUs), among multiple operators. In this paper, we introduce a novel scheme based on coalitional game theory to identify the potential room for cooperation among different MNOs that provide service to the same area. The proposed scheme sets the rules for profitable collaboration and identifies the core formation conditions (i.e., pricing) for various scenarios with different market and spectrum shares among three operators. Our results show that i) cooperation among subcoalitions of MNOs is always beneficial, yielding both higher revenues and enhanced Quality of Service (QoS) for the end users, and ii) the cooperation of all operators (grand coalition) is profitable for given user pricing in different scenarios.

In this paper, we propose a novel policy for device caching that facilitates popular content exchange through high-rate device-to-device (D2D) millimeter-wave (mmWave) communication. The D2D-aware caching policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices, with the goal to enable D2D connections. By exploiting the high bandwidth availability and directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the throughput gains of the D2D communication in the sub-6-GHz bands. Furthermore, based on a stochastic-geometry modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex modes for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte Carlo simulations. In addition, for a wide range of a content popularity indicator, the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.

In this paper, we perform a system-level feasibility analysis of full-duplex (FD) relay-aided cellular networks that are equipped with multiple antennas at the base stations (BSs) and the relay nodes (RNs). The aim is to understand whether FD relaying is capable of enhancing the rate of cellular networks. With the aid of tools from stochastic geometry, we develop a tractable approach for computing the percentile rate, which allows us to gain insights on the impact of FD relaying for both the cell-edge and the cell-median mobile terminals subject to network interference. Contrary to previous works that do not consider the network interference, the framework reveals that even in the absence of self-interference at the FD RNs, a network with half-duplex (HD) RNs can outperform its FD counterpart for a moderate number of antennas at the BSs and RNs. On the other hand, the FD-based network can substantially outperform both the HD-based one and the one without RNs for a sufficiently large number of antennas at the BSs and RNs and substantially small self-interference power effect at the RNs. Finally, the aforementioned analytical insights are validated by means of Monte Carlo simulations.

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular, the delay performance of FiWi enhanced LTE-Advanced (LTE-A) heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints. We use recent and comprehensive smartphone traces of the PhoneLab data set to verify whether the previously reported assumption that the complementary cumulative distribution function of both WiFi connection and interconnection times fit a truncated Pareto distribution is still valid. In this paper, we put a particular focus on the 5G key attributes of very low latency and ultra-high reliability and investigate how they can be achieved in FiWi enhanced LTE-A HetNets. Furthermore, given the growing interest in decentralization of future 5G networks (e.g., user equipment assisted mobility), we develop a decentralized routing algorithm for FiWi enhanced LTE-A HetNets.

In long term evolution advanced (LTE-A) networks, the mobile devices can concurrently participate in cooperative outband device-to-device (D2D) data exchange by virtue of user- or network-related parameters (e.g., interest in the same content and cooperative transmissions, respectively). In these scenarios, two major problems arise: 1) the coexistence of multiple devices creates channel access issues, demanding effective medium access control (MAC) schemes, and 2) cellular network factors (i.e., scheduling policy and channel conditions) affect the D2D communication, as the circulating information in D2D links is mainly of cellular network origination, stressing the need for cross network approaches. In this context, the contribution of this paper is threefold. First, exploiting idle devices as relays and the benefits of network coding (NC) in bidirectional communications, we propose an adaptive cooperative NC-based MAC (ACNC-MAC) protocol for the D2D data exchange. Then, we devise a cross-network model that captures the impact of cellular network characteristics on D2D communication. Finally, we evaluate the performance of the ACNC-MAC in terms of throughput, energy efficiency, and battery consumption. Our results show that the LTE-A parameters and the relays' participation significantly affect the D2D throughput, while the D2D performance deteriorates with the increase of cell congestion.

Energy harvesting brings a key solution to the increasing energy bill and environmental concerns but, at the same time, potential energy shortage may deteriorate the network availability. In this paper, we analyze the performance of off-grid small-cell base stations (scBS) with finite battery capacity and design a new power-availability-aware cell association based on periodical broadcast of the scBS battery level. Each mobile terminal (MT) targets its own set of available scBSs before association, i.e., the set of scBSs that can guarantee service provided 1) the scBS battery level; 2) the power required to satisfy a received power constraint at each MT, given the scBS-MT distance and the shadowing attenuation; and 3) the estimated power consumed to serve other MTs potentially associated to the same scBS, which is computed using stochastic geometry tools. Next, we develop for it a tractable performance analysis and derive closed-form expressions for the probability of power outage and the coverage probability. By dynamically adapting to the fluctuations of the base station battery and the MT received power requirement, the proposed cell association allows a more even distribution of the available energy in the network, brings robustness against harvesting impairment and thereby, significantly outperforms conventional strategies.

Wireless network virtualization enables efficient network sharing in Long Term Evolution Advanced (LTE-A) networks. For the accommodation of ever-increasing user demands, Mobile Virtual Network Operators (MVNOs) can lease and operate virtual resources in network infrastructure that belongs to Mobile Network Operators (MNOs). The coexistence of multiple MVNOs that serve users with different Quality of Service (QoS) requirements and spatial distribution in an LTE-A cell further complicates the arising resource allocation problem. Moreover, the MVNOs aim to satisfy the QoS demands for the maximum possible number of users with the minimum possible cost. This multifaceted context renders centralized optimization approaches for virtual resource allocation in shared LTE-A networks unsuitable. As an alternative, the framework of matching theory, thanks to its distributed nature, can achieve a proper allocation through a stable matching between resources and MVNOs. In this context, we introduce a matching-theoretic formulation for the virtual resource allocation problem and propose a distributed algorithm that properly matches the MVNOs with the available resources. Our simulation results show that the proposed algorithm reaches a stable matching that satisfies the QoS demands for more users in comparison with other approaches.

Full-Duplex (FD) wireless and Device-to-Device (D2D) communication are two promising technologies that aspire to enhance the spectrum and energy efficiency of wireless networks, thus fulfilling key requirements of the 5thgeneration (5G) of mobile networks. Both technologies, however, generate excessive interference, which, if not managed effectively, threatens to compromise system performance. To this direction, we propose two transmission policies that enhance the communication of two interfering FD-enabled D2D pairs, derived from game theory and optimization theory. The game-theoretic policy allows the pairs to choose their transmission modes independently and the optimal policy to maximize their throughput, achieving significant gains when the pairs interfere strongly with each other.

We demonstrate for the first time an analytical model for computing the end to end packet delay of an Optical/Wireless 60GHz Radio-over-Fiber (RoF) network operating under the Medium-Transparent MAC (MT-MAC) protocol. The model takes into account contention both at the optical and the wireless layer, effectively incorporating the MT-MAC mechanism for seamless and dynamic capacity allocation over both optical and wireless transmission media. Based on this model, we provide an extensive delay performance analysis of the Medium Transparent MAC protocol for various optical capacity availability scenarios, varying load conditions and optical network fiber lengths. The theoretical results are found to be in good agreement with respective simulation-based findings, confirming that the employment of Medium Transparent MAC protocols can allow for efficient incorporation of the MT-MAC scheme into the upcoming era of 5G mm-wave small-cell networks.

The emerging Internet of Things paradigm has triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks. However, the channel randomness and the random deployment of such networks could cause missed detections by isolated (i.e., unable to disseminate their messages) or inactive (i.e., without enough energy to transmit) nodes. Moreover, as the number of nodes increases, the use of “green” solutions such as wireless energy harvesting for powering battery-less devices could eliminate the maintenance costs and boost immensely the network lifetime. In this paper, we study the connectivity in a wireless-powered sensor network (WPSN) with battery-less devices under two common routing schemes, namely unicast and broadcast. We provide an analytical model for the ability of a WPSN to be reliable and evaluate the trade-offs among the different scenarios via simulation.

We investigate the possibility of wirelessly charging autonomous wearable sensors for patient health-monitoring through dedicated power transfer from Power Beacons (PB). We propose a novel strategy for transmitting energy beams towards patients, where sensors emit a charging request based on a battery lower threshold and receive energy till the battery reaches an upper threshold. These energy beams are steered towards the patient's smartphone rather than towards the sensor, relieving the need for tracking the PB-sensor channel at only negligible performance loss. Furthermore, as PBs cannot serve all requiring sensors simultaneously, we compare different scheduling algorithms, jointly with the battery thresholds and the energy beam width. For the examined configurations, results show that the energy outage probability is slightly affected by the scheduling policy, and most of the energy is harvested from non-intended beams. We show that the strategy for energy-beam transmission should be adjusted to the sensor location on the human body and the patient's velocity for better performance. Finally, proposing new strategies minimizing the waiting duration can be used as a guideline to reduce the energy outage.

Future Internet of Things (IoT) will connect to the Internet billions of heterogeneous smart devices with the capacity of interacting with the environment. Therefore, the proposed solutions from an IoT networking perspective must take into account the scalability of IoT nodes as well as the operational cost of deploying the networking infrastructure. This will generate a huge volume of data, which poses a tremendous challenge both from the transport, and processing of information point of view. Moreover, security issues appear, due to the fact that untrusted IoT devices are interconnected towards the aggregation networks. In this paper, we propose the usage of a Software- Defined Networking (SDN) framework for introducing security in IoT gateways. An experimental validation of the framework is proposed, resulting in the enforcement of network security at the network edge.

Contemporary technologies as implemented in the field of healthcare have provided the everyday clinical practice with a plethora of tools to be used in various settings. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most promising technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. WSN4QoL is a Marie Curie project involving academic and industrial partners from three EU countries, which aims to show how new WSNs-based technologies suit the specific requirements of pervasive healthcare applications. In particular, in this paper, the WSN4QoL's system architecture is presented as designed to exploit the Network Coding (NC) mechanisms to achieve energy efficiency in the wireless communications and distributed positioning solutions to locate patients in indoor home environments. The system has been validated through experimental activities using commercial off the shelf (COTS) WSN testbeds and medical devices prototypes offered by a commercial partner. Results demonstrate that NC helps in achieving substantial gains in terms of energy efficiency as compared to traditional relay mechanisms, while the proposed positioning solution is able to locate people in indoor environments at a sub-room accuracy level, without requiring any extra dedicated hardware.

Resource allocation in multi-operator scenarios requires an estimate of the tenants' traffic needs. This is necessary in the scenario where a Mobile Network Operator (MNO) owns the Radio Access Network (RAN) and many Mobile Virtual Network Operators (MVNOs) act as resellers of their host network's capacity under their own brands, to their own customers. In such scenarios, the forecasted MVNO traffic is the basis for providing resources suitable with the corresponding MVNOs demand. To that end, the dynamic provision of resources among MVNOs should be performed in flexible, short-term time scales. In this paper, we effectively address this issue by integrating the capacity broker into the 3rd Generation Partnership Project (3GPP) network management architecture using the minimum set of enhancements. In addition, to fully exploit its capabilities, we propose the Multi-tenant Slicing (MuSli) of capacity algorithm, to allocate resources towards MVNOs in coarse time scales. MuSli considers the estimated capacity and the impact of the traffic type (i.e., guaranteed QoS and Best-Effort) in each MVNO, to provide better utilization of the host network's capacity. Our results highlight the gains in the number of served requests without compromising their service quality.

In the Internet of Things (IoT) context, the massive proliferation of wireless devices implies dense networks that require cooperation for the multihop transmission of the sensor data to central units. The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the introduction of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a non-parametric statistical approach, based on the Kruskal-Wallis method, for the detection of user misbehavior in network coding scenarios. The proposed method is shown to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.

Due to the dense small cell deployment to cope with the increased data traffic of fifth generation (5G) networks, the direct backhaul (BH) connection of all small cells to the core network becomes challenging. To that end, millimeter wave (mmWave) is a promising solution for cost-efficient high capacity small cell BH links. However, due to the severe signal attenuation at high frequencies, mmWave can provide good coverage only for short distances, thus favoring a multi-hop 5G BH architecture. In this context, user association that impacts both the network and user performance calls for BH-aware strategies. Hence, in this paper, we study the user association problem aiming at the joint energy and spectrum efficiency maximization of the network, without compromising the user quality of service. The problem is formulated as a generalized assignment problem, which considers the capacity and energy consumption both in the access network and BH links. Provided that the considered problem is shown to be NP-hard, we employ optimization techniques to obtain an upper bound on system performance. We also propose a low complexity algorithm, which is shown to outperform the state-of-the-art while achieving near-optimal performance.

International audience

Mobile data offloading has been proposed as a solution for the network congestion problem that is continuously aggravating due to the increase in mobile data demand. However, the majority of the state-of-the-art is focused on the downlink offloading, while the change of mobile user habits, like mobile content creation and uploading, makes uplink offloading a rising issue. In this work we focus on the uplink offloading using IP Flow Mobility (IFOM). IFOM allows a LTE mobile User Equipment (UE) to maintain two concurrent data streams, one through LTE and the other through WiFi access technology, that presents uplink limitations due to the inherent fairness design of IEEE 802.11 DCF by employing the CSMA/CA scheme with a binary exponential backoff algorithm. In this paper, we propose a weighted proportionally fair bandwidth allocation algorithm for the data volume that is being offloaded through WiFi, in conjunction with a pricing-based rate allocation for the rest of the data volume needs of the UEs that are transmitted through the LTE uplink. We aim to improve the energy efficiency of the UEs and to increase the offloaded data volume under the concurrent use of access technologies that IFOM allows. In the weighted proportionally fair WiFi bandwidth allocation, we consider both the different upload data needs of the UEs, along with their LTE spectrum efficiency and propose an access mechanism that improves the use of WiFi access in uplink offloading. In the LTE part, we propose a two-stage pricing-based rate allocation under both linear and exponential pricing approaches, aiming to satisfy all offloading UEs regarding their LTE uplink access. We theoretically analyse the proposed algorithms and evaluate their performance through simulations. We compare their performance with the 802.11 DCF access scheme and with a state-of-the-art access algorithm under different number of offloading UEs and for both linear and exponential pricing-based rate allocation for the LTE uplink. Through the evaluation of energy efficiency, offloading capabilities and throughput performance, we provide an improved uplink access scheme for UEs that operate with IFOM for uplink offloading.

As large-scale dense and often randomly deployed wireless sensor networks (WSNs) become widespread, local information exchange between colocated sets of nodes may play a significant role in handling the excessive traffic volume. Moreover, to account for the limited life-span of the wireless devices, harvesting the energy of the network transmissions provides significant benefits to the lifetime of such networks. In this paper, we study the performance of communication in dense networks with wireless energy harvesting (WEH)-enabled sensor nodes. In particular, we examine two different communication scenarios (direct and cooperative) for data exchange and we provide theoretical expressions for the probability of successful communication. Then, considering the importance of lifetime in WSNs, we employ state-of-the-art WEH techniques and realistic energy converters, quantifying the potential energy gains that can be achieved in the network. Our analytical derivations, which are validated by extensive Monte-Carlo simulations, highlight the importance of WEH in dense networks and identify the tradeoffs between the direct and cooperative communication scenarios.

Device-to-device (D2D) discovery is the inextricable prelude for the direct exchange of local traffic between cellular users in proximity. The D2D discovery process can be based on either autonomous actions taken by D2D-enabled devices, also known as network-assisted D2D discovery or core network functionalities to estimate proximity, also known as network-assisted D2D discovery. A key advantage of network-assisted D2D discovery is its potential to reduce the energy, signaling, and interference required for D2D discovery, by exploiting knowledge of the network layout. We analyze the performance of network-assisted D2D discovery in random spatial networks and derive useful guidelines for its design. We derive approximate expressions for the distance distribution between two D2D peers conditioned on the core network's knowledge of the cellular network layout, assuming that the base stations are distributed according to the Poisson point process. The expressions are used to assess the interplay between the D2D discovery probability and key system parameters, such as network intensity and transmit power, as well as to identify conditions to maximize the D2D discovery probability. Numerical results validate the accuracy of our findings and provide insights on the performance tradeoffs of network-assisted D2D discovery.

In a fluctuating mobile environment where operators have to confront the ever increasing demands of their subscribers, insufficient spectrum poses capacity limitations. This is more evident in the downlink (DL) direction, since DL resources are over-utilized compared to the uplink (UL) ones as a result of asymmetry in the generated traffic and intense interference. In this framework, we propose the creation of Device-to-Device (D2D) based clusters of users where intra-cluster communication will be achieved over UL resources. The minimization of the required resources (equivalent to the maximization of the spectral efficiency), is formulated as an integer (binary) linear optimization problem. Finally, a low- complexity clustering optimization algorithm for resource efficiency (CORE), is devised. Illustrative results prove that CORE, manages to increase the spectral efficiency and the network's capacity.

The dense deployment of high and low power nodes, i.e., eNodeBs (eNBs) and small cells (SCs), respectively, is expected to be the core element of fifth generation (5G) networks. Given the massive SC deployment, the aggregate power consumption attributed to SCs in the coverage area of a macrocell becomes considerable, thus raising issues for the network energy efficiency. However, the coexistence of multiple mobile network operators (MNOs) in the same geographic area fosters cooperation agreements that suggest further energy and cost saving. In this paper, with a view to address the aforementioned issues, we attempt to study the energy efficiency aspects in the case of next generation outdoor Long Term Evolution (LTE) heterogeneous networks (HetNets) owned by rival MNOs. More specifically, we propose a cooperative switching off scheme for low-traffic hours that applies to both eNBs and SCs and exploits roaming-based infrastructure sharing to guarantee the provided quality of service (QoS). To gain insights, we study the energy consumption impact compared to existing switching off schemes. It is shown that the proposed scheme achieves notable energy saving, without compromising the QoS to the network users.

Device-to-Device (D2D) communications enable direct exchange of localized traffic between cellular users in proximity. Nonetheless, the employment of D2D communications heavily relies on the capability of the cellular devices to infer on their proximity. In this paper, we analyze the performance of network- assisted D2D discovery in random cellular networks and derive optimal deployment strategies for transforming the today's cellular network, which is optimized for coverage and capacity, into a D2D- centric network where the discovery and communications between cellular devices will be orchestrated by the core network. In this direction, we develop an analytical framework that exploits existing knowledge of the cellular network topology and effectively estimates the probability that two tagged devices are in proximity, a.k.a. D2D discovery probability. Also, we identify conditions under which the D2D discovery probability is maximized and assess the key performance trade-offs inherent to network-assisted D2D discovery.

Smart Grid (SG) is considered as the future of the electrical power distribution system, where Advanced Metering Infrastructure (AMI) will let utilities to acquire and analyze the consumption data and access and control several home appliances for power balancing purposes through special devices, known as Smart Meters (SMs). Long Term Evolution (LTE) appears as a promising solution to handle the SMs traffic due to its high bandwidth and flexibility. However, given that SMs need to periodically send measurements to the eNodeB (eNB), the network uplink could be a potential bottleneck, thus affecting the users running real-time applications, such as voice calls. To overcome this problem, in this paper, we present a scheduling policy that jointly considers the channel quality, the traffic prioritization and the AMI Packet Delay Budget (PDB) in order to provide the SMs with the required resources and reduce the impact on the real-time traffic. Extensive simulation experiments have been carried out, indicating the smooth coexistence between the SMs and the voice users, since the proposed scheduler achieves higher percentage of served users compared to widely employed schedulers.

Device-to-Device (D2D) communication allows the direct connection of mobile devices in a cellular network. In D2D networking, cooperative communication is inherent due to the proximity of the devices that are able to overhear and forward information. Particularly, adjacent devices can act as relays and assist the communication of other devices. Network Coding (NC) can further increase the cooperation gains, since a number of packets can be encoded and transmitted together. However, the contention among multiple relays causes channel access issues that must be regulated by effective Medium Access Control (MAC) protocols. In this context, we propose an Adaptive Cooperative Network Coding-based MAC (ACNC-MAC) protocol that utilizes cooperative relaying and exploits NC opportunities in a D2D topology. Both analytical and simulation results show that the proposed protocol is advantageous in terms of energy efficiency without sacrificing the Quality of Service (QoS).

Future cellular networks as envisaged by mobile operators, are expected to consist of macro cells overlaid with small nodes in dense architectures. These multi-tier deployments pose challenges to Mobility Management procedures like Handover, since traditional algorithms fail to keep up with heterogeneity, thus causing degradations in Handover performance. Finding ways to reduce unnecessary Handovers, mainly for fast moving users, and boost offloading of the lower speed ones to small cells, are subjects undergoing intense study. Most studies propose solutions based on the appropriate selection of the key parameters involved in the Handover procedure, such as the Hysteresis Margin and Time-to-Trigger. However, they do not take into account the impact that inter-site distance has on the Handover performance. In this framework, we study the dependency of the Handover procedure on the inter-site distance between a small cell and the overlaid macro cell in a two-tier deployment. Moreover, a Handover performance analysis in terms of Handover, Radio Link Failure, Handover Failure and Ping-Pong probabilities is carried out and evaluated through extensive simulations. Finally, the impact of TTT on the Handover performance is presented, while it is concluded that the appropriate TTT value should be selected according to inter-site distance, user profile (i.e speed) and overall mobility in the network automatically and in line with the concept of Self-Organized Networks (SON).

As the number of nodes in wireless sensor networks (WSNs) increases, new challenges have to be faced in order to maintain their performance. A fundamental requirement of several applications is the correct transmission of the measurements to their final destinations. Thus, it is crucial to guarantee a high probability of connectivity, which characterizes the ability of every node to report to the fusion center. This network metric is strongly affected by both the fading characteristics and the different routing protocols that are used for the dissemination of data. In this paper, we study the probability of a network to be fully connected for two widely employed routing mechanisms, namely unicast and K-anycast. The analytical derivations and the simulations evaluate the trade-offs among the different routing mechanisms and provide useful guidelines on the design of WSNs.

In this paper we focus on the uplink offloading based on IP Flow Mobility (IFOM). With IFOM a User Equipment (UE) is able to maintain concurrently two data streams, one through LTE and the other through WiFi. We propose a weighted proportionally fair algorithm for the WiFi access and a linear pricing based algorithm for the LTE access. The existence of a malicious UE is considered that aims to exploit the WiFi bandwidth against its peers in order to upload less data through the energy demanding LTE uplink and a reputation based method is proposed to combat its selfish operation. We theoretically analyse our approach and evaluate the performance of the malicious and the truthful UEs in terms of energy efficiency through simulations. We show that while the malicious UE presents better energy efficiency before being detected, its performance is significantly degraded with the proposed reaction method.

The densification of mobile networks with small cells is seen as the most promising solution to the explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the service providers, the emergence of third parties that deploy and lease small cell networks opens up new business opportunities. In this paper, we study a proportionally fair auction scheme as an efficient way of small cell capacity distribution, both in network and financial terms. To improve the bidders' strategies, we propose a novel learning mechanism that alleviates the uncertainty incurred by variations in the traffic and the lack of information in the auctions. Extensive simulations prove the efficiency of our proposal, which also performs in equal terms with the ideal case of complete information.

In this paper, we shed some light on the latency and reliability issues of mobile backhaul networks, which have been largely ignored in the past, and examine their impact on LTE-A heterogeneous networks (HetNets). Specifically, we propose a backhaul-aware user association algorithm for fiber-wireless (FiWi) enhanced LTE-A HetNets. The performance limiting factors of state-of-the-art fiber backhaul infrastructures are highlighted and a variety of solutions are described. To mitigate the vulnerability of the backhaul against fiber cuts, we introduce different advanced protection techniques. Accounting for the given conditions of the backhaul in terms of delay and reliability, we present a distributed load-balancing algorithm for user association in FiWi-LTE HetNets. The proposed algorithm is analyzed and evaluated numerically by comparing its performance with state-of-the-art alternative approaches in terms of average delay, blocking probability, average achievable throughput, and service interruption percentage. The obtained results demonstrate that our algorithm outperform its counterparts in terms of delay and service interruption percentage, while its average achievable throughput is the same as that of a backhaul-unaware alternative solution. In addition, the blocking probability of the proposed backhaul-aware load-balancing method is shown to be higher than that of backhaul-unaware ones.

Wireless body area networks (WBANs) are composed of sensors that either monitor and transmit vital signals or act as relays that forward the received data to a body node coordinator (BNC). In this paper, we introduce an energy efficient vital signal telemonitoring scheme, which exploits compressed sensing (CS) for low-complexity signal compression/reconstruction and distributed cooperation for reliable data transmission to the BNC. More specifically, we introduce a cooperative compressed sensing (CCS) approach, which increases the energy efficiency of WBANs by exploiting the benefits of random linear network coding (RLNC). We study the energy efficiency of RLNC and compare it with the store-and-forward (FW) protocol. Our mathematical analysis shows that the gain introduced by RLNC increases as the link failure rate increases, especially in practical scenarios with a limited number of relays. Furthermore, we propose a reconstruction algorithm that further enhances the benefits of RLNC by exploiting key characteristics of vital signals. With the aid of electrocardiographic (ECG) and electroencephalographic (EEG) data available in medical databases, extensive simulation results are illustrated, which validate our theoretical findings and show that the proposed recovery algorithm increases the energy efficiency of the body sensor nodes by 40% compared to conventional CS-based reconstruction methods.

In this paper, we consider Analog Network Coding (ANC) in the Multiple Access Relay Channel (MARC) with multiple relays, and provide the following three-fold contribution: 1) we introduce a tractable mathematical framework for computing the Symbol Error Rate (SER) of Maximum-Likelihood (ML), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE) receivers; 2) by capitalizing on this tractable mathematical framework, we formulate a power allocation problem that is proved to be convex for ML, ZF and MMSE receivers; and 3) we provide closed-form expressions of the optimal power to be allocated to the sources and the relays for ZF and MMSE receivers. With the aid of Monte Carlo simulations, we validate the accuracy of the proposed mathematical framework for various network topologies and channel conditions, as well as study the effectiveness of optimal power allocation. It is shown, in particular, that power optimization is beneficial as the number of sources increases and if the quality of the source-relay links is better than the quality of the relay-destination links.

Today's heterogeneous wireless network (HWN) is a collection of ubiquitous wireless networking elements (WNEs) that support diverse functional capabilities and networking purposes. In such a heterogeneous networking environment, proximity estimation will play a key role for the seamless support of emerging applications that span from the direct exchange of localized traffic between homogeneous WNEs (peer-to-peer communications) to positioning for autonomous systems using location information from the ubiquitous HWN infrastructure. Since most of the existing wireless networking technologies enable the direct (or indirect) estimation of the distances and angles between their WNEs, the integration of such spatial information is a natural solution for robustly handling the unprecedented demand for proximity estimation between the myriads of WNEs. In this paper, we develop an analytical framework that integrates existing knowledge of the HWN layout to enable proximity estimation between WNE supporting different radio access technologies (RATs). In this direction, we derive closed-form expressions for the distance distribution between two tagged WNEs given partial (or full) knowledge of the HWN topology. The derived expressions enable us to analyze how different levels of location-awareness affect the performance of proximity estimation between WNEs that are not necessarily capable of communicating directly. Optimal strategies for the deployment of WNEs, as means of maximizing the probability of successful proximity estimation between two WNEs of interest, are presented, and useful guidelines for the design of location-aware proximity estimation in the nowadays HWN are drawn.

The explosive data traffic demands stress the need for additional capacity in cellular systems, which can be achieved through the exploitation of Small Cells (SCs). However, the deployment of SCs implies higher energy consumption in the network, something that translates into higher financial cost for the operators. To that end, the existence of a third party that provides a common SC infrastructure for the operators has been introduced as an appealing "green" solution, raising though important issues with regard to the cost sharing for the operators. In this paper, we effectively address this issue by proposing an accurate cost model for the SCs and employing different state-of-the-art techniques to share this cost. In addition, taking into account the impact of the traffic pattern on this cost, we propose a novel hybrid cost sharing policy that provides a fair outcome for the operators. Our results highlight the potential energy efficiency gains in the network along with different ways of sharing the cost of the deployed SCs.

Wireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches.

In the next few years, small cells (SCs) are expected to be densely deployed to achieve sustainable capacity enhancement. Due to the high SC density, some SCs will not have a direct connection to the core network, and thus will forward their traffic to their neighboring SCs through a multi-hop backhaul (BH). In such multi-hop architectures, the user association problem becomes challenging with BH energy consumption playing a key role. In parallel, the ever-increasing need to minimize the user equipment (UE) transmission power along with the uplink (UL) and downlink (DL) traffic asymmetry, predicate the joint study of UL and DL. Thus, in this paper, we study the joint UL and DL cell selection problem aiming at maximizing the total network energy efficiency, without compromising the UE quality of service. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a heuristic context-aware algorithm that associates the UEs in an energy-efficient way, while considering both access and BH energy consumption in UL and DL. We evaluate the proposed algorithm performance and we show that it can achieve significantly higher energy efficiency than the reference approaches, while maintaining high spectral efficiency and low UE power consumption.

Mobile data offloading has been proposed as a solution for network congestion. However, the majority of the state-of-the-art is focused on the downlink offloading, while the change of mobile user habits, like mobile content creation and uploading, makes uplink offloading a rising issue. In this work we focus on the uplink offloading using Ip Flow Mobility (IFOM). IFOM allows a LTE mobile User Equipment (UE) to maintain two concurrent data streams, one through LTE and the other through WiFi access technology, that presents uplink limitations due to the inherent fairness design of IEEE 802.11 DCF. In this paper, we propose two uplink offloading algorithms to improve the energy efficiency of the UEs and to increase the offloaded data volume under the concurrent use of access technologies that IFOM provides. In the first algorithm, UEs with heavy traffic are promoted and are given priority in accessing the WiFi Access Point (AP) to offload their data. In the second algorithm, we propose a proportionally fair bandwidth allocation over the data volume needs of the UEs. We theoretically analyse the proposed algorithms and evaluate their performance through simulations. We compare their performance with the 802.11 DCF access scheme and with a state of the art access algorithm under different number of offloading UEs. Through the provided evaluation we show that there is room for improvement in the uplink access scheme of the WiFi that affects the energy efficiency of a UE during IFOM uplink offloading.

The increasing energy consumption driven by striking growths in the number of users and data usage turns out the focal point for mobile operators in fulfilling requirements on cost reduction and environmental impact targets. As a step towards incorporating more energy friendly mobile platforms in future networks, 3GPP LTE-Advanced has adopted coordinated multipoint (CoMP) transmission/reception due to its ability to mitigate and/or coordinate inter-cell interference (ICI). The major CoMP techniques which already existed are joint transmission (JT) and coordinated scheduling/ beamforming (CS/CB). In this paper we propose a novel energy-efficient design (NEED) for heterogeneous network (HetNet) CoMP architecture composing both JT and CS/CB which provides a realistic trade-off for green wireless networks. A feedback based ICI coordination scheme is investigated in terms of different performance metrics (throughput; cell average and cell-edge energy efficiency; radio signaling and backhaul overhead comparison; and power consumption ratio) which eventually helps to reach a decision in favor of the proposed architecture in terms of performance trade-off.

In this paper, we aim at the optimal use of the scarce energy collected by a node powered by human energy harvesting in order to improve the provided Quality of Service (QoS). To achieve this goal, we have developed a Power-QoS control scheme, called PEH-QoS. PEH-QoS is composed of three sub-modules that interact with each other in order to make optimal use of energy and get the best possible QoS. The scheme intends to ensure that a node can both capture/detect the medical events and transmit the respective data packets efficiently. One of the main features of our mechanism is that only useful data sequences are transmitted, discarding data packets that have lost their clinical validity (i.e., out of date). Extensive simulations have been conducted in order to evaluate the behavior of PEH-QoS in a typical medical node under energy harvesting conditions.

Random dense deployment of heterogeneous networks (HetNets), consisting of macro base stations (BS) and small cells (SC), can provide higher quality of service (QoS) while increasing the energy efficiency of the cellular network. In addition, it is possible to achieve lower deployment cost and, therefore, maximize the benefits for the network providers. In this paper, we propose a novel method to determine the minimum deployment cost of a two-tier heterogeneous cellular network using random deployment. After deriving the coverage probability of the two-tier deployment by using stochastic geometry tools, we identify the tier intensities that provide the minimum deployment cost for a given coverage probability. Extensive simulations verify the existence of a unique set of intensities for different coverage constraints.

To meet the ever-increasing traffic demands, future cellular networks are about to include a plethora of small cells (SCs), with user equipments (UEs) being able of communicating via multiple bands. Given that SCs are expected to be eventually as close as 50 m apart, some of them will not have a direct connection to the core network, and thus will forward their traffic to the neighboring SCs until they reach it. In such architectures, the user association problem becomes challenging with backhaul (BH) energy consumption playing a key role. Thus, in this paper, we study the user association problem aiming at maximizing the network energy efficiency. The problem is formulated as an optimization problem, which is NP-hard. Therefore, we propose a cognitive heuristic algorithm that exploits context-aware information (i.e., UE measurements and requirements, the HetNet architecture knowledge and the available spectrum resources of each base station (BS)) to associate the UEs in an energy-efficient way, while considering both the access and the BH energy consumption. We evaluate the performance of the proposed algorithm under two study-case scenarios and we prove that it achieves significantly higher energy efficiency than the reference algorithms, while maintaining high spectral efficiency.

In this paper, we examine multi-source multi-relay systems that employ Analog Network Coding for which we provide a two-fold contribution: i) We derive a closed-form upper bound of the average symbol error rate (SER) per source of the system, which is shown to be tight in the high-Signal-to-Noise Ratio (SNR) region, especially for an adequate number of relays, and ii) based on this bound, for a given total power budget to be distributed among the source and relay nodes we formulate the power allocation optimization problem with the aim of minimizing the SER per source. Results show that for a common target SER among the sources, an increase in their number results in an increase in the expected energy gains over the equal power allocation for all the nodes policy.

Radio Access Network (RAN) virtualization is a promising commercial solution where multiple service providers can share underlying radio physical resources and dynamically compose heterogeneous virtual networks that coexist in isolation within the same physical infrastructure. Based on the expected future requirements, this work proposes a solution named Resources nEgotiation for NEtwork Virtualization (RENEV), which can be applied in Long Term Evolution-Advanced (LTE-A) environments, consisting of numerous small cells. This algorithm aims at achieving an efficient mapping of radio virtual network elements onto the radio resources of the existing physical network, utilizing the concept of radio resource transfer. It establishes a common virtualized control layer by handling the resources in an holistic way. The proposed solution achieves significant gains in terms of system throughput and its performance is evaluated by means of analytical model, as well as simulation results.

To address the challenging issues of energy-efficiency and seamless connectivity in heterogeneous networks, 3GPP and IEEE have recently incorporated several architectural and functional enhancements to the baseline operation of their standards for cellular and wireless local area network access, respectively. Based on the 3GPP Access Network Discovery and Selection Function (ANDSF) and the advanced measurement capabilities provided by the IEEE 802.11-2012 and the 3GPP Long Term Evolution - Advanced (LTE-A) Standards, we propose an ANDSF-assisted energy-efficient vertical handover decision algorithm for the heterogeneous IEEE 802.11-2012 / LTE-A network. The proposed algorithm enables a multi-mode mobile terminal to select and associate with the network point of attachment that minimizes its average overall power consumption and guarantees a minimum supported quality of service for its ongoing connections. System-level simulation is used to evaluate the performance of the proposed algorithm and compare it to that of other competing solutions.

In this paper, a novel Network Coding (NC)-aided Medium Access Control (MAC) protocol with mobility support (NCMOB-MAC) is presented. The proposed protocol is compatible with Automatic Repeat reQuest (ARQ) techniques and can be applied in Vehicular Ad hoc Networks (VANETs). NCMOB-MAC proposes a new way of coordinating the communication between a sender, a moving destination and a set of fixed relay nodes. The relays employ different Contention Window (CW) sizes to assign priorities and optimize the NC opportunities. Extensive simulations have been carried out to evaluate the performance of the protocol and the results show an improvement in throughput and energy efficiency compared to conventional NC-aided MAC protocols.

The existence of multiple active sources in wireless data dissemination scenarios raises considerable channel access issues. Although the overall goal in the network is the successful accomplishment of the dissemination, the individual wireless nodes aim at maximizing their battery lifetime by minimizing their particular energy consumption. Considering thus the self-centered behavior of the nodes, we anticipate to design Medium Access Control (MAC) policies in order to provide the terminals with energy efficient solutions without compromising the dissemination completion time. In this paper, we propose non-cooperative game theoretic channel access strategies by estimating equilibrium points that achieve balance between conserving energy and completing the data dissemination. In particular, we present two different MAC strategies: i) a distributed approach for ad-hoc networks, and ii) a coordinated approach for infrastructure networks, where a central controller is sporadically used to accelerate the data dissemination. In addition, network-coded transmissions are considered to eliminate the need of control packets exchange. Both analytical and simulation results are provided to evaluate our proposed schemes, demonstrating the significant gains that game theoretic techniques can bring to the network performance compared to standardized solutions.

In this paper, we provide a theoretical analysis of the optimal Maximum-Likelihood (ML) detector for the recently proposed multistream Spatial Modulation (SM) concept for multiple-input-multiple-output (MIMO) systems. A Union Bound that is based on closed-form formulas is derived for the average bit error probability (ABEP) of the ML detection, which shows an excellent fit with respect to Monte Carlo simulations for the high signal-to-noise ratio (SNR) region. Furthermore, numerical results for different rates are provided regarding the comparison of the ABEP of multistream SM with ML detection with its recently proposed counterpart with suboptimal detection and with the ABEPs of other well-known MIMO schemes, such as conventional Spatial Multiplexing, Alamouti, and SM, which show its advantage over them. In addition, for the examined MIMO configuration and rates, the relative transmit energy efficiency gain of multistream SM with ML detection over the aforementioned methods is calculated for a low target ABEP.

Energy is an important resource in wireless communications systems that has to be optimized in order to guarantee low interference in the network as well as efficient use of the available resources. One of the prominent options within the Long Term Evolution (LTE) standard is the Time Division Duplexing (TDD) option where both Uplink (UL) and Downlink (DL) share the same bandwidth and they are allocated on different times. One of the major problems in the practical implementation of TDD systems is that adjacent cells may have different loads for their UL and DL, so that the resources are allocated in a different portion between UL-DL. Such an unsynchronization among cells generates interference that downgrades the system performance. This paper calculates the optimum amount of energy that should be allocated to each entity in the system to mitigate the interference effect and to guarantee minimum Quality of Service (QoS) satisfaction at all receivers. Closed form expressions and computer simulations show the advantages of the proposed energy allocation scheme.

In this paper, we propose a hybrid polling Medium Access Control (MAC) protocol with Human Energy Harvesting capabilities, called HEH-BMAC, designed for Wireless Body Area Networks (WBANs). The proposed protocol uses a dynamic schedule algorithm to combine User Identification polling (ID) and Probabilistic Contention (PC) random access, adapting the network operation to the random, time-varying nature of the human energy harvesting sources. HEH-BMAC offers different levels of node priorities (high and normal), energy-awareness and flexibility. Extensive simulations have been conducted in order to evaluate the energy efficiency and throughput of our scheme compared to IEEE 802.15.6 standard in energy harvesting conditions.

Life expectancy is projected to increase significantly in the coming years. This fact has pushed the need for designing new and more pervasive healthcare systems. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most suitable technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. This paper proposes recent advancements in this field by introducing WSN4QoL, a Marie Curie project which involves academic and industrial partners from three EU countries. The project aims to propose new WSN-based technologies to meet the specific requirements of pervasive healthcare applications. In particular, in this paper, a Network Coding (NC) mechanism and a distributed localization solution are presented. They have been implemented on WSN testbeds to achieve efficiency in the communications and to enable indoor people tracking. Preliminary results in a real environment show good system performance that meet our expectations.

The emerging increase of the number of Base Stations causes redundant energy consumption, especially during low traffic periods, when the Base Stations capacity is underutilized. In this paper, we study energy efficiency issues in multi-operator mobile networks. Our primary goal is to save energy, without compromising the offered Quality of Service, by switching off the excessive Base Stations. To this end, we propose a novel game theoretic strategy using cost-based functions to decide the best suitable Base Stations to remain active. Mathematical analysis and simulations demonstrate that the proposed scheme can significantly reduce the energy consumption.

In this paper we evaluate the energy efficiency of MIMO technologies introduced in the emerging IEEE 802.11n standard for infrastructure based WLANs. The main focus is on exploiting multiuser capabilities of MIMO technologies, while studying the proper design of multiuser MAC schemes to achieve an energy efficient solution. Three different multiuser MAC schemes are presented and analyzed. Analytical models for energy efficiency are mathematically developed and verified with link-level computer simulations. The results show the feasibility of achieving higher energy efficiency with the proper adjustment of the system parameters, and without significant degradation of throughput performance.

Long Term Evolution (LTE) supports closed-loop MIMO techniques to improve its performance, but in order to exploit multiuser MIMO channel capabilities, the design of an efficient MAC scheme that supports Multiuser MIMO is still an open issue in the literature. This paper proposes a novel energy efficient MAC scheme for LTE, which aims to achieve simultaneous downlink transmissions to multiple users, through the deployment of a low complexity beamforming technique at the physical layer. Our proposed scheme takes advantage from the multiuser gain of the MIMO channel and the multiplexing gain of the Multibeam Opportunistic Beamforming (MOB) technique, not only to improve the system throughput but also to provide an energy efficient wireless network. We show that our proposed scheme can provide good energy saving performance at the eNB, where the mathematical expression for performance evaluation in terms of the saved energy is also presented.

In this paper, we evaluate a novel contention-aware channel selection algorithm that focuses on energy efficiency improvement of a secondary network (SN) in a scenario where other non-cooperating SNs are also using the primary resources. We present a detailed energy efficiency analysis and we study how the time between two consecutive sensing periods affects the energy efficiency. Our analysis proves that a categorization of the idle channels based on their contention level and the selection of the less contended ones can result in up to 70% gain in energy efficiency. The model is further evaluated through simulations.

In this paper, we exploit the characteristics of physical layer network coding (PNC) in multicast short range communication (SRC) networks. We present a scalable scenario of partially overlapping SRC networks, where collisions are resolved through indirect inter-group cooperation based on the features of PNC. Our scenario consists of a central network and several peripheral networks. In our previous work we proved that the central network presents significant gains in terms of throughput, delay and energy efficiency when applying the proposed scheme. Here we explore the impact of that scenario on the peripheral networks through a decomposition approach and we prove that they also present significant performance gains.

In this paper, we propose a closed-loop and single RF multiple-input-multiple-output (MIMO) method, particularly suitable for the Uplink of cellular systems, which is based on the low-complexity and recently devised method of Spatial Modulation. By selecting a subset of the available transmit antennas to implement Spatial Modulation based on the instantaneous Bit Error Rate (BER), the proposed method achieves both multiplexing and transmit-diversity gains by utilizing only one RF chain. By taking into account the total power consumption at the transmit side, we numerically show that our proposed method is more energy efficient than the single RF transmit antenna selection method for the same target performance and several MIMO configurations and correlation values among the transmit antennas.

Femtocells will play a key role for the wide adoption of the LTE-Advanced system, as they bring the access network closer to the end user in a cost-effective manner. This disruptive communication paradigm, however, dictates the use of advanced interference and mobility management algorithms to cope with the dense yet unplanned network layout. In this paper, we present an interference-aware handover decision algorithm for the LTE-Advanced femtocell network, which utilizes standard signal measurements to select the candidate cell that a) attains the minimum required channel gain for sustaining service continuity and b) minimizes the mean UE transmit power for a prescribed mean SINR target. The proposed algorithm attains backwards compatibility with the LTE-Advanced system, as it is deployed by using the private mechanism for non-standard use. Based on the evaluation methodology of the Small Cell forum, we validate the performance of the proposed algorithm and compare it against that of other state-of-the-art algorithms.

Wireless Sensor Networks (WSNs) are fostering pervasive healthcare applications, where people are remotely monitored, without the full time assistance of professional care-givers. A key enabling element is the possibility to locate people in indoor environments, especially in emergency situations. Framed within the WSN4QoL project, this paper proposes the use of a ray tracing model for network planning, to support efficient people localization and performance analysis. Preliminary results show accurate radio coverage estimations, with a minimal calibration effort.

Human movement is strongly affected by the needs of humans to socialize or cooperate with each other, in one form or another, which results in some degree of clustering and correlation with their movements. In this paper, we exploit the movement correlations, and propose a new community-based sequential paging algorithm to reduce the paging signaling overhead. Our simulation results demonstrate that given the same location update scheme employed in the LTE-A network, the proposed algorithm is capable of reducing 20% of the paging messages in the best scenario when having high moving rate.

Multiple Input Multiple Output (MIMO) adds a new dimension, the spatial one, to be optimized in Cognitive Radio (CR) by offering service simultaneously to more than one user. In this paper, statistical optimization techniques are applied to assess the performance of the Quality of Experience (QoE) in CR systems, where each user has different demands. A Multiuser scenario is considered where the transmitter can accomplish either a random or an opportunistic scheduling approach. Closed form expressions are derived for different scenarios and obtained for three QoE indicators in the system. The performance of primary and secondary users in such scenarios are mathematically formulated and compared their results to computer simulations.

Over the last years, cooperative communications have been studied from both fundamental and practical points of view. However, most of the existing studies of cooperative communications rely on math or computer simulation. In this paper, we describe the implementation of a cooperative communication strategy for wireless networks in a testbed based on Click Modular Router. We describe the methodology we have followed to reprogram the drivers controlling the data layer functions of off-the-shelf commercial Wireless Network Interface Cards (NICs). More precisely, we have implemented a Cooperative Automatic Retransmission (C-ARQ) scheme to show that the performance of wireless communications can be boosted by using an intermediate relay when the wireless channel between the source and the destination is in bad conditions. The results presented in this paper are promising, as they show that previous theory and simulation results presented in the literature can become true in a real implementation.

In this paper we present an analog-network-coding-aided layout for data dissemination. We propose a game theoretic Medium Access Control (MAC) protocol that exploits the recent developments in network coding domain to enhance the system performance. In particular, random linear network coding is used to eliminate the need of exchanging control packets, while ZigZag decoding techniques are applied to resolve the data packet collisions. Our proposed protocol is proved to enhance both the provided Quality of Service and the energy efficiency of the system.

In this paper we propose a game theoretic medium access strategy for data dissemination in wireless networks. In data dissemination scenarios, where the global target is the spreading of a particular content in a network, the main goal of the wireless nodes is twofold: to guarantee a low completion time, and to increase their battery lifetime. Hence, the existence of many active sources in the network implies conflicting situations, since the nodes have to balance a trade-off between proceeding the dissemination and saving energy. To model these conflicts, we introduce a n-player medium access game using energy-based utility functions. Both analytical and simulation results are provided to evaluate our proposed strategy.

Reducing the energy consumption in wireless networks has become a significant problem, not only because of its great impact on the global energy crisis, but also because it represents a noteworthy cost for telecommunication operators. The Base Stations (BSs), constituting the main component of wireless infrastructure, are intended to serve their customers during peak time periods. On the other hand, they are more than sufficient when the traffic load is low. Our paper proposes a solution for the problem of inefficient use by using a dynamic BSs switch on/off algorithm. We demonstrate via analysis and simulations that we can achieve significant reduction in energy consumption when we dynamically switch off the unnecessary BSs depending on the traffic variations and the distance between the User Equipments (UEs) and their associated BS. While maintaining the Quality of Service (QoS), we calculate the maximum number of BSs that can be switched off considering the time varying characteristic of the traffic pattern.

The Persistent Relay Carrier Sensing Multiple Access (PRCSMA) Protocol has been proposed in the literature as a Medium Access Control (MAC) protocol suitable for Cooperative Automatic Retransmission Request (C-ARQ) schemes. The improvement in throughput achieved with PRCSMA is already known. In this paper, we analyze the energy efficiency of this protocol to assess application to energy-constrained networks. Towards this aim, a suitable energy model is derived. The model has been validated through computer simulations and shows that a C-ARQ based on PRCSMA can help improve the energy efficiency of wireless communications and thus increase the lifetime of devices.

In this paper we introduce a novel Medium Access Control (MAC) protocol for Automatic Repeat reQuest-based (ARQ-based) cooperative wireless networks. Our protocol coordinates the channel access among a set of relays that use network coding techniques to minimize the number of total transmissions, thus enhancing the performance of the network in terms of Quality of Service (QoS) metrics. The proposed solution is compared to other cooperative schemes, while analytical and simulation results are provided to evaluate our protocol.

In this paper we propose game theoretic Medium Access Control (MAC) strategies for data dissemination scenarios. In particular, we use energy-based utility functions that inherently imply power-awareness, while we consider network coding techniques to eliminate the necessity of exchanging acknowledgement control packets. Simulation results show that our proposed strategies enhance the energy efficiency of the system and reduce the dissemination completion time compared to an optimized standard protocol.

It is necessary to re-think the current access selection polices to the simultaneously available wireless networks, whose number is growing, and the appropriate management mechanisms, to enhance the experience for the final user, for example in terms of QoS and energy efficiency. The IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimize handovers between networks of different technologies (WiMAX, Wi-Fi, 3GPP, etc.). As the integration of multiple wireless interfaces in mobile terminals becomes ordinary it is important to efficiently manage those interfaces. Not only to conveniently provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility by extending the battery life of its mobile terminal. In this paper the IEEE 802.21 standard is used while considering handovers between WiMAX and Wi-Fi, through an implementation in ns-2 introducing a simple, but effective, location-assisted energy saving technique.

In this paper we show how a cooperative Medium Access Control (MAC) protocol can help extend the lifetime of Machine to Machine (M2M) networks. A comparison between non-cooperative Automatic Repeat reQuest (ARQ) schemes (retransmissions performed only from the source) and a cooperative retransmission scheme (all the stations in the network are spontaneous helpers and assist the destination during the communication) in energy-constrained networks is also presented in this work. The results shown in this paper are discussed in terms of the trade off between the network lifetime and the amount of data delivered, leading to the conditions where it is possible to optimize the lifetime of this kind of networks.

In this paper, we propose a switch on/off algorithm for Base Stations (BSs), which exploits the knowledge of the distance between the User Equipments (UEs) and their associated BS. Our novel approach hopes to provide an improvement to the problem of energy consumption. The major concern lies on reducing the energy consumption of the telecommunication networks by optimizing the power utilization without sacrificing the offered Quality of Service (QoS). Our proposed scheme achieves a significant power saving, based on switching off the Base Stations that are underutilized during low traffic periods (especially during night) in the LTE-Advanced.

In this paper we demonstrate that a flatrate policy is an incentive for saving energy in mobile cooperation networks. Inspired by the work of Courcoubetis and Weber about peer-to-peer systems, we present a game theoretic incentive mechanism obtained by providing an interpretation in terms of energy efficiency and coverage. The asymptotic properties of the obtained model support the application of a flatrate policy, which requires every participants to commit to the cooperation scheme a fix contribution in energy for reciprocally roaming communication routes and guarantees to every participant a sufficient incentive for cooperation by an individual average energy win from the scheme. We provide results from simulations which confirm the theory but also show the disrupting effects of actual traffic intensity and routing on game theoretic equilibria, pointing out how these equilibria can be restored.

Multiple Input Multiple Output (MIMO) adds a new dimension, the spatial one, to be optimized in Cognitive Radio (CR) by delivering service simultaneously to more than one user. In this paper, statistical optimization techniques are carried out to assess the Quality of Experience (QoE) performance where each type of users has different demands. The Multiuser scenario is considered where the transmitter can accomplish an Opportunistic Scheduling approach. Closed form expressions are derived for different scenarios and obtained for the QoE indicators in the system regarding the minimum guaranteed throughput and maximum allowed scheduling delay. The primary users and secondary users performance within such scenarios are mathematically formulated and compared their results to simulations.

Femtocells are attracting a fast increasing interest nowadays, as a promising solution to improve indoor coverage, enhance system capacity, and lower transmit power. Technical challenges still remain, however, mainly including interference, security and mobility management, intercepting wide deployment and adoption from mobile operators and end users. This paper describes a novel handover decision policy for the two-tier LTE network, towards reducing power transmissions at the mobile terminal side. The proposed policy is LTE backward-compatible, as it can be employed by suitably adapting the handover hysteresis margin with respect to a prescribed SINR target and standard LTE measurements. Simulation results reveal that compared to the widely-adopted strongest cell policy, the proposed policy can greatly reduce the power consumption at the LTE mobile terminals, and lower the interference network-wide.

Generalized Space Shift Keying (GSSK) modulation is a recently proposed low-complexity concept for Multiple-Input- Multiple-Output (MIMO) wireless systems. GSSK modulation is a generalized version of Space Shift Keying (SSK) modulation, which provides a better spectral efficiency through multiple active antennas at the transmitter. An apparent weakness of GSSK modulation is that it does not exploit the transmit-antennas to achieve transmit-diversity. In this paper, we propose a precoding method for GSSK modulation, which simultaneously achieves high diversity and coding gains. The solution is based on: i) \emph{co-phasing} the active antennas of each spatial-constellation point; and ii) properly \emph{rotating} the phases among spatial-constellation points. The new scheme requires Channel State Information at the Transmitter (CSIT), i.e., the channel phases of each wireless link, which can be obtained through a feedback channel. For the case of a perfect feedback channel, we analytically show that for three and four antennas at the transmitter a full transmit diversity can be achieved without reducing the achievable rate. Furthermore, for various MISO configurations and achievable rates we show through Monte Carlo simulations that our proposed scheme outperforms state-of-the-art open- loop GSSK schemes, in terms of both diversity and coding gain, when the number of bits allocated for the quantization of each channel phase is between 2 and 4.

The growing presence of concurrent heterogeneous wireless access networks, together with the increasing service demands from the end-users, require re-thinking of current access selection polices and appropriate management mechanisms, namely concerning quality of service, energy efficiency, etc. The recent IEEE 802.21 standard introduces link layer intelligence as well as related network information to upper layers in order to optimise handovers between networks of different technologies, such as WiMAX, Wi-Fi and 3GPP. With the massification of mobile terminals with multiple wireless interfaces it is important to efficiently manage those interfaces not only to appropriately provide the requested services to the user, but also to do that in an energy efficient way in order to allow higher mobility to the user by extending the battery life of its terminal. The study the IEEE 802.21 standard is briefly introduced, presented in the signalling in a handover between WiMAX and Wi-Fi, and exploited through an implementation in ns-2 introducing a simple, but effective, energy-saving approach.

We demonstrate for the first time an analytical model for computing the saturation throughput of a Medium-Transparent MAC protocol in 60 GHz Radio-over-Fiber networks, assuming a finite number of terminals and ideal channel conditions. The model takes into account contention both at the optical and the wireless layer, effectively incorporating the Medium-Transparent MAC mechanism for seamless and dynamic capacity allocation over both optical and wireless transmission media. Based on this model, we provide an extensive saturation throughput performance analysis of the Medium Transparent MAC protocol for various optical capacity availability scenarios and different numbers of wireless users. The theoretical results are found to be in good agreement with respective simulation-based findings, confirming that the employment of Medium Transparent MAC protocols can allow for efficient extended LAN operation of 60 Radio-over-Fiber networks.

Accurate representation of the physical layer is instrumental for a sound design and optimization of Medium Access Control (MAC) protocols for cooperative wireless networks. However, the vast majority of MAC protocols are designed and analyzed by considering simplified physical layer and channel models, which often lead to too optimistic performance predictions. In particular, even though many experimental activities have showcased the important role played by shadow-fading, most protocols are designed and evaluated by taking into account only the transmission distance (circular coverage model) or only the fast-fading. Motivated by the proved unsuitability of these models, the contribution of this paper is threefold: i) we provide important considerations on how to adequately include the effect of shadowing into the design of MAC protocols for cooperative networks; ii) we provide an analytical framework to determine the subset of active relays in order to meet a given Quality-of- Service (QoS) requirement; and iii) we study, through analysis and simulation, the performance of a promising MAC protocol for cooperative networks, which is called Persistent Relay Carrier Sensing Multiple Access (PRCSMA), by explicitly taking into account the effect of shadowing. Our study shows that shadowing can dramatically change system and protocol performance.

In cognitive wireless ad-hoc networks the cooperative sensing achieves more accurate results than non-cooperative ones. However, it also exposes the network to attacks. In this paper an algorithm based on the non-parametric Kruskal-Wallis test is proposed to detect malicious users without having any a priori knowledge. The algorithm makes use of the existing statistical differences between the cooperative decision and the non-cooperative decisions. Once an attack of malicious users is detected, the Conover-Inman post-hoc method is used to discern between honest and malicious users.

We present in this paper the evaluation of the energy consumption of PRCSMA, an 802.11-based medium access control protocol designed to coordinate the retransmissions from the relays in a wireless network implementing a Cooperative Automatic Retransmission Request (C-ARQ) scheme. A comparison in terms of energy efficiency with non-cooperative ARQ schemes (retransmissions performed only from the source) and with ideal C-ARQ (with perfect scheduling among the relays) is included in this paper. The main results show the conditions under which a C-ARQ scheme with PRCSMA outperforms, in terms of energy efficiency, non-cooperative ARQ schemes and also show that the overhead of the MAC layer cannot be neglected in order to accurately evaluate the performance of a C-ARQ scheme.

In this paper we introduce a novel Automatic Repeat reQuest (ARQ) scheme for cooperative wireless networks. Our scheme adopts network coding techniques in order to enhance the total bandwidth of the network by minimizing the total number of transmissions. The performance of the proposed approach is evaluated by means of computer simulations and compared to other cooperative schemes, while an analytical solution is provided to validate the results.

Multiple Input Multiple Output (MIMO) adds a new dimension to be exploited in Cognitive Radio (CR) by simultaneously serving several users. The spatial domain that is added through MIMO is another system resource that has to be optimized, and shared when possible. In the current paper, we present a spatial sharing that is carried out through Zero Forcing beamforming (ZFB). The power consumption in such a scenario is mathematically formulated and compared to single user case, to check the feasibility of employing spatial cognition from the power's perspective. Interesting conclusions are obtained about the utility of spatial cognition thanks to the derived closed form expressions for the data rate and consumed power. To provide a comprehensive measure of the expediency of spatial cognitive scenario, a joint power and rate metric is also proposed and analyzed.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

Current mobile devices are equipped with multi-standard interfaces to fully utilize both the existing and the imminent Public Land Mobile Network infrastructure. This flexibility comes with significant energy consumption overheads at the mobile terminal side and thus, the adopted interface / network selection scheme, a.k.a. Vertical Handover mechanism, should be based on both Quality of Service oriented and energy-efficiency criteria. This paper summarizes the current state of the art on energy-centric vertical handover algorithms, discusses weak aspects of existing approaches and presents a novel context-aware vertical handover framework towards energy-efficiency. Rather than a pure energy-centric vertical handover scheme, this framework is a context-awareness enabler for energy-centric vertical handover decision making.

The emerging 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However there are still many open issues regarding MAC protocol design for MIMO based systems, especially in order to exploit the multiuser capabilities of the MIMO channel. In this paper we propose a novel MAC scheme that considers an opportunistic channel-aware scheduling policy to achieve simultaneous downlink transmissions to multiple users. In an effort to offer a complete and practicable proposal, our MAC scheme is combined with a low-complexity beamforming technique at the Physical layer in a system where multiple antennas are employed at least at the transmitter side (Access Point). A mathematical model for the throughput performance of the proposed scheme is presented and validated through link-layer simulation results.

Cognitive Wireless Ad-Hoc Networks use cooperation in order to sense the spectrum accurately. However, the space diversity provided by the cooperative sensing is only efficient when the sensing users are selected appropriately. This paper proposes a distributed probability-based mechanism that allows the Secondary Users to decide what channels should be sensed by each user in order to reduce the sensing overhead.

In this paper we analyze the performance of a distributed queuing medium access control (MAC) protocol designed to execute cooperative ARQ (C-ARQ) schemes at the MAC layer. Due to the broadcast nature of the wireless channel, a user that receives a data packet with unrecoverable errors can request retransmission from any of the other users in the transmission range of the transmitter that overheard the original transmission. These users can act as spontaneous relays and provide the communication with cooperative diversity gains. Upon retransmission request, the relays have to contend for the access to the channel. The DQCOOP protocol has been proposed in the literature as a high-performance MAC protocol for this kind of scenario. In this paper we theoretically evaluate its performance. The analytical results are supported by computer-based simulation that show the accuracy of the analysis.

In this paper we introduce a connection admission control (CAC) mechanism for IEEE 802.16 broadband wireless access standard. Our scheme is based on the bandwidth reservation concept and has been developed considering the problem of "busy hour" in communications traffic variation during a typical day. The proposed solution, which is compatible to the IEEE 802.16 Standard, provides higher priority to VoIP calls compared to other types of traffic in the network. The performance of the proposed mechanism is evaluated by means of computer simulations and compared to simple traditional admission control schemes, while we provide an analytical solution as well.

We describe in this paper a methodology to facilitate the coexistence of the Distributed Queuing Medium Access Control (MAC) protocol for Ad hoc Networks (DQMAN) with the IEEE 802.11 Standard MAC protocol. By using dual terminals which can operate with the two MAC protocols, it is possible to run a hybrid network with legacy and novel terminals. In addition, it is possible to enable the intercommunication between all the terminals of the network. Computer simulations have been carried out to assess the performance and the feasibility of such dual operation. The main conclusion of the study is that the use of dual terminals allows the integration of DQMAN within legacy networks without degrading the performance of IEEE 802.11 terminals. In addition, the mechanism presented in this paper can be used to enable the coexistence of any cluster-based distributed MAC protocol within IEEE 802.11 networks.

The recently approved 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However, it does not exploit the multiuser capabilities of the MIMO channel. In this paper we present a multiuser downlink transmission scheme that combines low-complexity beamforming at the Physical layer with an opportunistic channel-aware MAC scheduling policy. A mathematical model for the throughput calculation of the proposed scheme is presented and validated through link-layer simulation results.

Multiple Input Multiple Output (MIMO) opens new challenges to be exploited in Cognitive Radio (CR) by simultaneously serving several users. The spatial domain that is added through MIMO is another system resource that has to be optimized, and shared when possible. In this paper, we present a spatial sharing approach that is carried out through Zero Forcing beamforming (ZFB). Statistical assessment of the primary user's (high priority user) performance is carried out through two indicators: Quality of Service (QoS) and Probability of Satisfaction (PoS). Closed form expressions are derived for the primary user's behaviour in the developed scenario and compared its performance to extensive simulations. Interesting results are obtained regarding the number of simultaneous serviced users that can share the spatial domain.

We present in this paper the analysis of an IEEE 802.11 network in the presence of hidden and exposed terminals when a Cooperative ARQ (C-ARQ) scheme is executed at the MAC layer. When spontaneous helpers transmit to assist in a failed transmission, the area exposed to the original transmission increases in comparison to non-cooperative ARQ schemes. In this paper we quantify this effect and we evaluate the relevance of designing efficient protocols to combat the exposed terminal problem.

This paper provides details and performance evaluation of the implementation of the Persistent Relay Carrier Sensing Multiple Access (PRCSMA) protocol for Cooperative ARQ (C-ARQ) scenarios using off-the-shelf wireless cards. The underlying idea of PRCSMA is to modify the basic rules of the IEEE 802.11 MAC protocol to execute a distributed C-ARQ scheme in wireless networks in order to enhance their performance and to extend coverage. For the implementation, we modify the HostAP driver for off-the-shelf air-interfaces. HostAP is a Linux based driver for 802.11b WLAN cards based on Intersil's Prism 2.5. Performance evaluation tests provide proof-of-concept of the efficiency, in terms of mean delay, of the PRCSMA in realistic conditions.

The emerging 802.11n standard establishes the integration of MIMO technology in WLANs with the goal of achieving high data rates. However there are still many open issues regarding MAC protocol design for MIMO based systems, especially in order to exploit the multiuser capabilities of the MIMO channel. In this paper we investigate practical solutions to implement multiuser downlink transmission in infrastructure 802.11n based WLANs. A low-complexity beamforming transmission technique is employed at the physical layer and four MAC schemes that vary in complexity and efficiency are presented and evaluated through computer simulations.

We present in this paper the analytical evaluation of a simple mechanism to reduce the average transmission delay of master stations in a network based on the Distributed Queuing MAC protocol for Ad hoc Networks (DQMAN). When DQMAN is executed, the network is self-organized into dynamic and spontaneous master-slave clusters. Within each cluster, a high performance MAC protocol based on a tree-splitting collision resolution algorithm which uses access minislots is executed. By allowing temporary master stations to avoid contention to get access to the channel, their average packet transmission delay can be effectively reduced compared to that of slaves. This technique provides thus master stations with higher access priority to the channel and, indeed, could be used in any MAC protocol based on access minislots to provide a subset of users with higher priority.

A cross-layer based dynamically tuned queue length scheduler is presented in this paper, for the Downlink of multiuser and multiantenna WLAN systems with heterogeneous traffic requirements. An opportunistic scheduling algorithm is applied, while real time classes are prioritized. A trade-off between the throughput maximization of the system and the guarantee of the QoS requirements is obtained. Therefore the length of the queue is dynamically tuned to select the appropriate conditions based on the operator requirements.

In this paper we describe a new MAC protocol for ad hoc networks called distributed point coordination function (DPCF). It constitutes an extension of the PCF of the IEEE 802.11 standard to operate over infrastructureless networks. It combines both the operation of DCF and PCF. Users get access to the channel through the DCF function. However, once they get access to the channel, they establish a virtual cluster where the PCF coordination function can be executed. The performance of DPCF in single-hop and multi-hop ad hoc networks has been evaluated by means of link-level computer simulation. The results show that DPCF outperforms DCF in terms of throughput and average transmission delay when executed in networks without infrastructure.

Body sensors consume a significant amount of power when data is either transmitted or received. This paper evaluates the energy efficiency per utile bit as a function of the network load and data frame length while introducing power management solutions for a high-performance distributed queuing medium access control (DQ MAC) protocol in wireless body sensor networks. To do so, a new energy analytical model derived from delay theoretical studies is provided. Simulation results are displayed to further validate the protocol energy performance using IEEE 802.15.4 system parameters. The achieved outcome shows that the proposed scheme outperforms IEEEperformance using IEEE 802.15.4 system parameters. The achieved outcome shows that the proposed scheme outperforms IEEE 802.15.4 MAC in all possible scenarios.

Wireless body sensor networks (BSNs) in healthcare systems operate under conflicting requirements. These are the maintenance of the desired reliability and message latency of data transmissions, while simultaneously maximizing battery lifetime of individual body sensors. In doing so, the characteristics of the entire system, including physical, medium access control (MAC), and application layers have to be considered. The aim of this paper is to develop a new MAC model for BSNs to fulfill all these specific rigorous requirements under realistic medical settings. For that purpose, a novel cross-layer fuzzy-rule scheduling algorithm and energy-aware radio activation policies are introduced. The main idea is to integrate a fuzzy-logic system in each body sensor to deal with multiple cross-layer input variables of diverse nature in an independent manner. By being autonomously aware of their current condition, body sensors are able to demand a "collision-free" time slot, whenever they consider it strictly required (e.g. high system packet delay or low body sensor residual battery lifetime). Similarly, they may refuse to transmit, if there is a bad channel link, thus permitting another body sensor to do so. This results in improving the system overall performance. The proposed MAC model is evaluated by computer simulations in terms of quality of service and energy consumption under specific healthcare scenarios.

The Persistent Relay Carrier Sensing Multiple Access (PRCSMA) has been recently presented in the literature as a novel Medium Access Control (MAC) protocol based on the IEEE 802.11 and modified to cope with the contention among the relays in a Cooperative ARQ (C-ARQ) scheme. We present in this paper a theoretical model to calculate the average cooperation delay of PRCSMA. Computer simulations have been carried out to assess the accuracy of the model.

DQCA is a near-optimum medium access control (MAC) protocol designed for wireless local access networks (WLANs). The protocol operates as a random access scheme under low traffic load and switches smoothly and automatically to a reservation scheme as the traffic grows. The inherent feedback mechanism of DQCA facilitates the implementation of a link adaptation algorithm that properly selects the best transmission rate taking into account the state of the wireless channel. This paper presents an analytical model for the evaluation of the performance in terms of throughput and mean message delay in the presence of a time-variant channel response.

A throughput analysis of the Persistent Relay Carrier Sensing Multiple Access (PRCSMA) protocol is presented in this paper. The protocol has been designed to execute a cooperative ARQ (C-ARQ) scheme in wireless networks. The model herein presented takes into account the contention time among the relays. This time consists in wasted time due to both backoff deferral periods and collisions occurred between relays attempting to access the channel in a same time slot. Link-level computer simulations have been carried out to assess the accuracy of the model and to evaluate the performance of the protocol.

Several clustering algorithms have been proposed during the last decade within the context of ad hoc and sensor networks. They allow adding some organization to an otherwise disorganized network. Although the virtual organization attained by clustering has been typically designed within the scope of routing algorithms, clusters can also enable the use of conventional highly efficient medium access control (MAC) protocols to support heavy traffic loads. In this paper we adopt a novel approach to calculate the saturation throughput of a single- hop ad hoc network when using a MAC protocol embedded in a passive clustering mechanism based on the Carrier Sensing Multiple Access (CSMA) protocol. The DQMAN protocol is considered as a study case to validate the proposal.

The distributed queuing MAC protocol for cooperative networks (DQCOOP) is presented in this paper as an innovative MAC protocol designed to coordinate the relay retransmissions in a cooperative automatic retransmission request (C-ARQ) scheme for wireless networks. The distributed cooperative ARQ scheme when the DQCOOP is applied at layer two is compared to an ideal non-cooperative ARQ scheme. Computer simulations show that the relay multiple access problem is efficiently handled by the DQCOOP and the cooperative approach outperforms the traditional non-cooperative ARQ scheme in all cases. In addition, the DQCOOP is compared with an IEEE 802.11-based MAC protocol for C-ARQ schemes, demonstrating improved efficiency.

Voice transmission over WLAN and cross-layer optimization are emerging research topics. This paper proposes two novel cross-layer mechanisms for a WLAN system where the offered traffic is of mixed nature (voice and data) while using a distributed MAC protocol. On one hand, the performance of the proposed mechanisms has been analyzed, and a significant system efficiency improvement in terms of throughput and packet delay for data nodes is shown. On the other hand, a remarkable increase in the number of simultaneous possible voice nodes is also achieved. These benefits are obtained by means of a PHY-MAC dialogue and a smart scheduling of the radio resources. Transmissions are scheduled taking into account the measurements of the channel state and the waiting time of packets in the accessing system. The two proposed mechanisms have different performance behavior as they present a trade-off between system enhancement and fairness in resource assignment among the nodes. The obtained results emphasize the advantages of the proposed schemes and the importance of Cross-Layer design in wireless communication systems.

We consider a cooperative network where the communication between a single source-destination pair is assisted by the transmission of relays. In the considered cooperative model, once a node requires cooperation, it broadcasts a claim for cooperation message. Under high mobility conditions or in some special scenarios this is the natural procedure to discover the potential relays. However, when there is a large number of nodes that can potentially serve as a relay, a Multiple Relay Access Control (MRAC) problem turns up and the relay transmissions must be organized in an effective manner. We introduce this problematic and propose three different strategies to manage this severe competitive channel. In these strategies either the source, the destination or both trigger the cooperative process. The methodology employed makes the results and conclusions valid for a wide range of cooperative scenarios, and hence, useful for a wide range of applications.

Cross-layer opportunistic scheduling on DQCA-based WLAN systems yields high performance results. This paper emphasizes the need for accurate channel state information (CSI) and presents an enhanced mechanism that provides frequent CSI updates. The proposed idea consists in introducing special update frames specifically dedicated to channel state estimation. The impact of this mechanism on the performance of the system is evaluated through simulations. The paper shows that there is a trade-off between the overall system performance and the number of update frames that are used. The periodicity of these special frames is a parameter that needs to be carefully selected, depending on the channel time-variation characteristics.

Significant power is consumed at a sensor node when it either transmits a packet or when it receives a packet. In this paper, we study energy efficiency as a function of the number of sensors in the network and the payload length. Additionally, we propose power management solutions based on a distributed queuing MAC protocol for wireless sensor networks. Analytical values for the energy consumption performance are derived as a function of the system parameters. The obtained results show that our proposed MAC scheme outperforms that of IEEE 802.15.4. These benefits are obtained from eliminating back-off periods and collisions in data packet transmissions while minimizing the control overhead and the overall energy consumption

It has been shown that the cooperation among nodes can improve the performance of a network under certain considerations. So far, research focus has been mainly put on cooperation and not on the coordination required to obtain this cooperation. In this paper we discuss how the MAC layer plays a key role in determining the effectiveness of a cooperative orthogonal multiple relay channel. We propose and analyze the performance of a novel MAC protocol based on the legacy IEEE 802.11 standard in a representative and general study case scenario. The main conclusion of the study is that a proper selection of the MAC protocol being used in a cooperative system is highly relevant in order to actually achieve the benefits of this kind of systems

Cross-Layer optimization is an emerging research topic. This paper proposes two novel cross-layer mechanisms to be included in a WLAN system where a near-optimum distributed MAC protocol is used. Their performance in terms of throughput and packet delay is analysed, and a significant system efficiency improvement is shown. This benefit is achieved by means of a PHY-MAC dialogue and a smart scheduling of the radio resources. Transmissions are scheduled taking into account the measurements of the channel state and the waiting time of packets in the accessing system. The two proposed mechanisms have different performance behaviour and thus present a trade-off between system enhancement and fairness. The obtained results emphasize the advantages of the proposed schemes and the importance of cross-layer design in wireless communication systems

This paper proposes a cross-layer priority-based scheduling algorithm for WLAN systems using the DQCA MAC protocol where heterogeneous voice-data traffic is present. In this algorithm voice users have always the highest priority while among the data users, higher priority is given to the user with higher measured signal-to-noise ratio. Therefore, each user transmits with the maximum corresponding available rate. The performance of the proposed algorithm is compared to those without cross-layer and with the basic DQCA operation without any service distinction. Simulation results show that this enhanced scheme improves the efficiency of the system in terms of data throughput and voice users' capacity. Furthermore, for a given number of voice users, a higher offered data load is supported