Computer Science

Mobile communication via high-altitude platforms operating in the stratosphere is an idea that has been on the table for decades. In the past few years, however, with recent advances in technology and parallel progress in standardization and regulatory bodies like 3GPP and ITU, these ideas have gained considerable momentum. In this article, we present a comprehensive overview of HIBS - High Altitude Platform Stations as IMT Base Stations. We lay out possible use cases and summarize the current status of the development, from a technological point of view as well as from standardization in 3GPP, and regarding spectrum aspects. We then present preliminary system level simulation results to shed light on the performance of HIBS. We conclude with pointing out several directions for future research.

The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number of users, applications, and services that are highly distributed with distinct and heterogeneous communications requirements. Resource allocation in this context requires dynamic, efficient, and customized solutions and Bandwidth Allocation Models (BAMs) are an alternative to support this new trend. This paper proposes the BAMSDN (Bandwidth Allocation Model through Software-Defined Networking) framework that dynamically allocates resources (bandwidth) for a MPLS (MultiProtocol Label Switching) network using a SDN (Software-Defined Networking)/OpenFlow strategy with BAM. The framework adopts an innovative implementation approach for BAM systems by controlling the MPLS network using SDN with OpenFlow. Experimental results suggest that using SDN/OpenFlow with BAM for bandwidth allocation does have effective advantages for MPLS networks requiring flexible resource sharing among applications and facilitates the migration path to a SDN/OpenFlow network.

In order to meet the ever-increasing demand for high throughput in WiFi networks, the IEEE 802.11ax (11ax) standard introduces orthogonal frequency division multiple access (OFDMA). In this letter, we address the station-resource unit scheduling problem in downlink OFDMA of 11ax subject to minimum throughput requirements. To deal with the infeasible instances of the constrained problem, we propose a novel scheduling policy based on weighted max-min fairness, which maximizes the minimum fraction between the achievable and minimum required throughputs. Thus, the proposed policy has a well-defined behavior even when the throughput constraints cannot be fulfilled. Numerical results showcase the merits of our approach over the popular proportional fairness and constrained sum-rate maximization strategies.

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working towards the standardization of MEC in 5G networks and the corresponding solutions for routing user traffic to applications in local area networks. Nevertheless, there are neither practical implementations for dynamically relocating applications from the core to a MEC host nor from one MEC host to another ensuring service continuity. In this paper we propose a solution based on Software-Defined Networking (SDN) to create a new instance of the IP anchor point to dynamically redirect User Equipment (UE) traffic to a new physical location (e.g. an edge infrastructure). We also present a novel approach that leverages SDN to replicate the previous context of the connection in the new instance of the IP anchor point, thus guaranteeing Session and Service Continuity (SSC), and compare it with alternative state replication strategies. This approach can be used to implement edge services in 4G or 5G networks.

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching

Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -- from a theoretical and a practical viewpoint -- the main energy efficiency enabling features that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key technology features, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -- and the status on -- green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.

Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane application programming interfaces (APIs) which may be leveraged by user-defined software-defined networking (SDN) control. This offers great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community, and it is supported by various software and hardware platforms. In the first part of this paper we give a tutorial of data plane programming models, the P4 programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we categorize a large body of literature of P4-based applied research into different research domains, summarize the contributions of these papers, and extract prototypes, target platforms, and source code availability. For each research domain, we analyze how the reviewed works benefit from P4's core features. Finally, we discuss potential next steps based on our findings.

The performance of data delivery in wireless relay networks (WRNs), such as delay-tolerant networks and device-to-device communications heavily relies on the cooperation of mobile nodes (i.e., users and their carried devices). However, selfish nodes may refuse to relay data to others or share their resources with them due to various reasons, such as resource limitations or social preferences. Meanwhile, misbehaving nodes can launch different types of internal attacks (e.g., blackhole and trust-related attacks) to disrupt the normal operation of the network. Numerous mechanisms have been recently proposed to establish secure and efficient communications in WRNs in the presence of selfish and malicious nodes (referred as non-cooperative WRNs). In this paper, we present an in-depth survey on human-centric communication challenges and solutions in the non-cooperative WRNs that focuses on: (1) an overview of the non-cooperative WRNs and introduction to various types of node selfish and malicious behaviors, (2) the impact analysis of node selfish and malicious behaviors on the performance of data forwarding and distribution, (3) selfish and malicious node detection and defense systems, and (4) incentive mechanisms. Finally, we discuss several open problems and future research challenges.

Benefiting from the usage of the high-frequency band, utilizing part of the large available bandwidth for wireless backhauling is feasible without considerable performance sacrifice. In this context, integrated access and backhaul (IAB) was proposed by 3GPP to reduce the fiber optics deployment cost of 5G and beyond networks. In this paper, we first give a brief introduction of IAB based on the 3GPP release. After that, we survey existing research on IAB networks, the integrations of IAB to cache-enabled network, optical communication transport network, and the non-terrestrial network. Finally, we discuss the challenges and opportunities that might arise while developing and commercializing IAB networks.

Internet of Things (IoT) is an innovative paradigm envisioned to provide massive applications that are now part of our daily lives. Millions of smart devices are deployed within complex networks to provide vibrant functionalities including communications, monitoring, and controlling of critical infrastructures. However, this massive growth of IoT devices and the corresponding huge data traffic generated at the edge of the network created additional burdens on the state-of-the-art centralized cloud computing paradigm due to the bandwidth and resources scarcity. Hence, edge computing (EC) is emerging as an innovative strategy that brings data processing and storage near to the end users, leading to what is called EC-assisted IoT. Although this paradigm provides unique features and enhanced quality of service (QoS), it also introduces huge risks in data security and privacy aspects. This paper conducts a comprehensive survey on security and privacy issues in the context of EC-assisted IoT. In particular, we first present an overview of EC-assisted IoT including definitions, applications, architecture, advantages, and challenges. Second, we define security and privacy in the context of EC-assisted IoT. Then, we extensively discuss the major classifications of attacks in EC-assisted IoT and provide possible solutions and countermeasures along with the related research efforts. After that, we further classify some security and privacy issues as discussed in the literature based on security services and based on security objectives and functions. Finally, several open challenges and future research directions for secure EC-assisted IoT paradigm are also extensively provided.