As network complexity escalates, there is an increasing need for more sophisticated methods to manage and operate these networks, focusing on enhancing efficiency, reliability, and security. A wide range of Artificial Intelligence (AI)/Machine Learning (ML) models are being developed in response. These models are pivotal in automating decision-making, conducting predictive analyses, managing networks proactively, enhancing security, and optimizing network performance. They are foundational in shaping the future of networks, collectively forming what is known as Network Intelligence (NI). Prominent Standard-Defining Organizations (SDOs) are integrating NI into future network architectures, particularly emphasizing the closed-loop approach. However, existing methods for seamlessly integrating NI into network architectures are not yet fully effective. This paper introduces an in-depth architectural design for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a novel end-to-end NI orchestrator that supports closed-loop NI operations across various network domains. The primary goal of this design is to streamline the deployment and coordination of NI throughout the entire network infrastructure, tackling issues related to scalability, conflict resolution, and effective data management. We detail exhaustive workflows for managing the NI lifecycle and demonstrate a reference implementation of the NI Stratum, focusing on its compatibility and integration with current network systems and open-source platforms such as Kubernetes and Kubeflow, as well as on its validation on real-world environments. The paper also outlines major challenges and open issues in deploying and managing NI.

The native integration of AI and ML algorithms in the next-generation mobile network architecture will allow for meeting the expectations of 6G. This aspect is targeted by the DAEMON project, which proposed a solution to natively manage Network Intelligence (NI) through novel architectural elements and procedures. In this paper, we discuss how NI solutions based on AI and ML can leverage NI native procedures implemented by the NI Orchestrator to improve their lifecycle management. We also discuss how the architectural procedures can be implemented in practice, using state-of-the-art software components.

As research in mobile networks is already transitioning from 5G to 6G, we identify a set of fundamental barriers in the current 5G architecture that limit efficient and global operations. We propose innovative architectural solutions that can remove such barriers and lay the foundation for 6G systems. Specifically, we introduce three novel architectural components: the Global Service-Based Architecture (GSBA), the Compute Continuum Layer (CCL), and the Zero-Trust Layer (ZTL). These components collectively aim to enhance network efficiency, security, and scalability, addressing future mobile networks' dynamic and demanding needs. Furthermore, we discuss the integration of Network Intelligence (NI) that exploits the afore-mentioned architectural innovations to ensure global operations and services. Ultimately, our proposed vision entails a more adaptive, secure, and intelligent network architecture, setting the groundwork for the next generation of mobile networks.

The quest for autonomous mobile networks introdu-ces the need for fully native support for Network Intelligence (NI) algorithms, typically based on Artificial Intelligence tools like Machine Learning, which shall be gathered into a NI stratum. The NI stratum is responsible for the full automation of the NI operation in the network, including the management of the life-cycle of NI algorithms, in a way that is synergic with traditional network management and orchestration framework. In this regard, the NI stratum must accommodate the unique requirements of NI algorithms, which differ from the ones of, e.g., virtual network functions, and thus plays a critical role in the native integration of NI into current network architectures. In this paper, we leverage the recently proposed concept of Network Intelligence Orchestrator (NIO) to (i) define the specific requirements of NI algorithms, and (ii) discuss the procedures that shall be supported by an NIO sitting in the NI stratum to effectively manage NI algorithms. We then (iii) introduce a reference implementation of the NIO defined above using cloud-native open-source tools.

This work contributes towards optimizing edge analytics in Mobile Edge Computing (MEC) systems. We consider requests for computing tasks that are generated from users and can be satisfied either locally at their devices, or they can be offloaded to an edge server in their proximity for remote execution. We study a multi-user MEC system with limited energy autonomy for the mobile devices and with limitations on the computing capability of both mobile devices and at an edge server, where users can offload part of their computation load. We define a utility over 'resource residuals', that capture the difference between the resources assigned through our decisions, and those needed in practice, and we aim at the minimization of regret, i.e., of the difference between the utility obtained by an optimal offline benchmark that knows the system evolution in hindsight, and our online decision policy. We design an algorithm that jointly learns policies for offloading computations and scheduling them for execution at the shared MEC server. We prove that our algorithm is asymptotically optimal, i.e., it has no regret over the optimal static offline benchmark, and that its performance is independent of the number of devices in the system. From our numerical evaluation we conclude that our algorithm adapts to unpredictable demand changes, it learns to identify resource-limited devices, and it learns to share the server's resources.

Industrial Internet of things (IIoT), one enabler for Industry 4.0 Smart Factories, is a mission-critical and latency-sensitive application of 5G networks. Due to the stringent latency requirements in IIoT, coordinating the simultaneous transmissions of massive entities and knowing the interference they create to each other is not feasible. Additionally, due to the mobility feature of mobile robots and automated guided vehicles, the experienced channel fading may differ from the estimated one. Therefore, some uncertainties exist in IIoT networks while we decide the communication and control mechanisms. Within the context of IIoT, this paper discusses some resource allocation solutions from the perspective of Online Convex Optimization (OCO). OCO is a computationally lightweight and memory-efficient mathematical tool which tackles the optimization problems, given that the network environment is arbitrary and unknown. We first introduce the key performance indicators in IIoT networks and highlight the uncertain factors, which we may encounter while allocating the communication resources in IIoT. Then we provide an overview of main principles of OCO and present the comparison benchmarks and related metrics for performance evaluation. Moreover, we discuss the kind of resource allocation problems in IIoT that can be tackled by OCO. Finally, we summarize the advantages of applying OCO to IIoT networks.

The present paper examines how the Chinese economy has managed to maintain its overall economic growth, and therefore its production, throughout the various crises, that is, the global crisis of 2007 and the E.U. structural crisis. The paper sets up a network-global vector autoregressive (GVAR) model that is estimated simultaneously for all the economies in the model, capable of uncovering the dominant entities in the network system, in the time period 1992-2017. According to our findings, the economies of the United States and EU17 are found to exhibit a dominant role in the GVAR system, which is confirmed using both node theory as well as econometric techniques. The results show that the Chinese economy is unaffected, in the long run, by unanticipated shocks in the dominant economies of the United States and EU17.

We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of α-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of cost-efficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.

Content caching has been experiencing revived interest within the context of current and next generation wireless networks. It brings content closer to users, decreasing the aggregate delivery costs and service delays for network providers. Recommender systems have become integral components of content provision sites. They offer personalized recommendations, increasing the individual user satisfaction and engagement with the content provider’s platform. Traditionally, caching and recommendation decisions are taken separately. However, there is a recent persistent trend where both network and content providers tend to deploy their own content delivery solutions. In light of this, we explore how the phenomenally conflicting objectives of content caching and recommendation can be jointly addressed. In this chapter we approach recommender systems as network traffic engineering tools that actively shape content demand to serve both user- and network-centric performance objectives. We introduce a model that captures the coupling between caching decisions and issued recommendations. Based on experimental evidence, we describe the impact of recommendations on user content requests and present a systematic way of engineering the user recommendations. Our viewpoint to recommender systems raises some concerns, not least ethical ones. Hence, we introduce a measure called preference distortion tolerance to quantify how much the engineered recommendations distort the original user content preferences. We describe the “recommendation window” as a way to controllably bound the distortion that recommendations will undergo and discuss specific properties that the recommender system should have in order to ensure that the “Quality of Recommendations (QoR)" is kept high by the issued recommendations. We formulate a joint optimization problem for the content to cache and recommend to each user, aiming to maximize the cache hit ratio. The preference distortion tolerance is embedded as a constraint to this problem formulation and marks an equally important dimension for assessing possible solutions. We prove that the problem lacks those properties that would guarantee its approximability, and devise a low-complexity practical algorithm that solves it efficiently. The algorithm is essentially a form of lightweight control over user recommendations, so that the recommended content is both appealing to the end-user and more friendly to the caching system and the network resources. We thoroughly evaluate it, by establishing its main properties and analytical performance bounds. We conduct an extensive sensitivity analysis over various system parameters, both analytically and through simulations with real and synthetic dataset. Next we extend our model and approach for a system in which users can access content from multiple caches, and analyse the intermediate joint caching and user association problem. We then discuss open directions and provide a detailed taxonomy of the related work. Due to space limitations, we do not provide proofs for our results. However, the interested reader may refer to [1-3] for more propositions and their detailed proofs.

Caching at the edge of the radio network is increasingly viewed as a promising countermeasure to the staggering demand for mobile video content. The persistent orientation of newer generations of mobile communication systems towards lower latency and faster radio access speeds only strengthens the arguments in its favor. When content caching is coordinated with other radio resource management functions, in particular, the benefits for the end users and the network operator are significant. In this paper, we investigate these benefits in cache-enabled small cell networks that jointly control (i) the Small-Cell Base Stations (SBSs) that serve as network access points for the mobile users; and (ii) the content that is stored at the SBS co-located caches. Our main contribution is a fast-converging computationally simple heuristic algorithm that iterates between assigning users to small cells and content to SBS caches, to maximize the overall cache hit ratio. The algorithm solutions compete with the optimal assignments at small problem instances and outperform alternative solutions for larger instances, especially when the content demand exhibits spatial locality. Combining good performance with non-prohibitive complexity, the algorithm could become a valuable tool for small cell network operators seeking to optimize the use of radio network resources.

The ever-growing trend of mobile users to consume high quality videos in their devices has pushed the backhaul network to its limits. Caching at Small-cell Base Stations (SBSs) has been established as an effective mechanism for alleviating this burden. Next generation mobile networks promise high Access Network density, hence multiple SBS association options per mobile user may exist. In this paper, we study the joint problem of mobile user association to SBSs and content placement to SBS-collocated caches, aiming to further optimize the utilization of backhaul network. The problem is solved periodically, considering time intervals where the users' location to the system is assumed to be fixed. First, we decompose the joint problem into two sub-problems that are solve sequentially, namely the content preference similarity-driven user association and the demand aware content placement sub-problems. We then propose a heuristic that consists of multiple phases. In particular, at a preparation phase, it performs clustering of users based on the similarity of their content preferences, accounting also for geographical constraints. The resulting clusters are then utilized for the demand-aware association of users to the SBS, while the placement of content is driven by the resulting local demand in each SBS, and takes place at the end. We demonstrate the effectiveness of our heuristic by evaluating its performance against multiple schemes that either lack a preparation phase or do not account for geographical constraints. As it is evident through the numerical results, the user clustering that takes place during the preparation phase can increase the overall cache hit ratio up to 20%.

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.

In this paper, we investigate the performance gains that are achievable when jointly controlling (i) in which Small-cell Base Stations (SBSs) mobile users are associated to, (ii) which content items are stored at SBS co-located caches and (iii) which content items are recommended to the mobile users who are associated to different SBSs. We first establish a framework for the joint user association, content caching and recommendations problem, by specifying a set of necessary conditions for all three component functions of the system. Then, we provide a concrete formulation of the joint problem when the objective is to maximize the total hit ratio over all caches. We analyze the problems that emerge as special cases of the joint problem, when one of the three functions is carried out independently, and use them to characterize its complexity. Finally, we propose a heuristic that tackles the joint problem. Proof-of-concept simulations demonstrate that even this simple heuristic outperforms an optimal algorithm that takes only caching and recommendation decisions into account and provide evidence of the achievable performance gains when decisions over all three functions are jointly optimized.