Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays

Agent-Based Modeling for Distributed Decision Support in an IoT Network

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-of-the-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and next-generation wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human- and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brain-machine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor

Designing for the Future: A Complex Systems Approach to Communication Networks

The network size and the deployment density of wireless networks continue to increase from year to year. Additionally, networks are experiencing an operational shift that affects both: (1) the network architecture, and (2) the service implementation. The network architecture is changing from a traditionally rigid hierarchical - hardware first - to a more flat and flexible - software first - implementation. This shift enables innovation related to, among other things, network infrastructure ownership, dynamic resource sharing, on-demand resource allocation. The evolution of the network architecture underpins the shift related to the service implementation. New services have strict requirements (e.g. latency, throughput, reliability), dynamically demanding resources on a more granular level. The growing size and operational changes demand scalability and adaptability to be part of the network design. Not only are individual networks becoming more sophisticated, but it is increasingly infeasible to consider any one kind of network in isolation; networks such as cellular, Wi-Fi, vehicular and IoT, increasingly have interdependencies. In addition, many subsets of networks are no longer centrally planned and rolled-out by a single owner, but evolve over time based on user deployed infrastructure. The network can be viewed as a living organism, that evolves over time and adapts to the changes in its environment. Focusing on making nodes more capable and intelligent as done to date by the cognitive radio community often results in higher cost, limiting the scalability of the adopted techniques. In this thesis, we propose a complex systems science approach to communication networks. We focus on three complex systems principles, i.e. complexity, degeneracy and emergence. Additionally, we divide the complex systems tools and techniques into three layers, i.e. analysis, modeling and design. The problem that we address in this thesis can be stated in the form of the following research question: "What does the analysis of the micro-scale structures tell us about the macro-scale performance, and how do the local interaction rules lead to global organization/synchronization in communication networks?" Our first step was to identify what aspects of the network (e.g. infrastructure, network functions, signal processing) could be analyzed in the context of complex systems. The focus is on network functions (e.g. clustering in WSN, frequency allocation in cellular networks, allowing us to have a better understanding of the impact that different protocol procedures have on the network operation. The research question can be broken down into three parts, which drove the development of the thesis: 1. How to quantify the impact that micro-scale structures have on the macro-scale performance of communication networks? 2. How to identify macro-scale/system-level topologies that are constructed out of diverse micro-scale structures (i.e. degenerate structures)? 3. How to achieve global organization through limited knowledge and local interactions? We start with a discussion of different tools and techniques that have been developed and applied to understand unexplored system properties (e.g. the capability of a system to store, communicate and process information) in sciences like physics, neurobiology, urbanism, social networks, etc. We address the above-mentioned questions by resorting to a wide range of tools for analysis (e.g. network science, information theory, statistical mechanics), modeling (e.g. Equation-Based Modeling, Agent-Based Modeling) and design (e.g. Reference Design, Trial and Error, Analytical Approach). We also discuss the evolution of communication networks, showing that the need for flexibility, scalability and adaptability demands new approaches to analysis, modeling and design. We then propose a framework to model the underlying structure of network functions with graphs, allowing us to study the organizational characteristics of network functions as complex systems. In order to quantify the impact that micro-scale structures have on the macro-scale performance, we propose a complexity metric called functional complexity. This allows us to correlate the organizational structure to the performance of different network functions (e.g. frequency allocation in cellular networks, clustering in WSN). Then we shift our focus from complexity to degeneracy and reapply the same modeling approach, i.e. modeling network functions with graphs. Here we focus on the identification of macro-scale/system-level topologies that are constructed out of diverse micro-scale structures in IoT networks by studying degeneracy, i.e. the multiplicity of computational graphs that allow us to perform the same computation by using different subgraph structures. Finally, we move on to the third layer, i.e. design. We design local rules of interaction between base stations in a cellular network that emerge in a desirable global property, i.e. formation of handover regions that minimize the signaling and latency related to mobility management. Here, we apply the tools from all three layers, i.e. analysis, modeling and design, showing the full potential and benefits of the application of complex systems tools to design scalable, adaptive and robust network functions for future communication networks