Over the last few years, the analysis and modeling of networks as well as the analysis and modeling of networked dynamical systems, has attracted considerable interdisciplinary interest, especially using the complex systems theory. These efforts are driven by the fact that systems, as diverse as genetic networks or the Internet can be effectively described as complex networks. Contrary, despite the unprecedented evolution of technology, basic issues and fundamental principles related to the structural and evolutionary properties of communication networks still remain largely unaddressed. The situation is even more complicated when we attempt to model the mobile communication networks and especially the 5th generation (5G) and eventually the forthcoming 6th generation (6G). In this work, we attempt to review basic models of complex networks from a communication networks perspective, focusing on their structural and evolutionary properties. Based on this review we aim to reveal the models of complex networks, that may apply when modeling the 5G and 6G mobile communication networks. Furthermore, we expect to encourage the collaboration between complex systems and networking theorists toward meeting the challenging demands of 5G networks and beyond.

Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized.

Next-generation mobile networks (such as Fifth-Generation (5G), and Sixth-Generation (6G)) are envisioned to undergo an unprecedented transformation from connected things to connected intelligence with more stringent characteristics, i.e., low end-to-end latency, high bandwidth, reliable connectivity, etc. Such networks will significantly increase network traffic in the distribution networks, causing the need for real-time automated decision-making, such as automated network resource allocation. The network resources must be allocated dynamically based on the Quality of Service (QoS) requirements. However, the primary concern is that the distribution networks may get congested soon after allocating network resources. Thus, a QoS-aware prediction framework can be used proactively to predict the future trend of network traffic in each QoS class. In this paper, we propose a framework for predicting the heterogeneous multivariate QoS-aware network traffic to make the best use of network resources dynamically. Specifically, we use a Recurrent Neural Network (RNN) to integrate a Bidirectional Long Short-Term Memory (BLSTM) neural network. The results show that the fusion of RNN-BLSTM can predict the QoS-aware network traffic for over 13 hours with an average accuracy of 97.68%. Moreover, the proposed model is trained and tested over limited data, collected and identified through Deep Packet Inspection (DPI) over an operational network. In addition, we compared the RNN-BLSTM with other prediction algorithms (i.e., LSTM, ARIMA, SVM) in terms of precision, execution time, and energy consumption. Lastly, the proposed framework is used to assign the network resources to each QoS class based on the QoS requirements of that class and its pre-defined priority.

This paper focuses on D2D communication for 5G/6G in dynamic environments where the D2D network topology changes through time due to D2D Devices’ mobility. Specifically, we consider a scenario within the coverage area of a Base Station (BS) serving a number of User Devices (UEs), with variations in speed and direction causing changes in the D2D network topology, either via direct connections or via D2D single-hop or D2D Multi-hop paths. Based on this scenario, we formulate a problem aiming to maximise the total Spectral Efficiency (SE) whilst minimising the total Power Consumption (PC), by selecting the best transmission mode that the D2D devices will operate. In order to address the aforementioned problem, the Distributed Artificial Intelligence Solution (DAIS) plan proposed in our earlier work and designed for static environments is extended to consider the mobility (i.e., speed, direction and indirectly link distance change) of the D2D Device, targeting the dynamic creation of stable and efficient clusters and good backhauling links towards the gateway. The enhanced DAIS performance is comparatively evaluated in terms of SE and PC against selected variations in UE speed and direction, changes in the link Transmission Power (TP), and an increase in the number of Devices in the D2D network. Overall, the results obtained demonstrated superior performance of enhanced DAIS over all the other related investigated approaches (i.e., Distributed Sum Rate (DSR), Single Hop Relay Approach (SHRA), Distributed Random (DR)), in terms of Spectral Efficiency (SE) and Power Consumption (PC). Also, the ability of enhanced DAIS to react and adapt quickly and efficiently to D2D network topology changes, with reduced signalling overhead and control delay in responding to changes, shows that it is a well-poised approach to be used in a Dynamic D2D Environment.

Data dissemination via periodic broadcasting considers a set of items, each with a given request probability, size and scheduling cost. The goal is to construct a broadcast schedule that minimizes the mean query serving time and the mean scheduling cost at the same time. This task has been proven to be NP-Hard, and related studies have gradually discarded the scheduling cost attribute in an effort to simplify the problem. The present study reinstates the cost attribute, as well as any number of additional cost attributes per data item. The proposed, MULTIOPT scheduling algorithm then achieves optimal mean serving time and mean values for all costs concurrently. Comparison with brute-force results and related approaches yield optimality in all tested cases.