With the rising demands of next-generation networks, additional resources are essential to meet these growing requirements. Given the general resource limitations, efficient allocation becomes critical to address these demands effectively. Passive optical networks (PONs) have emerged as a key technology to support the future needs of access networks. However, due to the huge traffic characteristics of optical network units (ONUs), resource scheduling methods are crucial, resulting in a significant amount of packet drops in the network. To address this, we propose a Hungarian algorithm-based dynamic timeslot scheduling scheme that optimally allocates available timeslots to ONUs in a time- and wavelength-division multiplexed passive optical network (TWDM-PON) network based on time-variant traffic demands. This increases efficiency and reduces congestion. Results show that the proposed algorithm reduces packet losses significantly compared to existing methods by 40-70% under different scenarios.

This paper presents the implementation of a fully functional 5G network on a compact, energy-efficient edge micro datacenter, showcasing features such as workload prediction and preemptive scaling. The network extends across multiple edge micro datacenters and incorporates quantum-secure connectivity. Our demonstration provides a blueprint for forward-looking 5G deployments aiming to meet challenging latency and throughput requirements while complying with stringent security requirements. Measurements are performed for network throughput and latency as well as for CPU load of the different components of the 5G deployment, including distributed unit, centralised unit control and user planes, 5G core and the RAN intelligent controller. Additionally, an intelligent workload prediction mechanism, based on an LSTM model, enables preemptive scaling of the centralised unit’s user plane. This proactive approach helps to mitigate bottlenecks as the number of users connecting to the network increases.

As modern 5G systems are being deployed, researchers question whether they are sufficient for the oncoming decades of technological evolution.Growing numbers of interconnected intelligent devices put these networks under tremendous pressure, demanding their development.Paving the way for beyond 5G and 6G systems, commonly denoted by B5G herein, therefore means seeking enablers to increase efficiency from different perspectives.One novel look on this is the application of inexact computations where nine 9s reliability is not needed, for example, in non-critical mobile broadband traffic.The paradigm of Approximate Computing (AxC) focuses on such areas where constrained quality degradation results in savings that benefit the users and operators.This paper surveys the state-of-the-art publications on the intersection of AxC and B5G systems, identifying and emphasizing trends and tendencies in existing work and directions for future research.The work highlights resource allocation algorithms as particularly mesmerizing in the former, while research related to Intelligent Reflective Surfaces appears the most prominent in the latter.In both, problems are often NP-hard and, thus, only solvable using heuristics or approximations, Successive Convex Approximation and Reinforcement Learning are most frequently applied.

Future wireless networks will be more heterogeneous due to different radio access technologies (RATS), types of interconnecting links between the users, and diverse requirements of the connected users. The multiple RAT (multi-RAT) network composed of cellular and Wi-Fi access points is considered as a promising solution to meet indoor traffic needs during pandemic situations. Therefore, in this paper we design and provide an experimental validation of a radio access network controller (RANO) prototype solution and investigate different long term evolution medium access control (UTE MAC) scheduling strategies to show the quality-of-service (QoS) and quality-of- experience (QoE) performance of multi-RAT. The main aspects of this paper are to show the involvement towards coordination strategies amongst multiple RATs and integration of parametric control of higher MAC and upper layer network protocols. Moreover, we also evaluate and examine the possibility to take advantage of the fact of using interworking concepts such as lightweight Internet protocol (LWIP) and LTE-WLAN aggregation (LWA) while making the scheduling decisions. Such a solution would contribute to the software-defined networking (SDN) approach, where multi-RAT aware scheduler adapts to dynamic channel conditions to provide robustness against severe real-time channel conditions. Finally, we provide comparative analysis of multi- RAT scenarios and evaluate the QoE performance of different scheduling algorithms with SINR based information centric LWA switching and QoE-aware LWA switching by using RANO.

While the concept of ultra-dense small cell networks (SCNs) has brought a number of significant opportunities for the telecommunication industry, it has also introduced a major challenge for researchers, who must develop techniques to reduce the sharp increase in power consumption that will be required to backhaul traffic from SCNs to the core network. In this research, we investigate the green backhauling challenge for a fifth generation (5G) wireless communication network that uses both the passive optical network (PON) and millimeter wave (mmWave) backhauling to support its diverse groups of customers and applications. Our approach is based on the fact that the energy efficiency figures for the PON and the mmWave technologies are different under a given load condition. The PON technology is more energy efficient under heavy load conditions, whereas the mmWave technology offers better energy efficiency under low load conditions. As such, in response to varying traffic loads during various hours of the day, a fixed backhauling strategy is insufficient to guarantee the minimum power consumption and the required data rates. We formulate an optimization problem that considers the estimated hourly traffic load and determines the most energy efficient backhauling strategy for various hours of the day. Considering the complexity of the optimization problem, we also propose an energy efficient heuristic solution to solve this problem. Simulation results indicate that the proposed solution provides up to 32 percent more energy savings than the existing solution.

The concept of heterogeneous networks and small cells is likely to be a key addition to the fifth generation (5G) wireless communication standard, the prospect of which has motivated 5G researchers to investigate the coverage and integration of various small cells. Heterogeneous networks with small cells offer key benefits such as high frequency reuse, huge data rates and low power consumption for mobile nodes. High frequency reuse is particularly important given the exponential growth of data rates and the looming frequency scarcity problem. Another major consideration for 5G is the energy efficiency as with the exponential growth of demand, power consumption in the information and communication technology (ICT) sector is also expected to significantly increase. While the concept of small cells in heterogeneous networks addresses the frequency scarcity problem to a great extent, unless otherwise carefully managed, a large number of uncoordinated and lightly loaded small cells significantly increase the overall power consumption, contrary to the green communication target of the 5G standard. In this paper, we focus on an energy efficient resource management and planning system that investigates the impact of growing number of small cells in a macro cell. We present an analytical model for calculating the optimum number of small cells that need to be kept awake at various hours of a day for meeting the quality of service demands from all users. Simulation results show significant power consumption improvement than an existing model.