Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead

Energy efficiency (EE) constitutes a key target in the deployment of 5G networks, especially due to the increased densification and heterogeneity. In this paper, a Deep Q-Network (DQN) based power control scheme is proposed for improving the system-level EE of two-tier 5G heterogeneous and multi-channel cells. The algorithm aims to maximize the EE of the system by regulating the transmission power of the downlink channels and reconfiguring the user association scheme. To efficiently solve the EE problem, a DQN-based method is established, properly modified to ensure adequate QoS of each user (via defining a demand-driven rewarding system) and near-optimal power adjustment in each transmission link. To directly compare different DQN-based approaches, a centralized (C-DQN), a multi-agent (MA-DQN) and a transfer learning-based (T-DQN) method are deployed to address whether their applicability is beneficial in the 5G HetNets. Results confirmed that DQN-assisted actions could offer enhanced network-wide EE performance, as they balance the trade-off between the power consumption and achieved throughput (in Mbps/Watt). Excessive performance was observed for the MA-DQN approach (>5 Mbps/Watt), since the decentralized learning supports low-dimensional agents to be coordinated with each other through global rewards. In further comparing the T-DQN against MA-DQN solutions, T-DQN presents beneficial usage for very low or very high inter-cell distances, whereas the usage of MA-DQN is preferred (by a factor of ~1.3) for intermediate inter-cell distances (100-600m), where the power savings are feasible towards achieving increased EE. Furthermore, T-DQN scheme guarantees good EE solutions (above 2 Mbps/Watt), even for densely-deployed macro-cells, with effortless training and memory requirements. On the contrary, MA-DQN offers the best EE solutions at the expense of massive training resources and required training time.

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Deep Reinforcement Learning for Energy-Efficient Multi-Channel Transmissions in 5G Cognitive HetNets: Centralized, Decentralized and Transfer Learning Based Solutions

A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices

One of the key goals of future 5G networks is to incorporate many different services into a single physical network, where each service has its logical network isolated from other networks. Besides, Network Slicing (NS) is considered as the key technology for meeting the service requirements of diverse application domains. Recently, NS faces several algorithmic challenges for 5G networks. This paper provides a review related to NS architecture with a focus on relevant Management and Orchestration (MANO) architecture across multiple domains. In addition, this survey paper delivers a deep analysis and a taxonomy of NS algorithmic aspects. Finally, this paper highlights some of the open issues and future directions.

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Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey

The provision of high data rate services to mobile users combined with improved quality of experience (i.e., zero latency multimedia content) drives technological evolution towards the design and implementation of fifth generation (5G) broadband wireless networks. To this end, a dynamic network design approach is adopted whereby network topology is configured according to service demands. In parallel, many private companies are interested in developing their own 5G networks, also referred to as non-public networks (NPNs), since this deployment is expected to leverage holistic production monitoring and support critical applications. In this context, this paper introduces a 5G NPN architectural approach, supporting among others various key enabling technologies, such as cell densification, disaggregated RAN with open interfaces, edge computing, and AI/ML-based network optimization. In the same framework, potential applications of our proposed approach in real world scenarios (e.g., support of mission critical services and computer vision analytics for emergencies) are described. Finally, scalability issues are also highlighted since a deployment framework of our architectural design in an additional real-world scenario related to Industry 4.0 (smart manufacturing) is also analyzed.

A Cost-Efficient 5G Non-Public Network Architectural Approach: Key Concepts and Enablers, Building Blocks and Potential Use Cases.

5G is considered to be the technology that will accommodate the development and management of innovative services with stringent and diverse requirements from end users, calling for new business models from the industry. In this context, the development and efficient management of Network Services (NS) serving specific vertical industries and spanning across multiple administrative domains and heterogeneous infrastructures is challenging. The main challenges regard the efficient provision of NSs considering the Quality of Service (QoS) requirements per vertical industry along with the optimal usage of the allocated resources. Towards addressing these challenges, this paper details an innovative approach that we have developed for managing and orchestrating such NSs, called SONATA, and compare it with OSM and Cloudify, which are two of the most known open-source Management and Orchestration (MANO) frameworks. In addition to examining the supported orchestration mechanisms per MANO framework, an evaluation of main operational and functional KPIs is provided based on experimentation using a real testbed. The final aim is the identification of their strong and weak points, and the assessment of their suitability for serving diverse vertical industry needs, including of course the Internet of Things (IoT) service ecosystem.

Arturo Zurita

Papers

Comparison of Management and Orchestration Solutions for the 5G Era