End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent Reinforcement Learning Approach

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which can only be partially mitigated by directional antennas. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges originated by heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by mobile users and obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, respectively for full-duplex and half-duplex networks. In addition, we propose an online training algorithm, which addresses the feasibility issues of practical systems, especially the communication and coordination among RL agents. Numerical results show the effectiveness of the proposed approach

METIS research advances towards the 5G mobile and wireless system definition

[EN] The Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project is laying the foundations of Fifth Generation (5G) mobile and wireless communication system putting together the point of view of vendors, operators, vertical players, and academia. METIS envisions a 5G system concept that efficiently integrates new applications developed in the METIS horizontal topics and evolved versions of existing services and systems. This article provides a first view on the METIS system concept, highlights the main features including architecture, and addresses the challenges while discussing perspectives for the further research work.Part of this work has been performed in the framework of the FP7 project ICT-317669 METIS, which is partly funded by the European Commission. The authors would like to acknowledge the contributions of their colleagues in METIS with special thanks to Petar Popovski, Peter Fertl, David Gozalvez-Serrano, Andreas Hoglund, Zexian Li, and Krystian Pawlak. Also thanks to Josef Eichinger and Malte Schellmann for the fruitful discussions during the revision of this article.Monserrat Del Río, JF.; Mange, G.; Braun, V.; Tullberg, H.; Zimmermann, G.; Bulakci, O. (2015). METIS research advances towards the 5G mobile and wireless system definition. 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Wireless Backhaul Architectures for 5G Networks

This thesis investigates innovative wireless backhaul deployment strategies for dense small cells. In particular, the work focuses on improving the resource utilisation, reliability and energy efficiency of future wireless backhaul networks by increasing and exploiting the flexibility of the network. The wireless backhaul configurations and topology management schemes proposed in this thesis consider a dense urban area scenario with static users as well as an ultra-dense outdoor small cell scenario with vehicular traffic (pedestrians, bus users and car users). Moreover, a diverse range of traffic types such as file transfer, ultra-high definition (UHD) on-demand and real-time video streaming are used. In the first part of this thesis, novel dynamic two-tier Software Defined Networking (SDN) architecture is employed in backhaul network to facilitate complex network management tasks including multi-tenancy resource sharing and energy-aware topology management. The results show the proposed architecture can deliver efficient resource utilisation, and QoS guarantee. The second part of the thesis presents wireless backhaul architectures that serve ultra-dense outdoor small cells installed on street-level fixtures. The characteristics of vehicular communications including diverse mobility patterns and unevenly distributed traffic are investigated. The system-level performance of two key technologies for 5G backhaul are compared: massive MIMO backhaul using sub-6GHz band and millimetre (mm)-wave backhaul in the 71 – 76 GHz band. Finally, innovative wireless backhaul architectures delivered from street fibre cabinets for ultra-dense outdoor small cells with vehicular traffic is proposed, which can effectively minimise the need for additional sites, power and fibre infrastructure. Multi-hop backhaul configurations are presented in order to bring in an extra level of flexibility, and thus, improve the coverage of a street cabinet mm-wave backhaul network as well as distribute traffic loads

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Traffic Scheduling in Software-defined Backhaul Network

In the past few years, severe challenges have arisen for network operators, as explosive growth and service differentiation in data demands require an increasing number of network capacity as well as dynamic traffic management. To adapt to the network densification, wireless backhaul solution is attracting more and more attentions due to its flexible deployment. Meanwhile, the software-defined network (SDN) proposes an promising architecture that can achieve dynamic control and management for various functionalities. In this case, by applying the SDN architecture to wireless backhaul networks, the traffic scheduling functionality may satisfy the ever-increasing and differentiated traffic demands. To tackle the traffic demand challenges, traffic scheduling for software-defined backhaul networks (SDBN) is investigated from three aspects in this thesis. In the first aspect, various virtual networks based on service types are embedded to the same wireless backhaul infrastructure. An algorithm, named VNE-SDBN, is proposed to solve the virtual network embedding (VNE) problem to improve the performance of the revenue of infrastructure providers and virtual network request acceptance ratio by exploiting the unique characteristics of SDBNs. In the second aspect, incoming traffic is scheduled online by joint routing and resource allocation approach in backhaul networks operated in low-frequency microwave (LFM) and those operated in millimetre wave (mmW). A digraph-based greedy algorithm (DBGA) is proposed considering the relationship between the degrees of vertices in the constructed interference digraph and system throughput with low complexity. In the third aspect, quality-of-service is provided in terms of delay and throughput with two proposed algorithms for backhaul networks with insufficient spectral resources. At last, as a trial research on E-band, a conceptual adaptive modulation system with channel estimation based on rain rate for E-band SDBN is proposed to exploit the rain attenuation feature of E-band. The results of the research works are mainly achieved through heuristic algorithms. Genetic algorithm, which is a meta-heuristic algorithm, is employed to obtain near-optimal solutions to the proposed NP-hard problems. Low complexity greedy algorithms are developed based on the specific problem analysis. Finally, the evaluation of proposed systems and algorithms are performed through numerical simulations. Simulations for backhaul networks with respect to VNE, routing and resource allocation are developed