The terahertz (THz) antennas, which have features of small size, wide frequency bandwidth and high data rate, are important devices for transmitting and receiving THz electromagnetic waves in the emerging THz systems. However, most of THz antennas suffer from relatively high loss and low fabrication precision due to their small sizes in high frequency bands of THz waves. Therefore, this paper presents a detailed overview of the most recent research on the performance improvement of THz antennas. Firstly, the development of THz antennas is briefly reviewed and the basic design ideas of THz antennas are introduced. Then, THz antennas are categorized as metallic antennas, dielectric antennas and new material antennas. After that, the latest research progress in THz photoconductive antennas, THz horn antennas, THz lens antennas, THz microstrip antennas and THz on-chip antennas are discussed. In particular, the practical difficulties for the development of THz antennas are discussed with promising approaches. In addition, this paper also presents a short review of the process technology of THz antennas. Finally, the vital challenges and the future research directions for THz antennas are presented.

An overview of terahertz antennas

Existing cellular communications and the upcoming 5G mobile network requires meeting high-reliability standards, very low latency, higher capacity, more security, and high-speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastructure and 5G networks allow for end-to-end network resource allocation using the concept of Network Slicing (NS). Data-driven decision making will be vital in future communication networks due to the traffic explosion and Artificial Intelligence (AI) will accelerate the 5G network performance. In this paper, we have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. We use available network Key Performance Indicators (KPIs) to train our model to analyze incoming traffic and predict the network slice for an unknown device type. Intelligent resource allocation allows us to use the available resources on existing network slices efficiently and offer load balancing. Our proposed DeepSlice model will be able to make smart decisions and select the most appropriate network slice, even in case of a network failure.

DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

5G-Slicing-Enabled Scalable SDN Core Network: Toward an Ultra-Low Latency of Autonomous Driving Service

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.

Dynamic Service Function Chain Embedding for NFV-Enabled IoT: A Deep Reinforcement Learning Approach

The integration of multidimensional networks such as space, air and ground is the future trend of the IoT. In this article, we introduce the SAG IoT network paradigm, including its composition and network architecture. Network slicing, the core 5G technology, will be applied to the SAG IoT network. The wide use of mmWave and UAVs is to produce many new scenarios, and it is necessary to study the effects of UAVs on mmWave channels in these new scenarios. In addition, mmWave imaging technology can provide a basis for this research. Machine learning is an important new technology that can be widely used in the SAG IoT. Simulation and measurement are both important means of evaluating the performance of communication systems. To cope with the emerging new applications and technologies, a cloud-based modular simulation system is introduced for 5G and future IoTs; this system is characterized by high efficiency, flexible configuration and high precision.

Space-Air-Ground IoT Network and Related Key Technologies

The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5G era, service based architecture is introduced into mobile networks. The monolithic network elements (e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5G core network are still big challenges. In this paper, we propose a novel management architecture for 5G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.

An SDN/NFV based framework for management and deployment of service based 5G core network

The invention discloses a bidirectional selection computing unloading method based on MEC and MCC. The method comprises steps of: sending to a network side a request to unload an operation program when it is detected that the computing amount of the operation program exceeds a threshold; receiving a unloading sequence allocated by the network side according to the request; computing local energy consumption and unloading energy consumption based on the received unloading sequence, and further computing each unloading energy consumption benefit and sending the same to the network side; and computing a local operation delay and further computing a difference between each unloading delay and local computing delay and sending the same to the network side; receiving a unloding decision matrix obtained by the network side based on the energy consumption benefit and the delay benefit, and returning a decision message; and performing unloading according to the unloading decision message. Therefore, the bidirectional selection computing unloading method based on MEC and MCC can realize the multi-criterion, multi-objective and bidirectional selection computing amount unloading of terminal linear weighting and network side analytical hierarchy process.

Bidirectional selection computing unloading method based on MEC and MCC

The invention provides a SDN (Software Defined Network)-based heterogeneous network convergence framework. According to the framework, the core idea of control and data separation in the software defined network is referred, a controller is added in an EPC core network, user identity information is packaged to be a unique user identifier to serve as the identity certificate during the network access and switching process. The concept of the user identifier is used, a method of realizing virtual switching under the control of the controller is brought forward, the switching access process can be theoretically simplified, and network access time delay can then be reduced. After switching, the IP remains unchanged, the time spent on IP layer switching in the case of heterogeneous network switching is saved, the target of reducing the entire switching time delay is achieved, IP address management and user safety management in a mobile environment are facilitated as the IP is maintained, and a feasible solution is provided for solving the problem of seamless switching between the LTE network and the WLAN network.

SDN (Software Defined Network)-based heterogeneous network convergence framework

Network function virtualization (NFV) has become an emerging issue in both academia and industry. By outsourcing network functions from dedicated hardware to virtualization platform, NFV promises to significantly improve the scalability and flexibility of network management and orchestration. One of the main challenges for NFV deployment is to realize coordinated service function chaining on NFV-based infrastructures. This challenge is referred to as the coordinated NFV resource allocation (coordinated NFV-RA) problem which is proved to be NP-hard. In order to response timely to the service variation, many heuristic or meta-heuristic algorithms are proposed to reduce the computing complexity. However, it is very difficult to evaluate the approach degree between obtained sub-optimal solutions and the optima, since finding the optimal solution is a non-trivial task. In this paper, a novel modeling approach called homogeneous link mapping is proposed to find the optimal solutions of a typical three-stage coordinated NFV-RA model with CPLEX. Then we further establish a service function chain (SFC) deployment database with optimal solutions and the results in the database can be used as a criterion to evaluate other SFC algorithms. In order to imitate different practical networks, the SFC deployments are conducted on three type network topologies. And we also analyze the SFC deploying performance on different topologies. At Last, we make the optimal modeling approach open source, and upload the database on http://www.opensource5g.org/database .

Constructing Service Function Chain Test Database: An Optimal Modeling Approach for Coordinated Resource Allocation

Due to the growing demands of various mobile services and large data traffic in wireless networks, WLAN has been deemed as an important complementary technology for offloading LTE network data. However, currently, WLAN and LTE networks are usually deployed independently, many important coordination and joint optimization can't be achieved, which may lead to low efficiency resource usage and bad user experience. To make a seamless convergence of these two networks still faces many challenges, among which seamless mobility is put forward as one of the most important issues. In this paper, an SDN-based convergence approach is proposed. SDN is introduced from both the access network and core network. Firstly, a virtualization based seamless handover in WLAN is introduced. Then a convergence architecture is proposed, and a handover workflow is designed to support the seamless mobility. Finally, we set up a test bed of the proposed architecture, including WLAN and LTE networks. Our experiment results demonstrate that service disruption delay is less than 100ms during handover, which means seamless convergence can be achieved in the proposed architecture.

Ma Lu

Papers

An SDN/NFV based framework for management and deployment of service based 5G core network

Bidirectional selection computing unloading method based on MEC and MCC

SDN (Software Defined Network)-based heterogeneous network convergence framework

Constructing Service Function Chain Test Database: An Optimal Modeling Approach for Coordinated Resource Allocation

An SDN-based seamless convergence approach of WLAN and LTE networks