Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 37

Although 5G provides low latency and very high speed support capabilities (e.g., eMBB), a wide number of devices (e.g., mMTC), a heterogeneous mix of traffic types from a diverse and challenging suite of applications (e.g., URLLC), AI is complemented by observing from specific environments to provide independent reach of operation, turning 5G into a data-driven adaptive real-time network [13]. AI is used for 5G system modeling, automation of core network (e.g. provisioning, scheduling, prediction of faults, protection, fraud detection), distributed computing, reduction of operating costs, and improvement of both service quality and customer evolves on chatbots, recommendation systems, and strategies such as automated processes. In addition, AI is used across all layers, from the disaggregated radio access layer (5G RAN) to the distributed cloud layer (5G Edge/Core) to the integrated access backhaul to fine tune performance [5].

AI is used for the 5G distributed cloud layer to optimize device resource usage, autoscaling, identification of anomalies, predictive analytics, prescriptive policies, and so on. In addition, the 5G distributed cloud layer offers acceleration technologies to enable federated and distributed learning for AI workloads [19].

Figure 2.1 Growth of 5G Connections worldwide.

The growth of mobile 5G worldwide and its market overview are shown in Figure 2.1 and Figure 2.2. To support a multitude of emerging technologies (e.g., AR/VR, Industrial IoT, autonomous vehicles, drones, Industry 4.0 projects, Smart Cities, Smart Ports), AI is also used for customer service management and business support systems. Several learning methods are used to train data models based on target use cases, such as supervised learning, unsupervised learning, reinforcement learning, federated learning, distributed learning, transfer learning, and deep learning based on algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) [9].

Figure 2.2 5G Market analysis.

Source: Press Release, Investor Relation Presentation, Annual Report, Expert Interview, and Markets and Markets Analysis

AI needs a mixture of AI, localized AI, and end-to-end AI at the system stage. In individual network modules, device-level AI is used to solve self-contained problems where no data needs to be transferred onto the network. Where AI is extended to one network domain or cross-network domains, localized AI allows data to be passed on to the network, but is limited to a local network domain, such as at the RAN or fronthaul. End-to-end AI is where the whole network needs to be accessible to the network, and where it needs to gather data and information from various network domains in order to implement AI properly. Slice management and network service assurance can provide examples of end-to-end AI [4, 8].

5G and AI are some of the world’s most groundbreaking innovations in hundreds of years. Although each sector is individually reshaping and allowing for unique ideas, 5G and AI integration will be truly revolutionary. In reality this integration is central to our small sensor edge definition, which includes in-home computing, edge cloud and 5G, to create an integrated intelligent system and service networking tool. Substantial amounting data, be it via on-device AI or gradual edge cloud storage over moderate 5G, can be distributed close to its source. Online AI is important to process data closer to the source because it provides efficient and effective output such as security, consistency and reliability as well as support for knowledge in scale [22].

The topic has several introductions. They all serve a different purpose and offer a different perspective on these rapidly changing 5G communications. The goal of this review is to provide the reader with 5G usable artificial intelligence and to assist mobile users. The main contributions of this work are

1 Provided a complete groundwork of integrated services of 5G in AI and AI in 5G;

2 Provided a clear road map of artificial intelligence and 5G in the industrial space;

3 Described the role of artificial intelligence in the mobile networks along with research challenges.

This survey is clearly differentiated from other recent surveys by the above listed points. The paper is structured as follows: Along with this detailed introduction, the complete study of AI in 5G and 5G in AI are reviewed in Section 2.2. Section 2.3 discusses artificial intelligence and 5G in the industrial space. Section 2.4 briefly describes the roles of AI in mobile networks followed by a conclusion in Section 2.5.