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Enabling AI in Future Wireless Networks: a Data Life Cycle Perspective Dinh C ACCEPTED AT IEEE COMMUNICATIONS SURVEYS & TUTORIALS 1 Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective Dinh C. Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N. Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, Fellow, IEEE, and H. Vincent Poor, Fellow, IEEE Abstract—Recent years have seen rapid deployment of mobile I. INTRODUCTION computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing Digitization and automation have been widely recognized as capabilities of wireless systems. Big data analysis, pervasive com- the next wave of technological revolution, which is envisaged puting, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world to create a connected world and fundamentally transform featured by data-driven AI. In this context, a novel paradigm industry, business, public services, and our daily life. In this of merging AI and wireless communications, called Wireless AI context, recent years have seen rapid advancements in the that pushes AI frontiers to the network edge, is widely regarded deployment of Internet of Things (IoT) networks, which can as a key enabler for future intelligent network evolution. To this be mostly attributed to the increasing communication and end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, sensing capabilities combined with the falling prices of IoT we first propose a novel Wireless AI architecture that covers devices [1]. According to the Cisco forecast, by 2030 there five key data-driven AI themes in wireless networks, including will be more than 500 billion IoT devices connected to the Sensing AI, Network Device AI, Access AI, User Device AI and Internet [2]. Moreover, annual global data traffic is predicted Data-provenance AI. Then, for each data-driven AI theme, we to increase threefold over the next 5 years, reaching 4.8 ZB per present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower year by 2022. Fueled by soaring mobile data and increasing wireless network functionalities. Particularly, compared to the device communication, wireless communication systems are other related survey papers, we provide an in-depth discussion evolving toward next generation (5G) wireless networks, by on the Wireless AI applications in various data-driven domains incorporating massive networking, communication, and com- wherein AI proves extremely useful for wireless network design puting resources. Especially, future wireless networks should and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area. provide many innovative vertical services to satisfy the diverse service requirements, ranging from residence, work, to social communications. The key requirements include the support of Index Terms—Wireless Networks, Artificial Intelligence, Deep Learning, Machine Learning, Data-driven AI. up to 1000 times higher data volumes with ultra-low latency and massive device connectivity supporting 10-100x number Dinh C. Nguyen and Pubudu N. Pathirana are with the School of connected devices with ultra-high reliability of 99.999% of Engineering, Deakin University, Australia (e-mails: fcdnguyen, pub- [3]. Such stringent service requirements associated with the in- [email protected]). creasing complexity of future wireless communications make Peng Cheng is with the Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia, and also traditional methods to network design, deployment, opera- with the School of Electrical and Information Engineering, the University tion, and optimization no longer adequate. Past and present of Sydney, Sydney, NSW 2006, Australia (e-mails: [email protected]; wireless communications, regulated by mathematical models, [email protected]). Ming Ding is with the Data61, CSIRO, Australia (e-mail: are mostly derived from extant conventional communication arXiv:2003.00866v2 [cs.NI] 27 Apr 2021 [email protected]). theories. Communication system design significantly depends David Lopez-Perez is with Huawei France R&D, Boulogne-Billancourt, on initial network conditions and/or theoretical assumptions to France (e-mail: [email protected]). Jun Li is with the School of Electronic and Optical Engineering, Nanjing characterize real environments. However, these techniques are University of Science and Technology, Nanjing, 210094, China. He is also unlikely to handle complex scenarios with many imperfections with the School of Computer Science and Robotics, National Research Tomsk and network nonlinearities [4]. The future wireless communi- Polytechnic University, Tomsk, 634050, Russia (e-mail: [email protected]). Aruna Seneviratne is with the School of Electrical Engineering and cation networks, where the quality of services (QoS) is far Telecommunications, University of New South Wales (UNSW), NSW, Aus- beyond the capabilities and applicability of current modeling tralia (e-mail: [email protected]). and design approaches, will require robust and intelligent Yonghui Li is with the School of Electrical and Information Engineering, the University of Sydney, Australia, (e-mail: [email protected]). solutions to adapt to the high network dynamicity for different H. Vincent Poor is with the Department of Electrical Engineering, Princeton services in different scenarios [5]. University, Princeton, NJ 08544 USA (e-mail: [email protected]). Among the existing technologies, artificial intelligence (AI) This work was supported in part by the CSIRO Data61, Australia, and in part by U.S. National Science Foundation under Grant CCF-1908308. The is one of the most promising drivers for wireless communica- work of P. Cheng was supported by ARC under Grant DE190100162. The tions to meet various technical challenges. AI with machine work of Jun Li was supported in part by National Key R&D Program under learning (ML) techniques well known from computer science Grants 2018YFB1004800 and in part by National Natural Science Foundation of China under Grants 61727802, 61872184. (Corresponding Authors: Dinh disciplines, is beginning to emerge in wireless communica- C. Nguyen and Peng Cheng). tions with recent successes in complicated decision making, 2 ACCEPTED AT IEEE COMMUNICATIONS SURVEYS & TUTORIALS Centralized AI Sensing AI Network Edge Network Core Centralized AI Centralized AI Edge Cloud/Server Data Receivers (Platform Data Custodians agnostic) Internet Private Network cloud device AI Data Collection Access AI Data Transfer Data Use wireless Tx wireline Tx Government Personal IoT IoT devices Raw Analysis Individuals Government Industry Analysts Industrial Smart home observation Prediction User IoT IoT device AI Data Generators Data- Environment Data Consumers provenance AI Society, People Centralized AI Fig. 1: An illustration of the Wireless AI paradigm. wireless network management, resource optimization, and in- been designing and developing many wireless AI-empowered depth knowledge discovery for complex wireless networking applications for mobile users (e.g., Face ID and Siri apps as environments [6]. It has been proved that AI techniques can Apple products [9] or Google Assistant tool of Google [10]). achieve outstanding performances for wireless communication These effort have boosted a wide spectrum of wireless AI applications thanks to their online learning and optimization applications, from data collection, data processing, massive capabilities in various complex and dynamic network settings. IoT communication to smart mobile services. Therefore, Wire- AI also lowers the complexity of algorithm computations en- less AI is potentially a game changer not only for wireless abled by data learning and interactions with the environment, communication networks, but also for emerging intelligent and hence accelerates the convergence in finding optimal solu- services, which will impact virtually every facet of our lives tions compared to conventional techniques [7]. By bringing the in the coming years. huge volumes of data to AI, the wireless network ecosystem will create many novel application scenarios and fuel the A. Wireless AI Architecture continuous booming of AI. Therefore, the integration of AI In this paper, we propose a novel Wireless AI architecture techniques into the design of intelligent network architectures for wireless networks as shown in Fig. 1. This new paradigm becomes a promising trend for effectively addressing technical is positioned to create cognition, intelligence, automation, and challenges in wireless communication systems. agility for future wireless communication system design. As Indeed, the marriage of wireless communications and AI shown in Fig. 1, there are mainly four entities involved in this opens the door to an entirely new research area, namely architecture. Wireless AI [8]. Instead of heavily relying on data centres, • Firstly, our environment, society, and people are mon- i.e., cloud servers, to run AI applications, Wireless AI takes itored and sensed by a variety of data generators, e.g. advantage of resources at the network edge to gain AI insights sensors and wearable devices to transform the observa- in wireless networks. More specifically, AI functions can be tions and measurements of our physical world into
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