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Future Internet is expected to meet explosive traffic growth and extremely complex architecture, which tend to make the traditional NTC strategies inefficient and even ineffective. Inspired by the latest breakthroughs of AI and its power to address large-scale and complex difficulties, the network community has begun to consider shifting the NTC paradigm from legacy rule-based to novel AI-based. As an applied inter-discipline, design and implementation are important. Although there have been some preliminary explorations along this frontier, they are either limited by only envisioning the prospects, or too scattered to provide high-level insight into a general methodology. To this end, we start with the domain knowledge relationships of AI and NTC, summarizing a baseline workflow toward deep reinforcement learning, which will be the dominant method for the AI-NTC paradigm. On top of that, we argue that AI-NTC training and running must be carried out in online environments in closed-loop fashion for the purpose of putting ti into practice. A series of challenges and opportunities are discussed from a realistic viewpoint, and a set of new architecture and mechanism to enable the online and closed-loop AI-NTC paradigm are proposed. Hopefully, this work can help the AI community to better understand NTC and the NTC community to better live with AI. [ABSTRACT FROM AUTHOR]

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC) has emerged as a promising paradigm for enabling sustainable low-power operation of Internet of Things devices. This is due to its ability to enable sensing and communication through backscattering ambient wireless signals (e.g., WiFi and TV sig-nals). But a great impediment to AmBC-enabled networks is the difficulty in decoding the backscat-ter signals because the ambient signals are usually modulated and meant for other legacy receivers rather than AmBC devices. Drawing from the ability of machine learning (ML) to enhance the performance of wireless communication systems, some ML-aided techniques have been developed to assist signal detection in AmBC. Hence, this article aims to provide a comprehensive overview of the subject by describing the operation of the AmBC network, highlighting the major challenges to signal detection in AmBC, discussing and com-paring the performance of some existing ML-assisted solutions to AmBC signal detection, and highlighting some future research that could be carried out on the subject. [ABSTRACT FROM AUTHOR]

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (Ai) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an Ai-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of Ai techniques for 6G networks and elaborate how to employ the Ai techniques to efficiently and effectively optimize the network performance, including Ai-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. We highlight important future research directions and potential solutions for Ai-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management. [ABSTRACT FROM AUTHOR]

UAV aided communication technology holds tremendous potential to upgrade outdoor link throughput and provide on-demand wireless services. The flexible deployment characteristic makes UAV-aided networks competent at emergency situations, including natural disasters and sudden traffic hotspots. In this backdrop, UAVs are required to be densely deployed to accommodate the huge volume of data traffic, where interference amid the neighboring cells turns out to be extremely challenging. To break this stalemate, this article systematically investigates spectrum sharing technology for ultra-dense UAV-aided networks from the architecture level down to the physical layer. We shed light on design principles and key challenges in utilizing overlapped spectrum for interference-enabled concurrent transmissions. With these principles in mind, we introduce SpecShare, which utilizes coding redundancy at the PHY layer for UAV spectrum sharing. We explore the optimal UAV placement strategy in the network layer to fully unleash the potential of such spectrum sharing capacity. We discuss the feasibility of SpecShare, and demonstrate its effectiveness in terms of network throughput. [ABSTRACT FROM AUTHOR]

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users? perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure. [ABSTRACT FROM AUTHOR]