The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous wireless connectivity for all. Transformative solutions are expected to drive the surge for accommodating a rapidly growing number of intelligent devices and services. Major technological breakthroughs to achieve connectivity goals within 6G include: (i) a network operating at the THz band with much wider spectrum resources, (ii) intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, (iii) pervasive artificial intelligence, (iv) large-scale network automation, (v) an all-spectrum reconfigurable front-end for dynamic spectrum access, (vi) ambient backscatter communications for energy savings, (vii) the Internet of Space Things enabled by CubeSats and UAVs, and (viii) cell-free massive MIMO communication networks. In this roadmap paper, use cases for these enabling techniques as well as recent advancements on related topics are highlighted, and open problems with possible solutions are discussed, followed by a development timeline outlining the worldwide efforts in the realization of 6G. Going beyond 6G, promising early-stage technologies such as the Internet of NanoThings, the Internet of BioNanoThings, and quantum communications, which are expected to have a far-reaching impact on wireless communications, have also been discussed at length in this paper.

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6G and Beyond: The Future of Wireless Communications Systems

. Anthropogenic atmospheric emissions of typical toxic heavy metals have caused worldwide concern due to their adverse effects on human health and the ecosystem. By determining the best available representation of time-varying emission factors with S-shape curves, we establish the multiyear comprehensive atmospheric emission inventories of 12 typical toxic heavy metals (Hg, As, Se, Pb, Cd, Cr, Ni, Sb, Mn, Co, Cu, and Zn) from primary anthropogenic activities in China for the period of 1949–2012 for the first time. Further, we allocate the annual emissions of these heavy metals in 2010 at a high spatial resolution of 0.5° × 0.5° grid with ArcGIS methodology and surrogate indexes, such as regional population and gross domestic product (GDP). Our results show that the historical emissions of Hg, As, Se, Cd, Cr, Ni, Sb, Mn, Co, Cu, and Zn, during the period of 1949–2012, increased by about 22–128 times at an annual average growth rate of 5.1–8.0 %, reaching about 526.9–22 319.6 t in 2012. Nonferrous metal smelting, coal combustion of industrial boilers, brake and tyre wear, and ferrous metal smelting represent the dominant sources of heavy metal emissions. In terms of spatial variation, the majority of emissions are concentrated in relatively developed regions, especially for the northern, eastern, and southern coastal regions. In addition, because of the flourishing nonferrous metal smelting industry, several southwestern and central-southern provinces play a prominent role in some specific toxic heavy metals emissions, like Hg in Guizhou and As in Yunnan. Finally, integrated countermeasures are proposed to minimize the final toxic heavy metals discharge on account of the current and future demand of energy-saving and pollution reduction in China.

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Quantitative assessment of atmospheric emissions of toxic heavy metals from anthropogenic sources in China: historical trend, spatial distribution, uncertainties, and control policies

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Federated Learning for Internet of Things: A Comprehensive Survey

The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient and pave the path to autonomous driving in the advent of the fifth-generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this upcoming area.

Machine Learning for Vehicular Networks: Recent Advances and Application Examples

An inventory of chromium emission into the atmosphere and water from anthropogenic activities in China was compiled for 1990 through to 2009. We estimate that the total emission of chromium to the atmosphere is about 1.92×105t. Coal and oil combustion were the two leading sources of chromium emission to the atmosphere in China, while the contribution of them showed opposite annual growth trend. In total, nearly 1.34×104t of chromium was discharged to water, mainly from six industrial categories in 20 years. Among them, the metal fabrication industry and the leather tanning sector were the dominant sources of chromium emissions, accounting for approximately 68.0% and 20.0% of the total emissions and representing increases of15.6% and 10.3% annually, respectively. The spatial trends of Cr emissions show significant variation based on emissions from 2005 to 2009. The emission to the atmosphere was heaviest in Hebei, Shandong, Guangdong, Zhejiang and Shanxi, whose annual emissions reached more than 1000t for the high level of coal and oil consumption. In terms of emission to water, the largest contributors were Guangdong, Jiangsu, Shandong and Zhejiang, where most of the leather production and metal manufacturing occur and these four regions accounted for nearly 47.4% of the total emission to water.

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Anthropogenic Chromium Emissions in China from 1990 to 2009

Identifying organic matter sources using isotopic ratios in a watershed impacted by intensive agricultural activities in Northeast China

Nitrate pollution in aquatic systems caused by intensive agricultural activities is a serious problem in the Sanjiang Plain. In this study, a dual isotope approach (δ(15)N-NO3(-) and δ(18)O-NO3(-)) was employed to identify potential nitrate sources (atmospheric deposition, AD; NO3(-) derived from soil organic matter nitrification, NS; NO3(-) derived from chemical fertilizer nitrification, NF; and manure and sewage, MS the low flow period, November). On the contrary, in the ground water, high NO3(-) concentrations were observed (7.84 ± 5.83 mg L(-1)) and no significant temporal variation in NO3(-) was found during the sampling periods. The sampled water δ(18)O-NO3(-) values suggest that the nitrification process was not the main N cycling process, because most of the measured δ(18)O-NO3(-) values were above the expected δ(18)O-NO3(-) from nitrification throughout the sampling periods. Both the chemical and isotopic characteristics indicated that the signs of de-nitrification were absent in the surface water. However, significant de-nitrification processes were observed in the ground water for all sample periods. Results from the SIAR model showed that source contributions differed significantly during the two sampling periods. During the high flow period, chemical fertilizers and soil N fertilizer equally contributed to the major sources of nitrate in the surface water. In contrast, manure and sewage sources dominated the source contribution during the low flow period (November). This study suggested that with the assessment of the behaviors and sources of NO3(-), effective nitrate reduction strategies and better management practices can be implemented to protect water quality.

Lu Lu

Papers

Machine Learning for Vehicular Networks: Recent Advances and Application Examples

Anthropogenic Chromium Emissions in China from 1990 to 2009

Identifying organic matter sources using isotopic ratios in a watershed impacted by intensive agricultural activities in Northeast China

Nitrate behaviors and source apportionment in an aquatic system from a watershed with intensive agricultural activities

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area.