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

In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous “agent,” a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications.

Deep Reinforcement Learning Based Resource Allocation for V2V Communications

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

Channel state information (CSI) at the base station (BS) is critical to resource allocation design for wireless networks, but it is hard to obtain accurate CSI in a high mobility vehicular environment. In this letter, we study the spectrum and power allocation problem in device-to-device-enabled vehicular networks, where CSI of vehicular links is only reported to the BS periodically. We maximize the sum throughput of all vehicle-to-infrastructure (V2I) links while guaranteeing the reliability of each vehicle-to-vehicle (V2V) link with the delayed CSI feedback. We propose a low-complexity algorithm to find the optimal spectrum sharing strategy among V2I and V2V links and properly adjust their transmit powers. Its desirable performance is confirmed by computer simulation.

Spectrum and Power Allocation for Vehicular Communications With Delayed CSI Feedback

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 emerging area. After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.

Machine Learning for Vehicular Networks

Techniques for random access (RA) in a cellular internet-of-things (CIOT) are discussed An example apparatus configured to be employed within a User Equipment (UE), comprises a receiver circuitry, a processor, and transmitter circuitry The receiver circuitry is configured to receive RA resource allocation information via one of a system information message or a downlink control information (DCI) message The processor is operably coupled to the receiver circuitry and configured to: select a RA preamble sequence; generate a payload; and spread the payload via a spreading sequence The transmitter circuitry is configured to transmit, based on the RA resource allocation information, a RA message comprising the RA preamble sequence and the payload, wherein the RA message is transmitted in a RA slot The receiver circuitry is further configured to receive a response comprising a device identity of the UE and one of an uplink (UL) grant or a RA reject message

Enhanced RACH (random access channel) design for 5G CIoT (cellular internet of things)

Embodiments of a network User Equipment (nUE), wearable User Equipment (wUE), and methods for sidelink communication are generally described herein. The nUE may transmit a control channel that allocates a subframe as either a downlink subframe or an uplink subframe for a sidelink communication between the nUE and a wearable User Equipment (wUE). When the control channel allocates the subframe as a downlink subframe, the nUE may contend for access to channel resources. The contention may include transmission of a transmitter resources acquisition and sounding (TAS) channel in a physical resource block (PRB) and an attempted detection of a receiver resources acquisition and sounding (RAS) channel from the wUE in the PRB. When the control channel allocates the subframe as an uplink subframe, the wUE may contend for access to the channel resources.

Kim Joonbeom

Papers

Machine Learning for Vehicular Networks: Recent Advances and Application Examples

Spectrum and Power Allocation for Vehicular Communications With Delayed CSI Feedback

Machine Learning for Vehicular Networks

Enhanced RACH (random access channel) design for 5G CIoT (cellular internet of things)

User equipment (ue) and method of sidelink data communication in fifth generation (5g) new radio (nr) things networks