Millimeter-Wave Wireless Communications for IoT-Cloud Supported Autonomous Vehicles: Overview, Design, and Challenges

Kong, Linghe; Khan, Muhammad Khurram; Wu, Fan; Chen, Guihai; Zeng, Peng

doi:10.1109/mcom.2017.1600422cm

Cited by 226 publications

(115 citation statements)

References 13 publications

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“…8 shows the probability of false alarm without RadCom for B c = 20 MHz for varying τ and three different d values. The transmissions during the vulnerable period V is observed to result with interference for especially τ < T /(2BT s ) = 1 µs and τ = 17-20 µs, described in (4). False alarm probability increases with increasing d due to attenuated radar received signals.…”

Section: B Resultsmentioning

confidence: 98%

“…Today, most automotive radar systems operate at 76-81 GHz [2], which provides good range resolution, on the order of centimeters [1] and the possibility to mitigate interference by locating the radars at different carrier frequencies [3]. Likewise, vehicle-to-vehicle (V2V) communication is becoming a standard, having proven its value in dissemination of safety critical information [1], [4]. However, both technologies have limitations related to the increased penetration rates.…”

Section: Introductionmentioning

confidence: 99%

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Radar Communications for Combating Mutual Interference of FMCW Radars

Aydoğdu

Garcia

Hammarstrand

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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Commercial automotive radars used today are based on frequency modulated continuous wave signals due to the simple and robust detection method and good accuracy. However, the increase in both the number of radars deployed per vehicle and the number of such vehicles leads to mutual interference, cutting short future plans for autonomous driving and active safety functionality. We propose and analyze a radar communication (RadCom) approach to reduce this mutual interference while simultaneously offering communication functionality. We achieve this by frequency division multiplexing radar and communication, where communication is built on a decentralized carrier sense multiple access protocol and is used to adjust the timing of radar transmissions. Our simulation results indicate that radar interference can be significantly reduced, at no cost in radar accuracy.

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Section: B Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Radar Communications for Combating Mutual Interference of FMCW Radars

Aydoğdu

Garcia

Hammarstrand

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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“…The Doppler effect is one of the challenges brought by the mmWave implementation to the cooperative UAV communication systems. Since the Doppler effect depends on frequency and 14 mobility, the Doppler shift will range from 10 Hz to 28 kHz if the mmWave frequency is 3-60…”

Section: B Millimeter-wave For U2u Communicationsmentioning

confidence: 99%

“…GHz with mobility speeds of UAVs within 3-500 km/h. Furthermore, due to the concentrated beam, there is a non-zero bias in the Doppler spectrum [14]. Therefore, the Doppler effect of mmWave needs to be well addressed in both U2N and U2U communications.…”

Section: B Millimeter-wave For U2u Communicationsmentioning

confidence: 99%

Cooperation Techniques for a Cellular Internet of Unmanned Aerial Vehicles

Zhang

Song

Han

et al. 2019

IEEE Wireless Commun.

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Unmanned aerial vehicles (UAVs) are powerful Internet-of-Things components to provide sensing and communications in the air due to their advantages in mobility and flexibility. As aerial users, UAVs are envisioned to support various sensing applications in the next generation cellular systems, which have been studied by the Third Generation Partnership Project (3GPP). However, the Quality-of-Services (QoS) of the cellular link between the UAV and the base station may not be guaranteed when UAVs are at the cell edge or experiencing deep fading. In this article, we first introduce the non-cooperative cellular Internet of UAVs. Then we propose a cooperative sense-and-send protocol, in which a UAV can upload sensory data with the help of a UAV relay, to provide a better communication QoS for the sensing tasks. Key techniques including trajectory design and radio resource management that support the cooperative cellular Internet of UAVs are presented in detail. Finally, the extended cooperative cellular Internet of UAVs is discussed for QoS improvement with some open issues, such as massive multiple-input multiple-output systems, millimeter-wave, and cognitive communications. arXiv:1904.01257v2 [cs.IT] 3 Apr 2019 2 Fig. 1. Illustration for UAV sensing applications.Recently, the Third Generation Partnership Project (3GPP) has approved the study item on enhanced support to seamlessly integrate UAVs into future cellular networks [4]. Notably, it has produced accurate modeling of UAV-to-ground channels, and defined various UAV scenarios together with their respective features. Therefore, the terrestrial cellular networks, e.g., LTE and 5G, are considered as a promising enabler for UAV sensing applications, which we refer to as cellular Internet of UAVs. In the cellular Internet of UAVs, the sensory data needs to be transmitted to the base station (BS) for timely processing. However, when UAVs are located at the cell edge or experiencing deep fading, the quality of the communication between the UAV and the BS may not be satisfactory, specially for emergency search-and-rescue and surveillance applications where the sensing task is far away from the BS. In the non-cooperative cellular Internet of UAVs, the UAV should fly to a communication point first to satisfy the QoS for the communication link, which will bring extra delay.To tackle this challenge, we propose the UAVs to transmit via a UAV relay for providing a better QoS, as shown in Fig. 2. To support the cooperative cellular Internet of UAVs, the system provides two types of communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U).

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“…Medium (e.g., monitoring of parking spaces) to high (e.g., monitoring of home video) data rate is needed in cases of mMTC monitoring activities. High data rate can be achieved by using a millimeter wave (mmWave) spectrum to share multi-gigabit data in the surrounding environment (i.e., mMTC monitoring with high data rate requirement) and to recognize an object via cloud in real time to find the optimal driving strategy instantaneously for IoT-autonomous vehicles application [101]. Data rates generated during transmissions of tracked medical assets, transport fleets, and ticked point of sale (POS) terminals is low (e.g., medium importance for ticketing and tracking activities in mMTC).…”

Section: Data Ratementioning

confidence: 99%

IoT’s Tiny Steps towards 5G: Telco’s Perspective

Cero¹,

Husić

Baraković

2017

Symmetry

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Abstract:The numerous and diverse applications of the Internet of Things (IoT) have the potential to change all areas of daily life of individuals, businesses, and society as a whole. The vision of a pervasive IoT spans a wide range of application domains and addresses the enabling technologies needed to meet the performance requirements of various IoT applications. In order to accomplish this vision, this paper aims to provide an analysis of literature in order to propose a new classification of IoT applications, specify and prioritize performance requirements of such IoT application classes, and give an insight into state-of-the-art technologies used to meet these requirements, all from telco's perspective. A deep and comprehensive understanding of the scope and classification of IoT applications is an essential precondition for determining their performance requirements with the overall goal of defining the enabling technologies towards fifth generation (5G) networks, while avoiding over-specification and high costs. Given the fact that this paper presents an overview of current research for the given topic, it also targets the research community and other stakeholders interested in this contemporary and attractive field for the purpose of recognizing research gaps and recommending new research directions.

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Millimeter-Wave Wireless Communications for IoT-Cloud Supported Autonomous Vehicles: Overview, Design, and Challenges

Cited by 226 publications

References 13 publications

Radar Communications for Combating Mutual Interference of FMCW Radars

Radar Communications for Combating Mutual Interference of FMCW Radars

Cooperation Techniques for a Cellular Internet of Unmanned Aerial Vehicles

IoT’s Tiny Steps towards 5G: Telco’s Perspective

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