Millimeter Wave Communication: A Comprehensive Survey

Wang, Xiong; Kong, Linghe; Kong, Fanxin; Qiu, Fudong; Xia, Mingyu; Arnon, Shlomi; Chen, Guihai

doi:10.1109/comst.2018.2844322

Cited by 469 publications

(240 citation statements)

References 196 publications

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“…As the control plane latency of X-Ethernet for reconfigurations has not been identified, the overall suitability of X-Ethernet for Xhaul needs further investigations. b) MillimeterWave (mmWave) Backhaul: Millimeter-Wave (mmWave) radio technology for wireless communications operates in the spectrum between 30 and 300 GHz [275], [318], [319]. mmWaves have relatively short wavelengths and thus suffer pronounced signal attenuation with propagation distance and due to obstacles.…”

Section: Sub-block4mentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

498

219

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Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.

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Section: Sub-block4mentioning

confidence: 99%

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Nasrallah

Thyagaturu

Alharbi

et al. 2019

IEEE Commun. Surv. Tutorials

498

219

View full text Add to dashboard Cite

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“…Compute Loss(w, β) = R(w, β) + L2(w, β) 13 Optimize: 14 Update w and β for minimizing Cost(w, β); 15 Clip w (min B, max ∞); 16 Clip β (min 0, max ∞); 17 end 18 end networks. Monotonic Network.…”

Section: A Deep Learning Networkmentioning

confidence: 99%

“…Recently, frameworks combining game theory and deep learning have been active subject of research [12], [13]. Results illustrate applications of such approach in various domains [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

Shin

Kim

Levorato

2019

IEEE Trans. Veh. Technol.

121

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State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auctionbased mechanism to control the charging schedule in multidrone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-priceauction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learningbased approach provides effective battery charging control in multi-drone scenarios.

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“…As such, their ground projection points are a nonhomogeneous PPP with the same intensity as well. 3 Please note that the penetration loss of the NLoS channels of the ground BSs is already modeled in their path-loss model so that their fading gain does not need to consider the LoS effect. shape and rate parameters N g , G * u ∼ Gamma(N u , N u ) is the beamforming fading channel gain 4 from U * , G g,i ∼ exp (1) is the fading gain from X g,i which is an exponential RV with unit mean,…”

Section: B Small-scale Fading Channel Model With Miso Beamformingmentioning

confidence: 99%

“…Exploiting the use of the mmWave band may effectively alleviate the spectrum crunch problem in the next generation cellular networks. However, its effectiveness may be significantly weakened by environmental blockages thanks to the high path loss and low penetration characteristics of mmWave channels [1]- [3]. A very attractive means of enhancing the propagation performance of the mmWave channels is to use unmanned aerial vehicles (UAVs) equipped with multiple antennas as the "flying" base stations (BSs) using the mmWave band because UAVs are able to agilely position themselves to ameliorate their channel quality in accordance of environmental variations.…”

Section: Introductionmentioning

confidence: 99%

MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization

Liu

2019

IEEE J. Select. Areas Commun.

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This paper aims to exploit the fundamental limits on the downlink coverage and spatial throughput performances of a cellular network consisting of a tier of unmanned aerial vehicle (UAV) base stations (BSs) using the millimeter wave (mmWave) band and a tier of ground BSs using the ultra high frequency (UHF) band. To reduce handover signaling overhead, the ground BSs take charge of control signaling delivery whereas the UAVs are in charge of payload data transmission so that users need to simultaneously associate with a ground BS and a UAV in this network with a control-data plane-split architecture. We first propose a three-dimensional (3D) location distribution model of the UAVs using stochastic geometry which is able to generally characterize the positions of the UAVs in the sky. Using this 3D distribution model, we propose the multi-cell coverage probability and the volume spectral efficiency of the network, derive their explicit low-complexity expressions and find their upper limits when each of the UAVs and ground BSs is equipped with a massive antenna array. We further show that the multi-cell coverage probability and the volume spectral efficiency can be maximized by optimally deploying and positioning the UAVs in the sky and thereby their fundamental maximal limits are found. These important analytical findings are validated by numerical simulations.Index Terms-Unmanned aerial vehicle network, millimeter wave, coverage, throughput, cell association, stochastic geometry. arXiv:1905.06099v3 [cs.IT] 18 Jul 2019 by a clustered channel model consisting of small-scale fading and angle-ofdeparture (AoD)-based transmit array gain vectors. Here we thus assume that all UAVs have a uniform linear array and are able to perfectly align their beam with the angle-of-departure (AoD) of their array in order to maximize their antenna array gain from them to their serving users.

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Millimeter Wave Communication: A Comprehensive Survey

Cited by 469 publications

References 196 publications

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization

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