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JournalISSN: 0018-9545

IEEE Transactions on Vehicular Technology 

About: IEEE Transactions on Vehicular Technology is an academic journal. The journal publishes majorly in the area(s): Fading & MIMO. It has an ISSN identifier of 0018-9545. Over the lifetime, 13359 publication(s) have been published receiving 469219 citation(s).
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Journal ArticleDOI
Wenfei Tang1, Hongtao Zhang1, Yuan He1Institutions (1)
Abstract: In urban UAV networks, UAVs deployment locations and air-to-ground (AtG) communication links will possibly conflict with densely located buildings, which exacerbate network irregularity and make interference management more complicated. This paper proposes interference coordination via power control under 3D blockage effects in urban environment, where building blockages are formed a Boolean model with independent heights (Hk) followed Rayleigh distribution, and analyzes system performance using tools from stochastic geometry. Specifically, a dynamic UAV group (UAVG) is organized to serve each user for interference coordination, which consists several nearest visible UAVs with line-of-sight (LOS) connections considering buildings blockage effects, and the modified distribution from user to its nearest unblocked UAV is derived. Power control is executed in UAVG where adjacent interfering UAVs within the group will mute their transmission for interference mitigation, and optimal UAVG radius coefficient is obtained, which reveals the trade off between interference mitigation and resource utilization. Leveraging stochastic geometry, theoretical expressions of network metrics are derived with Nakagami-m fading assumption, including network coverage probability and network connectivity. Analytical results show that in urban city scenario, comparing with traditional terrestrial networks, coverage performance can achieve 4.1 gain by deploying UAVs with optimal height, and achieve additional 26% gain.

1 citations

Journal ArticleDOI
Ersin H. Dogruguven, Ilker Ustoglu1Institutions (1)
Abstract: In the above paper [1] there are errors introduced during typesetting which we correct here.

Journal ArticleDOI
Zichao Zhao1, Junjuan Xia1, Lisheng Fan1, Xianfu Lei2  +2 moreInstitutions (4)
Abstract: This paper investigates a wireless federated learning (FL) network with limited communication bandwidth, where multiple mobile clients train their individual models with the help of one central server. We consider the practical communication scenarios, where the clients should complete the local computation and model upload within a defined latency. By jointly exploiting the dynamic characteristics of wireless channels and computational capability at the clients, we optimize the federated learning network by maximizing the number of active clients under the constraints of both latency and bandwidth. Specifically, we propose two bandwidth allocation (BA) schemes, where scheme I is based on the instantaneous channel state information (CSI), while scheme II employs the particle swarm optimization (PSO) method, based on the statistical CSI. Simulation results on the test accuracy and convergence rate are finally provided to demonstrate the advantages of the proposed optimization schemes for the considered FL network.

8 citations

Journal ArticleDOI
Abstract: The equivalent consumption minimization strategy (ECMS) has been considered as a practical energy management strategy for the hybrid electric vehicles (HEVs) because it can be implemented in real time while providing satisfactory performance. However, it is still challenging to adjust an equivalent factor (EF) to its own optimal value in real time because the EF is fundamentally affected by the current driving condition. Although many adaptive ECMSs (A-ECMSs) have been developed to adjust the EF based on a charge-sustaining condition, they do not adequately respond to a change in the driving conditions. In this study, a novel ECMS for the HEVs is proposed to provide the near-optimal performance by considering actual driving conditions. First, the near-optimal condition for the EF is defined to consider a driving condition. Based on it, an iterative scheme is presented to numerically obtain the near-optimal EF. Then, the convergence analysis of the iterative scheme is conducted with practical considerations to implementing the proposed method into real-world applications. Simulation results show that the proposed strategy has better adaptability to changes in the driving conditions with a smaller loss of optimality than conventional A-ECMS which relies only on the charge-sustaining condition. The proposed strategy is also experimentally validated under real-world driving conditions.

1 citations

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