235 research outputs found
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Energy Efficiency Optimization for D2D Communications Underlaying UAV-assisted Industrial IoT Networks with SWIPT
The industrial Internet of Things (IIoT) has been viewed as a typical application for the fifth generation (5G) mobile networks. This paper investigates the energy efficiency (EE) optimization problem for the device-to-device (D2D) communications underlaying unmanned aerial vehicles (UAVs)-assisted IIoT networks with simultaneous wireless information and power transfer (SWIPT). We aim to maximize the EE of the system while satisfying the constraints of transmission rate and transmission power budget. However, the designed EE optimization problem is non-convex involving joint optimization of the UAV’s location, beam pattern, power control and time scheduling, which is difficult to tackle directly. To solve this problem, we present a joint UAV location and resource allocation algorithm to decouple the original problem into several sub-problems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one, and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm to optimize the beam pattern. We then optimize UAV’s location and power control using the successive convex optimization techniques. Finally, after solving the above variables, the original problem can be transformed into a single-variable problem with respect to the charging time, which is linear and can be tackled directly. Numerical results verify that significant EE gain can be obtained by our proposed algorithm as compared to the benchmark schemes
Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey
Intelligent reflecting surfaces (IRSs) are considered a promising technology
that can smartly reconfigure the wireless environment to enhance the
performance of future wireless networks. However, the deployment of IRSs still
faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV)
enabled wireless environments to achieve higher capacity. This paper sheds
light on the different deployment strategies for IRSs in future terrestrial and
non-terrestrial networks. Specifically, in this paper, we introduce key
theoretical concepts underlying the IRS paradigm and discuss the design aspects
related to the deployment of IRSs in 6G networks. We also explore
optimization-based IRS deployment techniques to improve system performance in
terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement
learning (RL) techniques from the deployment aspect to address the challenges
of achieving higher capacity in complex and mobile IRS-assisted UAV wireless
systems. Finally, we highlight challenges and future research directions from
the deployment aspect of IRSs for improving system performance for the future
6G network.Comment: 16 pages, 3 Figures, 7 table
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