183 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
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism
THz band communication technology will be used in the 6G networks to enable
high-speed and high-capacity data service demands. However, THz-communication
losses arise owing to limitations, i.e., molecular absorption, rain
attenuation, and coverage range. Furthermore, to maintain steady
THz-communications and overcome coverage distances in rural and suburban
regions, the required number of BSs is very high. Consequently, a new
communication platform that enables aerial communication services is required.
Furthermore, the airborne platform supports LoS communications rather than NLoS
communications, which helps overcome these losses. Therefore, in this work, we
investigate the deployment and resource optimization for MEC-enabled UAVs,
which can provide THz-based communications in remote regions. To this end, we
formulate an optimization problem to minimize the sum of the energy consumption
of both MEC-UAV and MUs and the delay incurred by MUs under the given task
information. The formulated problem is a MINLP problem, which is NP-hard. We
decompose the main problem into two subproblems to address the formulated
problem. We solve the first subproblem with a standard optimization solver,
i.e., CVXPY, due to its convex nature. To solve the second subproblem, we
design a RMAPPO DRL algorithm with an attention mechanism. The considered
attention mechanism is utilized for encoding a diverse number of observations.
This is designed by the network coordinator to provide a differentiated fit
reward to each agent in the network. The simulation results show that the
proposed algorithm outperforms the benchmark and yields a network utility which
is , , and more than the benchmarks.Comment: 13 pages, 12 figure
Resource allocation optimization for future wireless communication systems
To meet the ever-increasing requirements of high data rate, extremely low latency, and ubiquitous connectivity for the fifth generation (5G) and beyond 5G (B5G) wireless communications, there is imperious demands for advanced communication system design. Particularly, efficient resource allocation is regarded as the fundamental challenge whereas an effective way to improve system performance. The term ”resource” refers to scare quantities such as limited bandwidth, power and time in wireless communications. Moreover, the development of wireless communication systems is accompanied by the innovation of applied technologies. Motivated by the above observations, efficient resource allocation strategies for several promising 5G and B5G technologies in terms of non-orthogonal multiple access (NOMA), mobile edge computing (MEC) and Long Range (LoRa) are addressed and investigated in this thesis. Firstly, the strong user’s data rate maximization problem for simultaneous wireless information and power transfer (SWIPT)-enabled cooperative NOMA system, considering the presence of channnel uncertainties, is proposed and investigated. Two major channel uncertainty design criteria in terms of the outage-based constraint design and the worst-case based optimization are adopted. In addition to the high-complexity optimal two-dimensional exhaustive search method, the low-complexity suboptimal solution is further proposed. The advantages of SWIPT-enabled cooperation in robust NOMA are confirmed with simulations. Secondly, considering the application of NOMA and user cooperation (UC) in a wireless powered MEC under the non-linear energy harvesting model, a computation efficiency maximization problem subject to the quality of service (QoS) and power budget constraint, is studied and analyzed. The formulated problem is nonconvex, which is challenging to solve. The semidefinite relaxation (SDR) approach is first applied, then the sequential convex approximation (SCA)-based solution is further proposed to maximize the system computation efficiency. Finally, taking into consideration the aspect of energy efficiency (EE), this thesis investigates the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. The energy efficient resource allocation is formulated as NP-hard problems. A low-complexity user scheduling scheme based on matching theory is proposed to allocate users to channels, then the heuristic SF assignment solution is designed for LoRa users scheduled on the same channel. The optimal power allocation strategy is further proposed to maximize the corresponding EE
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
A High Altitude Platform Station (HAPS) is a network node that operates in
the stratosphere at an of altitude around 20 km and is instrumental for
providing communication services. Precipitated by technological innovations in
the areas of autonomous avionics, array antennas, solar panel efficiency
levels, and battery energy densities, and fueled by flourishing industry
ecosystems, the HAPS has emerged as an indispensable component of
next-generations of wireless networks. In this article, we provide a vision and
framework for the HAPS networks of the future supported by a comprehensive and
state-of-the-art literature review. We highlight the unrealized potential of
HAPS systems and elaborate on their unique ability to serve metropolitan areas.
The latest advancements and promising technologies in the HAPS energy and
payload systems are discussed. The integration of the emerging Reconfigurable
Smart Surface (RSS) technology in the communications payload of HAPS systems
for providing a cost-effective deployment is proposed. A detailed overview of
the radio resource management in HAPS systems is presented along with
synergistic physical layer techniques, including Faster-Than-Nyquist (FTN)
signaling. Numerous aspects of handoff management in HAPS systems are
described. The notable contributions of Artificial Intelligence (AI) in HAPS,
including machine learning in the design, topology management, handoff, and
resource allocation aspects are emphasized. The extensive overview of the
literature we provide is crucial for substantiating our vision that depicts the
expected deployment opportunities and challenges in the next 10 years
(next-generation networks), as well as in the subsequent 10 years
(next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial
Maximizing the latency fairness in UAV-assisted MEC system
Unmanned aerial vehicles (UAV) assisted edge computing has risen as an assuring technique to accommodate ubiquitous edge computation for resource-limited devices. Thus, this paper proposes an approach to maximize the latency fairness in a UAV-assisted multi-access edge computing (MEC) system. To maximize latency fairness, the authors focus on minimizing the maximum latency experienced among the users. In here, multiple ground users (GUs) offload their tasks to MEC UAV in the absence or unavailability of ground servers due to a disaster or heavy traffic where an iterative algorithm is proposed to minimize the maximum latency among the users subject to minimum control link rate and total power constraints. Sequentially, the UAVs' 3D location, offloading ratio, GUs' transmit power and GUs' computational capacity are optimized. The location of the UAV is optimized by using the novel approach, guided pattern search algorithm while the altitude of the UAV is optimized by analyzing the elevation angle dependant behaviour of the channel gain. A simple approach is utilized for optimizing the offloading ratio of the users by considering the problem as minimizing the point-wise maximum of two convex functions while the bisection method is used to optimize the power allocation. Numerical simulation results illustrate that the proposed approach outperforms other baseline approaches in convergence, minimizing the maximum latency and maximizing and maintaining the fairness among the GUs. Furthermore, it is proved that the guided pattern search algorithm converges at least 3.5 times better while the proposed combined optimization gives 400% fairness gain, in comparison with the baseline approach
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