UAV Placement and Bandwidth Allocation for UAV Based Wireless Networks

Nguyen, Minh-Le; Ho, Tai Manh; Le, Long Bao; Girard, André

doi:10.1109/globecom38437.2019.9014076

Cited by 28 publications

(14 citation statements)

References 11 publications

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“…Recent years, many studies have spotted on how to optimize the UAVs placement to enhance the system performance. The common idea is to model the issue as an optimization problem with different constrains, such as coverage [13]- [15], throughput [16], [17], data rate [18], delay [19]- [21], energy efficiency [22], [23] and etc. According to how to solve such multi-constraints optimization, the work can briefly divided into three categories, i.e., convex based approach, learning based approach and heuristic based approach.…”

Section: Related Workmentioning

confidence: 99%

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang

Gao

2021

IEEE Access

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Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixedinteger optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this paper, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.

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Section: Related Workmentioning

confidence: 99%

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang

Gao

2021

IEEE Access

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“…The proposed mathematical model (6a) is a binary fractional problem, and is linearized according to [ In (14) and (15), the binary variables a l and b ul are multiplied with continuous variable π, which introduces non-linearlity in the problem and need to be removed. Let us suppose a binary variable ψ is multiplied with a continuous variable ρ,…”

Section: Appendix a Linearization Of Optimization Problemmentioning

confidence: 99%

“…In [60], the multiplication is expressed as linear inequalities as, (i) ρ − ϕ ≤ 1 − ψ, (ii) ϕ ≤ ρ, (iii) ϕ ≤ ψ, where ϕ ≥ 0. Similarly, in (14) and (15), assuming z l = a l π, ∀l and w ul = b ul π, ∀u, l. These multiplicative terms in (14) and 15 w ul = 1 (16b)…”

Section: Appendix a Linearization Of Optimization Problemmentioning

confidence: 99%

Joint Optimization of Sensors Association and UAVs Placement in IoT Applications With Practical Network Constraints

2021

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“…To overcome the limited backhaul capacity of ABSs, in [19], non-orthogonal multiple access (NOMA) was employed on backhaul transmission, and the authors jointly optimized the resource allocation at the MBS and ABSs along with the decoding order of the NOMA process and the positions of the ABSs to maximize the sum of achievable rate of the users. In [20], the total data rate of the users is maximized in a network with multiple ABSs with fixed altitudes, while taking into account the backhaul capacity limitations of ABSs. In [21], an interference management algorithm is proposed to maximize the sum rate of the network while optimizing the user-BS association and power allocations.…”

Section: A Related Workmentioning

confidence: 99%

Wireless Networks with Cache-Enabled and Backhaul-Limited Aerial Base Stations

Kalantari¹,

Yanıkömeroğlu²,

Yongaçoğlu³

2020

Preprint

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Use of aerial base stations (ABSs) is a promising approach to enhance the agility and flexibility of future wireless networks. ABSs can improve the coverage and/or capacity of a network by moving supply towards demand. Deploying ABSs in a network presents several challenges such as finding an efficient 3D-placement of ABSs that takes network objectives into account. Another challenge is the limited wireless backhaul capacity of ABSs and consequently, potentially higher latency incurred. Content caching is proposed to alleviate the backhaul congestion and decrease the latency. We consider a limited backhaul capacity for ABSs due to varying position-dependent path loss values and define two groups of users (delay-tolerant and delay-sensitive) with different data rate requirements. We study the problem of jointly determining backhaul-aware 3D placement for ABSs, user-BS associations and corresponding bandwidth allocations while minimizing total downlink transmit power. Proposed iterative algorithm applies a decomposition method. First, the 3D locations of ABSs are found using semidefinite relaxation and coordinate descent methods, and then user-BS associations and bandwidth allocations are optimized. The simulation results demonstrate the effectiveness of the proposed algorithm and provide insights about the impact of traffic distribution and content caching on transmit power and backhaul usage of ABSs.

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UAV Placement and Bandwidth Allocation for UAV Based Wireless Networks

Cited by 28 publications

References 11 publications

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Joint Optimization of Sensors Association and UAVs Placement in IoT Applications With Practical Network Constraints

Wireless Networks with Cache-Enabled and Backhaul-Limited Aerial Base Stations

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