Joint Optimization of Trajectory and Communication in Multi-UAV Assisted Backscatter Communication Networks

Du, Yu; Chen, Zijing; Hao, Jianjun; Guo, Yijun

doi:10.1109/access.2022.3165159

Cited by 10 publications

(12 citation statements)

References 35 publications

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“…, and its maximum flight speed is max 10 / V m s  . The other system parameter values are listed in Table 1 [12], [15], [16] and [19]. W W W , it can be obviously found that the larger the transmit power, the larger the final convergence of optimal value.…”

Section: Numerical Resultsmentioning

confidence: 99%

Resource Allocation Algorithm for UAV-Aided Symbiotic Radio Communication System

Zhang

Zhu

2023

Preprint

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Aiming at the issue of how to improve the system transmission rate in a multiple Internet of things (IoT) device application scenario, we propose the resource allocation algorithm of the symbiotic radio communication system under multiple backscatter devices (BDs) assisted by unmanned aerial vehicle (UAV). We consider the base station sends signals to cellular users, and multiple terrestrial BDs collect energy and backscatter the RF signals sent by the base station. The UAV receives the backscatter signals of all BDs and collects the data of them, that is, all BDs share the RF sources with the cellular users, forming a symbiotic radio system. We formulate the optimization problem of maximizing BDs’ sum rate by jointly optimizing the time allocation, BDs’ reflection coefficient and UAV location under constraints of BD’s harvested energy, quality of service (QoS) of cellular users and UAV. Since the optimum problem is non-convex, it is difficult to solve directly. Therefore, the iterative algorithm based on block coordinate descent (BCD) method can be adopted, which decomposes the optimization problem into three sub-problems: time allocation, reflection coefficients of BDs and UAV location. For non-convex sub-problems, we utilize the successive convex approximation (SCA) technique to transform it into convex optimization problems, and we prove that the conversion is convex optimization problems. Simulation results show that our proposed algorithm converges fast, and significantly improves system transmission rate compared with other schemes.

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Section: Numerical Resultsmentioning

confidence: 99%

Resource Allocation Algorithm for UAV-Aided Symbiotic Radio Communication System

Zhang

Zhu

2023

Preprint

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“…UAV-assisted communication has been widely investigated since it can avoid obstacles and provide the line-of-sight (LoS) communication with ground devices due to the high mobility of UAVs. Therefore, the UAVs can be regarded as either base stations [14], [15], [16], [17], or mobile relays [18], [19], [20], [21], [22]. Reference [14] investigates the joint optimization of trajectory and resource allocation for the multi-UAV assisted backscatter communication network.…”

Section: A Related Workmentioning

confidence: 99%

“…Therefore, the UAVs can be regarded as either base stations [14], [15], [16], [17], or mobile relays [18], [19], [20], [21], [22]. Reference [14] investigates the joint optimization of trajectory and resource allocation for the multi-UAV assisted backscatter communication network. In [14], the joint optimization of trajectory and resource allocation problem is formulated as a nonconvex optimization problem to maximize the max-min rate of the backscatter communication network.…”

Section: A Related Workmentioning

confidence: 99%

“…Reference [14] investigates the joint optimization of trajectory and resource allocation for the multi-UAV assisted backscatter communication network. In [14], the joint optimization of trajectory and resource allocation problem is formulated as a nonconvex optimization problem to maximize the max-min rate of the backscatter communication network. Particularly, the trajectory design in [14] is proposed to maximize system throughput.…”

Section: A Related Workmentioning

confidence: 99%

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Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Wang

2023

IEEE Access

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Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its nonconvexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.INDEX TERMS Cell-free, satellite-UAV network, power allocation, NOMA.

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“…In these delay-sensitive applications, using Unmanned Aerial Vehicles (UAVs), as mobile base stations, will be a viable solution for real-time remote monitoring of vital signs. As demonstrated in the literature [2]- [4], UAVs provide an efficient and reliable data harvesting system for wireless networks by hovering over outdoor areas. In addition, UAVs can easily access data packets of patients in unreachable areas by providing air-to-ground communication links and enhance throughput and energy efficiency of WBANs by adapting their horizontal locations and altitudes.…”

Section: Introduction a Background And Related Workmentioning

confidence: 99%

A Q-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs

et al. 2022

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Unmanned Aerial Vehicles (UAVs) have emerged as a flexible and cost-effective solution for remote monitoring of the vital signs of patients in large-scale Internet of Medical Things (IoMT) Wireless Body Area Networks (WBANs). This paper deals with the problem of using UAVs for real-time scheduling of the transmission of vital signs in delay-sensitive IoMT WBANs. The main challenge for such a network is to timely and reliably transmit the vital signs of patients to the remote monitoring center without interrupting their daily lifestyles. To achieve this goal, we propose a Q-learning-based algorithm to optimize the trajectory of each UAV, as the mobile Base Station (BS), to harvest vital signs of patients in outdoor applications, especially in unreachable areas. In this algorithm, UAVs learn to reach the best 3D position by discovering the network environment step-by-step. It stands for the position in which the covered patients by each UAV have the highest transmission rate, the least delay and energy consumption. Moreover, we employ the Non-Orthogonal Multiple Access (NOMA) technique to simultaneously schedule multiple transmissions by accepting a degree of interference between them in order to enhance the spectrum efficiency of the network. Eventually, the performance of our proposed scheme is evaluated via extensive simulations in terms of throughput, energy consumption, and delay. The simulation results show that our proposed scheme iteratively converges to the benchmark value of the mentioned factors by increasing the information of cluster environment through episodes.

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Joint Optimization of Trajectory and Communication in Multi-UAV Assisted Backscatter Communication Networks

Cited by 10 publications

References 35 publications

Resource Allocation Algorithm for UAV-Aided Symbiotic Radio Communication System

Resource Allocation Algorithm for UAV-Aided Symbiotic Radio Communication System

Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

A Q-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs

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