Energy-efficient design for mmWave-enabled NOMA-UAV networks

Pang, Xiaowei; Tang, Jie; Zhao, Nan; Zhang, Xiu Yin; Qian, Yi

doi:10.1007/s11432-020-2985-8

Cited by 138 publications

(54 citation statements)

References 33 publications

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“…The transformation is presented in the following inequality, where { p k [n], p J [n], ∀k, n} is a given feasible point. (17) shown in (18) at the top of the next page.…”

Section: B Subproblems 1 and 2: Scheduling And Transmit Power Optimizationmentioning

confidence: 99%

“…Based on the mathematical model for rotary-wing UAVs, Zhan and Lai minimized the UAV energy consumption for the Internet of Things (IoT) systems [16], and Zhang et al focused on the energy efficiency of UAV in a mobile edge computing system [17]. With the aid of non-orthogonal multiple access (NOMA), Pang et al studied the energy-efficient resource allocation for mmWave-enabled UAV networks [18]. Turgut et al presented a novel framework in [19], for analyzing the energy coverage performance of UAV energy harvesting networks.…”

Section: Introductionmentioning

confidence: 99%

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Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

Zhang

Pang

et al. 2021

IEEE Trans. Wireless Commun.

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Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

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“…The transformation is presented in the following inequality, where { p k [n], p J [n], ∀k, n} is a given feasible point. (17) shown in (18) at the top of the next page.…”

Section: B Subproblems 1 and 2: Scheduling And Transmit Power Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

Zhang

Pang

et al. 2021

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The binary variables in (22) makes the problem a mixinteger programming. Thus, we propose a device scheduling strategy according to the varying channel gains between UAV and ground devices to tackle it.…”

Section: Device Scheduling Strategymentioning

confidence: 99%

UAV-Assisted Time-Efficient Data Collection via Uplink NOMA

Wang

Zhao

Chen

et al. 2021

IEEE Trans. Commun.

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Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into N time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

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“…Pang et al allocated downlink power to maximize energy efficiency. The solution to it includes outer iteration and inner iteration, fractional programming, and successive convex optimization [ 7 ]. Chen et al considered the same problem under short packet communication.…”

Section: Related Workmentioning

confidence: 99%

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

Cui

Zhai

et al. 2021

Sensors

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The demand for bandwidth-intensive and delay-sensitive services is surging daily with the development of 5G technology, resulting in fierce competition for scarce radio resources. Power domain Nonorthogonal Multiple Access (NOMA) technologies can dramatically improve system capacity and spectrum efficiency. Unlike existing NOMA scheduling that mainly focuses on fairness, this paper proposes a power control solution for uplink hybrid OMA and PD-NOMA in dual dynamic environments: dynamic and imperfect channel information together with the random user-specific hierarchical quality of service (QoS). This paper models the power control problem as a nonconvex stochastic, which aims to maximize system energy efficiency while guaranteeing hierarchical user QoS requirements. Then, the problem is formulated as a partially observable Markov decision process (POMDP). Owing to the difficulty of modeling time-varying scenes, the urgency of fast convergency, the adaptability in a dynamic environment, and the continuity of the variables, a Deep Reinforcement Learning (DRL)-based method is proposed. This paper also transforms the hierarchical QoS constraint under the NOMA serial interference cancellation (SIC) scene to fit DRL. The simulation results verify the effectiveness and robustness of the proposed algorithm under a dual uncertain environment. As compared with the baseline Particle Swarm Optimization algorithm (PSO), the proposed DRL-based method has demonstrated satisfying performance.

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Energy-efficient design for mmWave-enabled NOMA-UAV networks

Cited by 138 publications

References 33 publications

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

UAV-Assisted Time-Efficient Data Collection via Uplink NOMA

Dual Dynamic Scheduling for Hierarchical QoS in Uplink-NOMA: A Reinforcement Learning Approach

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