Joint optimization of transmission and propulsion in aerial communication networks

Faqir, Omar J.; Kerrigan, Eric C.; Gündüz, Deniz

doi:10.1109/cdc.2017.8264241

Cited by 13 publications

(8 citation statements)

References 42 publications

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Section: B Contributionsmentioning

confidence: 99%

“…For example, one use case is that the UAVs collect data from a field and deliver the data to their respective serving GTs [4,5]. Note that the UAV-IC considered in this paper is generic and the techniques developed here can be generalized to other similar scenarios with multiple UAVs [22][23][24].In contrast to traditional interference channels with static terminals [25,26], the UAV-IC allows one to exploit the mobility of UAVs as a new degree of freedom to dynamically control the interference among K communication links via joint trajectory optimization and power control. Therefore, we formulate a joint TPC problem for maximizing the aggregate sum rate of all UAV-GT pairs.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Shen

Chang

Gong

et al. 2020

IEEE Trans. Signal Process.

132

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In this paper, we consider an unmanned aerial vehicle-enabled interference channel (UAV-IC), where each of the K UAVs communicates with its associated ground terminals (GTs) at the same time and over the same spectrum. To exploit the new degree of freedom of UAV mobility for interference coordination between the UAV-GT links, we formulate a joint trajectory and power control (TPC) problem for maximizing the aggregate sum rate of the UAV-IC for a given flight interval, under the practical constraints on the UAV flying speed, altitude, as well as collision avoidance. These constraints couple the TPC variables across different time slots and UAVs, leading to a challenging large-scale and non-convex optimization problem. By exploiting the problem structure, we show that the optimal TPC solution follows the fly-hover-fly strategy, based on which the problem can be handled by firstly finding an optimal hovering locations followed by solving a dimension-reduced TPC problem with given initial and hovering locations of UAVs. For the reduced TPC problem, we propose a successive convex approximation algorithm. To improve the computational efficiency, we further develop a parallel TPC algorithm that is effciently implementable over multi-core CPUs. We also propose a segment-bysegment method which decomposes the TPC problem into sequential TPC subproblems each with a smaller problem dimension. Simulation results demonstrate the superior computation time efficiency of the proposed algorithms, and also show that the UAV-IC can yield higher network sum rate than the benchmark orthogonal schemes.UAV trajectory design for data collection in wireless sensor networks, and the authors in [17] studied an interesting throughput-delay trade-off for UAV-enabled multi-user system. Reference [18] considered the UAV heading optimization for an uplink scenario with multiple antennas at the UAV. A general UAV-enabled radio access network (RAN) supporting multi-mode communications of the ground users was considered in [19], where new designs for the UAV initial trajectory were proposed. The work [20] utilized the UAV to offer dynamic computation offloading for GTs. Besides, a UAV-enabled wireless power transfer (WPT) system is studied in [21], where the harvested energy profile of GTs is characterized by optimizing the UAV trajectory.Besides the works considering single UAV only, references [22-24] studied more complicated scenarios with multiple UAVs. Specifically, [22] considered a scenario with multiple UAVs communicating with one GT and derived the optimal TPC for minimizing both transmission and propulsion energy. [23] considered the joint TPC and user association/scheduling problem for multiple UAVs serving multiple GTs.[24] considered the use of multiple UAVs for coordinated multipoint (CoMP) transmission to serve a set of GTs, and derived the optimal TPC and UAV deployment solutions for maximizing the ergodic sum rate of users under random channel phases. B. ContributionsIn this paper, we study a UAV-enabled interference channel (UAV-I...

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Section: B Contributionsmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Shen

Chang

Gong

et al. 2020

IEEE Trans. Signal Process.

132

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“…For solar-powered UAV, the authors gave the solar cells energy consumption expression and verified it via the virtual flight evaluation system [10]. The last category is the theoretical-driven energy model by kinematic and aircraft theory [15], [16], such as [17]- [21]. For example, the authors in [17] have defined an energy model as an integral function of the motor torque and rotor speed, where the motor torque is modeled as being proportional to the current through the torque constant.…”

Section: A Energy Model Reviewmentioning

confidence: 99%

Energy model for UAV communications: Experimental validation and model generalization

et al. 2021

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Wireless communication involving unmanned aerial vehicles (UAVs) is expected to play an important role in future wireless networks. However, different from conventional terrestrial communication systems, UAVs typically have rather limited onboard energy on one hand, and require additional flying energy consumption on the other hand, which renders energyefficient UAV communication with smart energy expenditure of paramount importance. In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible. Specifically, we first investigate how UAV power consumption varies with its flying speed for the simplest straightand-level flight. With about 12,000 valid power-speed data points collected, we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature. In addition, in order to exclude the potential bias caused by the theoretical energy model, the obtained measurement data is also trained using a modelfree deep neural network. It is found that the obtained curve from both methods can match quite well with the theoretical energy model. Next, we further extend the study to arbitrary 2-dimensional (2-D) flight, where, to our best knowledge, no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed, direction, and acceleration. To fill the gap, we first propose a heuristic energy model for these more complicated cases, and then provide experimental validation based on the measurement results for circular level flight.

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“…3D-MIMO can not only utilize the spatial freedom of large-scale transmit antennas but also adjust the direction of the transmit beam in horizontal and vertical dimensions, which improves spatial resolution, improves signal power, and reduces inter-cell interference [3]. UAV communications have been widely used in wireless communications recently such as communication relay, information dissemination, data collection and so on, due to the controllable mobility and everdecreasing manufacturing cost [5,6]. Sharma et al [7] concentrated on a simple path loss and shadow fading channel model that is commonly used to describe the propagation between an aerial base station and a user on the ground.…”

Section: Introductionmentioning

confidence: 99%

Low complexity channel estimation algorithm using paired spatial signatures for UAV 3D MIMO systems

Xie

Wan

et al. 2021

J Wireless Com Network

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This paper proposes a low complexity channel estimation algorithm for unmanned aerial vehicle three dimension multi-user multiple-input-multiple-output (3D MIMO) systems with the uniform planar array (UPA) at base station using paired spatial signatures. With the aid of antenna array theory and array signal processing, 3D channel is firstly modeled based on the angles between the direction of arrival along x- and y-axis of the UPA. And 3D MIMO channels can be projected onto the x- and y-directions, respectively. Then, channel estimation for multi-user uplinks using small amount of training resources is divided into two phases. At the first uplink preamble phase, each user is assigned the orthogonal pilot, and the paired spatial signatures and optimal rotation angle of each user through the same pilot sequence are obtained. We also propose a user grouping strategy based on three-dimension angle-division multiple access (3D-ADMA) to ensure that the user's spatial signatures do not overlap. At the second phase during several coherence times, the same pilot sequence within a group and orthogonal pilot sequences between groups are assigned, then, the channel state information of the user's x- and y-directions are recovered by the paired space signatures and optimal rotation angle of each user obtained in the preamble phase, respectively. And dynamically updating the user's paired spatial signatures and optimal rotation angle utilizes the obtained channel parameter of x- and y-directions. Finally, the channel parameter of the x- and y-directions are reconstructed by the updated user's space signatures and the optimal rotation angle, and the 3D MIMO channel estimation is obtained through the Kronecker product. Compared with the conventional channel estimation method of the 3D MIMO system under UPA using a low-rank model, the proposed methods reduce the computational complexity without degrading the estimated performance to a large extent. Furthermore, it is carried out with limited training resources, and the pilot resource overhead of the system is greatly reduced by the 3D-ADMA packet and the two-stage pilot allocation. Simulation results verified that the proposed algorithm is effective and feasible.

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Joint optimization of transmission and propulsion in aerial communication networks

Cited by 13 publications

References 42 publications

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Multi-UAV Interference Coordination via Joint Trajectory and Power Control

Energy model for UAV communications: Experimental validation and model generalization

Low complexity channel estimation algorithm using paired spatial signatures for UAV 3D MIMO systems

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