Base-Stations Up in the Air: Multi-UAV Trajectory Control for Min-Rate Maximization in Uplink C-RAN

Roth, Stefan; Kariminezhad, Ali; Sezgin, Aydin

doi:10.1109/icc.2019.8761630

Cited by 13 publications

(11 citation statements)

References 18 publications

(23 reference statements)

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“…For simplicity, we model the UAV movement in a horizontal plane with coordinates s x and s y only, while neglecting the height. The UAVs are represented by a linear state space model according to (1) and ( 2), which is based on the linearized model from [9], [10]. The state vector is defined as x = s x ṡx ϑ θ s y ṡy φ φ , where ϑ and φ are the roll and pitch Euler angles, respectively.…”

Section: Uav Modelmentioning

confidence: 99%

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An Energy-Efficient Event-Based MIMO Communication Scheme for UAV Formation Control

Karacora¹,

Sezgin²

2020

Preprint

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We consider a leader-follower formation control setup as an example for a multi-agent networked control system (NCS). This paper proposes an event-based wireless communication scheme with a MIMO precoder, that dynamically adapts to both the channel and the current control state. We apply a Lyapunov drift approach for optimizing the precoder and for defining an event-triggering policy. To evaluate the performance of the proposed scheme, we simulate a formation control of two unmanned aerial vehicles (UAVs) connected via a point-topoint MIMO fading channel. Compared to a benchmark scheme with periodic transmissions and a basic water-filling algorithm, we demonstrate that our proposed event-based scheme is more efficient by consuming less energy and requiring less frequent transmissions on average.

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Section: Uav Modelmentioning

confidence: 99%

“…Hence, the objective function can be reduced to Tr((A H C H PCA − C H PC)Σ k|k ). Using ( 9) and (10) and then applying the Woodbury Matrix Identity, the a-posteriori error covariance can be written as…”

Section: Lyapunov Drift Optimizationmentioning

confidence: 99%

An Energy-Efficient Event-Based MIMO Communication Scheme for UAV Formation Control

Karacora¹,

Sezgin²

2020

Preprint

Self Cite

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“…[7] studied coordinated multipoint (CoMP) in CRAN by using UAVs as RRHs and dynamically optimized the UAV placement for max-min rate fairness. [8] considered multi-UAV trajectory control to maximize the minimum rate. In both [7] and [8] the fronthaul links were assumed to have unlimited/very high capacity.…”

Section: Introductionmentioning

confidence: 99%

“…[8] considered multi-UAV trajectory control to maximize the minimum rate. In both [7] and [8] the fronthaul links were assumed to have unlimited/very high capacity. [9] maximized the sum-rate in the uplink of CRAN with LoS fronthaul and access links by optimizing the UE association, UAV placement, and UEs' and UAVs' transmit powers.…”

Section: Introductionmentioning

confidence: 99%

Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-Enabled CRANs

Huang

Ikhlef

2021

IEEE Wireless Commun. Lett.

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This paper proposes a novel architecture of multiunmanned aerial vehicle (UAV)-enabled cloud radio access network (CRAN). In particular, we propose to deploy the UAVs as flying remote radio heads (RRHs) to serve ground user equipments (UEs). The baseband unit (BBU) is equipped with a large-scale antenna array to serve the flying RRHs and affords all the baseband signal processing. To optimize the proposed architecture, we consider the maximization of the minimum rate of UEs by jointly optimizing UAVs placement, quantization noise variance, and power control. The corresponding optimization problem is not convex and to solve it we devise an efficient iterative algorithm combining the block coordinate descent and successive convex optimization methods. Numerical results demonstrate the superior performance of the proposed algorithm compared to two benchmark schemes.

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“…Power efficient deployment of UAVs as relays is studied in [18] for centralized and distributed UAV selection scenarios. Trajectory control of UAVs for maximizing the worst terrestrial user's spectral efficiency is studied in [19]. The deployment of UAVs for user-in-the-loop scenarios have also been considered [20].…”

Section: Introductionmentioning

confidence: 99%

Outage-Optimized Deployment of UAVs

Shabanighazikelayeh

Koyuncu

2019

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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We consider multiple unmanned aerial vehicles (UAVs) serving a density of ground terminals (GTs) as mobile base stations. The objective is to minimize the outage probability of GT-to-UAV transmissions. In this context, the optimal placement of UAVs under different UAV altitude constraints and GT densities is studied. First, using a random deployment argument, a general upper bound on the optimal outage probability is found for any density of GTs and any number of UAVs. Lower bounds on the performance of optimal deployments are also determined. The upper and lower bounds are combined to show that the optimal outage probability decays exponentially with the number of UAVs for GT densities with finite support. Next, the structure of optimal deployments are studied when the common altitude constraint is large. In this case, for a wide class of GT densities, it is shown that all UAVs should be placed to the same location in an optimal deployment. A design implication is that one can use a single multi-antenna UAV as opposed to multiple single-antenna UAVs without loss of optimality. Numerical optimization of UAV deployments are carried out using particle swarm optimization. Simulation results are also presented to confirm the analytical findings.Index Terms-UAV-aided communications, optimal placement, outage probability.

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Base-Stations Up in the Air: Multi-UAV Trajectory Control for Min-Rate Maximization in Uplink C-RAN

Cited by 13 publications

References 18 publications

An Energy-Efficient Event-Based MIMO Communication Scheme for UAV Formation Control

An Energy-Efficient Event-Based MIMO Communication Scheme for UAV Formation Control

Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-Enabled CRANs

Outage-Optimized Deployment of UAVs

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