Joint Placement Design, Admission Control, and Power Allocation for NOMA-Based UAV Systems

Tang, Rui; Cheng, Julian; Cao, Zhaoxin

doi:10.1109/lwc.2019.2956702

Cited by 30 publications

(38 citation statements)

References 16 publications

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“…The authors of [72] extended [19] to joint NOMA power allocation, user pairing, and UAV placement to maximize the minimum sum rate of user pairs. In [73], joint trajectory and power allocation for a NOMA-aBS serving ground users was investigated, aiming to maximize the minimum average rate, whereas [74] improves the number of satisfied users with quality-of-expereince by optimizing the UAV placement, admission control, and NOMA power. For the uplink, authors in [75] investigated NOMA random access Achieved EE is superior to that of benchmark approaches.…”

Section: Non-orthogonal Multiple Access In Aerial Networkmentioning

confidence: 99%

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Jaafar

Naser

Muhaidat

et al. 2020

IEEE Open J. Veh. Technol.

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Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed. Index Terms-Orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA), unmanned aerial vehicle (UAV), survey. I. INTRODUCTION D UE to the unprecedented growth of mobile data traffic and stringent quality-of-service (QoS) requirements, recent research efforts have focused on several key enabling technologies for 5G networks and beyond [1], such as millimeter waves (mmWave), terahertz (THz) communications, multiple-input multiple-output (MIMO), massive MIMO, multiple access techniques, relaying, cognitive radio, and unmanned aerial networks [2]. In particular, unmanned aerial vehicles (UAVs) have demonstrated great potential in enabling new applications. For instance, UAVs can be used for aerial security inspection, traffic monitoring, smart agriculture, aerial delivery, etc. [3], [4] (and references therein). Furthermore, UAVs can be deployed as aerial base stations (aBSs) to provide wireless access to ground and aerial devices/users, in several scenarios such as temporary events, disasters when a terrestrial cellular network is not fully operational, and congestion due to unpredictable traffic surges, as well as aerial devices/users (e.g., cargo drones) [5], [6].

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Section: Non-orthogonal Multiple Access In Aerial Networkmentioning

confidence: 99%

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Jaafar

Naser

Muhaidat

et al. 2020

IEEE Open J. Veh. Technol.

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show abstract

“…The work in [33] proposes a multiple-input multiple-output (MIMO)-NOMA based UAV network and analyzes the outage probability and ergodic rate with a stochastic geometry model. In [34], the authors investigate the joint UAV placement, admission control, and power allocation to maximize the user number for a heavyloaded NOMA based UAV system. Additionally, exploiting the UAV's high mobility is expected to unlock the full potential of UAV-to-ground communications [35]- [38].…”

Section: B Related Work 1) Terrestrial Networkmentioning

confidence: 99%

“…It is worth noting that it must have ϕ ≥ 0 due to f k,j,n (α k,j [n], q[n]) ≥ 0 within the feasible region of (P3). Inspired by [34], it can be proved that (P4) is equivalent to (P3), when µ ≥ µ * , where µ * is the optimal Lagrange multiplier associated with constraint (17a). In the following, we focus on solving problem (P4).…”

Section: B Joint 3d Uav Trajectory and Power Allocationmentioning

confidence: 99%

“…However, if we directly apply the SCA technique under the consideration of (17b), there will be some iterations where the subproblem is infeasible due to (17b) leading to the failure of obtaining solutions. Inspired by the recent results in [34], [47], we tackle this issue by alternatively considering the relaxation of constraints (17b). Specifically, by introducing slack variables φ, problem (P6) can be rewritten as…”

Section: Sic Order Optimizationmentioning

confidence: 99%

“…[n] and λ * is the optimal Lagrange multiplier associated with constraint (17b). It has been proved in [34] that φ = 0 at the convergence points, i.e., problem (P7) is equivalent to (P6). Now the SCA technique is used to tackle the non-convex constraints in the same way as Section III-B.…”

Section: Sic Order Optimizationmentioning

confidence: 99%

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Cognitive NOMA for UAV-Enabled Secure Communications: Joint 3D Trajectory Design and Power Allocation

Tang

2020

IEEE Access

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In the paper, we investigate the physical layer security (PLS) of an unmanned aerial vehicle (UAV) enabled communication system that integrates non-orthogonal multiple access (NOMA) with cognitive radio (CR), namely CR-NOMA. Specifically, the secondary UAV transmits confidential information to multiple secondary legitimate receivers with NOMA by sharing the spectrum with the primary networks, while an eavesdropper (Eve) attempts to wiretap the communication. We aim to jointly optimize the three-dimensional (3D) UAV trajectory and power allocation to maximize the worst-case average secrecy sum rate of all secondary receivers (SRs) while keeping the interference to primal receivers (PRs) below a certain threshold. The formulated problem is mixed-binary non-convex and is generally NP-hard. To address this issue, we first equivalently transform the binary variables into continuous ones by introducing tractable inequality constraints. Then, we decompose the optimization problem into two subproblems and develop an efficient iterative algorithm based on the successive convex approximate (SCA) technique, block coordinate descent (BCD) technique, and penalty function method. Numerical results are extensively studied to show that the proposed joint design significantly outperforms the benchmark schemes in terms of secrecy rate. INDEX TERMS UAV communications, physical layer security, NOMA, cognitive radio, 3D trajectory design, power allocation.

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Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

Abdelhakam

Elmesalawy

Ibrahim

et al. 2022

Trans Emerging Tel Tech

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Thanks to their mobility, flexibility, and adaptive altitude, unmanned aerial vehicles (UAVs) have been admitted for several applications in wireless communication systems. In particular, UAVs can function as aerial base stations (BSs) to increase cellular network capacity and provide persistent coverage. Due to the line-of-sight channels between UAVs and user equipments (UEs), strong co-channel interference may be generated in the network. In this article, a joint transmission coordinated multi-point technique is considered in the downlink cellular network to effectively manage the interference brought by UAVs. A two-timescale optimization approach is proposed to prevent exchanging absurd messages and channel information between the transmission points. The objective of the proposed method is to maximize the sum rate of the network subject to the constraints on UEs' quality of service as well as per-ground BS and per-UAV total transmitted power. The proposed approach is decomposed into long-term and short-term problems. The long-term problem aims to jointly optimize the UEs' clusters, the UAVs' locations, and the initial transmit beamforming vectors based on large-scale channel gain. This problem is a mixed-integer nonlinear programming optimization problem, which is nondeterministic polynomial hard. Meanwhile, the short-term problem requires obtaining the transmit beamforming vectors according to the overall channel vector. This problem is also a non-convex optimization problem. To address these challenges, two algorithms are proposed based on difference of convex functions programming and successive convex approximation technique to deal with these two problems. Extensive simulations show that the performance of the proposed approach outperforms the comparable algorithms.

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Joint Placement Design, Admission Control, and Power Allocation for NOMA-Based UAV Systems

Cited by 30 publications

References 16 publications

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Cognitive NOMA for UAV-Enabled Secure Communications: Joint 3D Trajectory Design and Power Allocation

Two‐timescale optimization approach for coordinated multi‐point design in unmanned aerial vehicle‐assisted cellular networks

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