UAV-Enabled Jamming Noise for Achieving Secure Communications in Cognitive Radio Networks

Nguyen, Phu X.; Nguyen, Hieu V.; Nguyen, Van‐Dinh; Shin, Oh‐Soon

doi:10.1109/ccnc.2019.8651678

Cited by 22 publications

(28 citation statements)

References 13 publications

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Section: B Related Work 1) Terrestrial Networkmentioning

confidence: 99%

“…There exist a few works in the literature focusing on the security of UAV-aided CR networks [29], [30]. The work in [29] deploys UAV as a mobile relay to assist communication from a secondary transmitter (ST) to a secondary receiver (SR) in the presence of an Eve, while the work in [30] employs UAV as a friendly jammer to interfere with the Eve. Moreover, the security of cognitive UAV communication networks is studied in [31], [32], where a cognitive/secondary UAV acts as a transceiver to communicate with an SR. With imperfect information regarding the locations of PRs and Eves, a joint robust two-dimensional (2D) trajectory and transmit power design is investigated in [32] to maximize the average secrecy rate by taking into account two practical inaccurate location estimations, i.e., the worst case and the outage-constrained case.…”

Section: B Related Work 1) Terrestrial Networkmentioning

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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Section: B Related Work 1) Terrestrial Networkmentioning

confidence: 99%

Section: B Related Work 1) Terrestrial Networkmentioning

confidence: 99%

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

Tang

2020

IEEE Access

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“…Yang et al in [10] investigate the different Pareto efficiency between the optimal GU transmit power and UAV trajectory design. Phu et al [11] use UAV as a friendly jammer to maximize the average secrecy rate of the cognitive radio network (CRN) by jointly optimizing the transmission power and UAV trajectory. Reference [9] designs the trajectory of UAV to minimize the mission completion time in UAV-enabled multi-casting systems based on the traveling salesman problem (TSP).…”

Section: Introductionmentioning

confidence: 99%

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Tran

Chatzinotas

et al. 2020

IEEE Trans. Veh. Technol.

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In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search's performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the stateof-the-art benchmarks in terms of both energy consumption and outage performance.

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“…In [40], the authors deploy a UAV as a secondary relay to bridge the communication between a secondary transmitterreceiver pair in the presence of a PR that operate over the same frequency band and a potential eavesdropper. Reference [41] considers a scenario where a UAV is introduced as a friendly jammer to help improve the secrecy rate performance of a CR network. However, only one eavesdropper with perfect location information and one PR are considered in [40] and [41], which is overly optimistic and extremely simplifies the practical consideration.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [41] considers a scenario where a UAV is introduced as a friendly jammer to help improve the secrecy rate performance of a CR network. However, only one eavesdropper with perfect location information and one PR are considered in [40] and [41], which is overly optimistic and extremely simplifies the practical consideration.…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory and Power Control for Cognitive UAV Secrecy Communication

Gao

Tang

et al. 2020

IEEE Access

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This paper investigates the physical layer security issue in an unmanned aerial vehicle (UAV) aided cognitive radio network. Specially, a UAV operates as an aerial secondary transmitter to serve a ground secondary receiver (SR) by sharing the licensed wireless spectrum assigned to primary terrestrial communication networks, and in the meantime multiple eavesdroppers (Eves) try to wiretap the legitimate UAV-to-SR link. Under the assumption that the location formation of the Eves is imperfect, we jointly optimize the robust trajectory and transmit power of the UAV over a finite flight period to maximize the SR's average worst-case secrecy rate, while controlling the co-channel interference imposed on the primary receivers (PRs) below a tolerable level. The design is formulated as a non-convex semi-infinite optimization problem that is challenging to be optimally solved. To deal with it, we first prove that the considered problem can be simplified as a more tractable one, which resolves the location uncertainties of the Eves without the aid of S-Procedure adopted in conventional methods. After that, an efficient iterative algorithm based on successive convex approximation (SCA) is developed to obtain a locally optimal solution. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and offer important system design insights.

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UAV-Enabled Jamming Noise for Achieving Secure Communications in Cognitive Radio Networks

Cited by 22 publications

References 13 publications

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

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

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Robust Trajectory and Power Control for Cognitive UAV Secrecy Communication

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