UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio System

Hua, Meng; Yang, Luxi; Wu, Qingqing; Pan, Cunhua; Li, Chunguo; Swindlehurst, A. Lee

doi:10.1109/twc.2021.3070014

Cited by 166 publications

(101 citation statements)

References 47 publications

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“…where (17c) makes (17) a mixed-integer problem, which is non-convex. To handle (17c), we introduce slack variable χ l m and it can be verified that (17c) is equivalent to α l m = χ l m , and α l m 1 − χ l m = 0 [15]. Then, we penalize these terms which are included in the OF, and obtain the following problem…”

Section: Optimization Of the Tarcsmentioning

confidence: 99%

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Simultaneous Transmission and Reflection Reconfigurable Intelligent Surface Assisted MIMO Systems

Niu,

Chu,

Zhou

et al. 2021

Preprint

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In this work, we investigate a novel simultaneous transmission and reflection reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output downlink system, where three practical transmission protocols, namely, energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. For the system under consideration, we maximize the weighted sum rate with multiple coupled variables. To solve this optimization problem, a block coordinate descent algorithm is proposed to reformulate this problem and design the precoding matrices and the transmitting and reflecting coefficients (TARCs) in an alternate manner. Specifically, for the ES scheme, the precoding matrices are solved using the Lagrange dual method, while the TARCs are obtained using the penalty concaveconvex method. Additionally, the proposed method is extended to the MS scheme by solving a mixedinteger problem. Moreover, we solve the formulated problem for the TS scheme using a one-dimensional search and the Majorization-Minimization technique. Our simulation results reveal that: 1) Simultaneous transmission and reflection RIS (STAR-RIS) can achieve better performance than reflecting-only RIS;2) In unicast communication, TS scheme outperforms the ES and MS schemes, while in broadcast communication, ES scheme outperforms the TS and MS schemes.

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Section: Optimization Of the Tarcsmentioning

confidence: 99%

“…For given {φ l , Ω l }, the optimal χ l m can be obtained by the first-order optimal condition, which is given by χ l m = α l m + α l m 2 1 + α l m 2 [15]. On the other hand, for given χ l m , (18) can be solved by the previously proposed method.…”

Section: Optimization Of the Tarcsmentioning

confidence: 99%

Simultaneous Transmission and Reflection Reconfigurable Intelligent Surface Assisted MIMO Systems

Niu,

Chu,

Zhou

et al. 2021

Preprint

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“…The authors in [42] analyze the ergodic capacity, SNR outage probability, and EE under three distinct modes, i.e., only UAV, only RIS, and integrated UAV-RIS modes. In [43], the data rates of all RISs to a given BS are maximized by exploiting the UAV. More specifically, a maximization-minimization optimization problem is investigated by jointly optimizing the phase shifts at the RIS, UAV's trajectory, and RIS scheduling.…”

Section: ) Uav-ris-based Systemsmentioning

confidence: 99%

Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

Khalili¹,

Monfard²,

Zargari³

et al. 2021

Preprint

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This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs) . More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave (µW) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the µW channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs' phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs' trajectories/velocities, RISs' phase shifts, and subcarrier allocations for µW; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the

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“…Currently, there is limited research on the RIS-assisted UAV communications. In [16][17][18][19][20], the RIS relayed the UAV downlink signals to maximize the transmission rate or minimize the bit error rate through optimal power allocation and phase shift designing [21]. In addition, the non-orthogonal multiple access (NOMA) was adopted in [19] to improve the spectrum efficiency, where multiple groups of ground users were serviced.…”

Section: Introductionmentioning

confidence: 99%

Passive Beamforming and Trajectory Optimization for Reconfigurable Intelligent Surface-Assisted UAV Secure Communication

Wang

Zhao

et al. 2021

Remote Sensing

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Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) methods are promising techniques for improving the energy and spectrum efficiency of Fifth Generation/Beyond Fifth Generation (5G/B5G) networks. In order to take advantage of both techniques, we propose an RIS-assisted UAV secure communication scheme, where an UAV is equipped with RIS to facilitate secure transmission. To maximize the average secrecy rate, we jointly optimize the beamforming power, reflect phase shift, and UAV’s trajectory. For this non-convex problem, we decompose it into the power beamforming problem, the phase shift optimization problem, and the UAV’s trajectory design problem, and proposed an efficient iterative algorithm to solve the problem. The numerical results verify the superiority of the proposed scheme that can improve the average secure transmission rate by about 20% compared to that of Eavesdropping elimination methods.

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UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio System

Cited by 166 publications

References 47 publications

Simultaneous Transmission and Reflection Reconfigurable Intelligent Surface Assisted MIMO Systems

Simultaneous Transmission and Reflection Reconfigurable Intelligent Surface Assisted MIMO Systems

Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

Passive Beamforming and Trajectory Optimization for Reconfigurable Intelligent Surface-Assisted UAV Secure Communication

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