Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks

Wang, Dawei; Zhou, Fuhui; Lin, Wensheng; Ding, Zhengtao; Al-Dhahir, Naofal

doi:10.1109/tccn.2022.3164928

Cited by 41 publications

(15 citation statements)

References 49 publications

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“…In this work, q is initialized randomly, and p is initially set to the maximum nominal value. The following steps are then Algorithm 1 Centralized Resource Management 1: Initialize q and p to feasible values Repeat: until convergence 2: Update weights β using ( 24), (35) 3: Update q, r, z, and p using the previous solution 4: Update auxiliary variables a using ( 46)-( 49) 5: Solve convex optimization problem (50) 6: End repeated until convergence, i.e., until the sum-logarithmic rate increase between iterations falls below a certain threshold ω. The CRM updates the weights β…”

Section: Centralized Resource Management (Crm) Protocolmentioning

confidence: 99%

“…Algorithm 2 Distributed Resource Management 1: Initialize q and p to feasible values Repeat: until convergence 2: Update weights β using ( 24), (35) 3: Update q, r, z, and p using the previous solution 4: Update auxiliary variables a using ( 46)-( 49 variables at each EC e. The DRM's upper-bound computational complexities for the CC and for each EC e become, therefore, O(Ω max (η c ) 3.5 ) and O(Ω max (η e ) 3.5 ), respectively. For completeness, the complexities of CRM and DRM are compared in Table II.…”

Section: E Distributed Resource Management (Drm) Protocolmentioning

confidence: 99%

“…While references [30]- [32] emphasize the Co-NOMA communication performance improvement, the analysis of the computation and delay aspects therein remains an open problem. More recently, in a single-EC setup, some works consider Co-NOMA-enabled MEC for minimizing energy consumption [33], for maximizing computation efficiency [34], and in a cognitive radio setup [35]. An end-to-end delay analysis of a relaying network is considered in [36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks

Reifert

Dahrouj

Shihada

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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Facing a vast amount of connections, huge performance demands, and the need for reliable connectivity, the sixth generation of communication networks (6G) is envisioned to implement disruptive technologies that jointly spur connectivity, performance, and reliability. In this context, this paper proposes, and evaluates the benefit of, a hybrid central cloud (CC) computing and mobile edge computing (MEC) platform, especially introduced to balance the network resources required for joint computation and communication. Consider a hybrid cloud and MEC system, where several power-hungry multiantenna unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden. While the multi-antenna base stations are connected to the cloud via capacity-limited fronthaul links, the UAVs serve the cell-edge users with limited power and computational capabilities. The paper then considers the problem of maximizing the weighted network sum-rate subject to peruser delay, computational capacity, and power constraints, so as to determine the beamforming vectors and computation allocations. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on 0-norm relaxation, successive convex approximation, and fractional programming, and has the compelling ability to be implemented in a distributed fashion across the multiple UAVs and the CC. The paper results illustrate the numerical prospects of the proposed algorithm for enabling joint communication and computation, and highlight the appreciable improvements of data processing delays and throughputs as compared to conventional system strategies.

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Section: Centralized Resource Management (Crm) Protocolmentioning

confidence: 99%

Section: E Distributed Resource Management (Drm) Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks

Reifert

Dahrouj

Shihada

et al. 2022

2022 IEEE Globecom Workshops (GC Wkshps)

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“…Recently, Yuo et al [11] studied the EE and SE tradeoff in RIS-aided MIMO network, where an iterative mean-square error minimization approach was developed to optimize the phase shifter. On the other hand, in [12], Wang et al studied the cooperative hybrid NOMA-based mobile-edge computing networks. In [13], Wang et al studied the delay-sensitive secure NOMA transmission for Internet-of-Things (IoT) networks.…”

Section: Introductionmentioning

confidence: 99%

Secrecy Energy Efficiency Optimization for Reconfigurable Intelligent Surface-Aided Multiuser MISO Systems

Bian

Wang

Zhou

2022

Wireless Communications and Mobile Computing

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Currently, the reconfigurable intelligent surface (RIS) has been applied to improve the physical layer security in wireless networks. In this paper, we focus on the secure transmission in RIS-aided multiple-input single-output (MISO) systems. Specifically, by assuming that only imperfect channel state information (CSI) of the eavesdropper can be obtained, we investigated the robust secrecy energy efficiency (SEE) optimization via jointly designing the active beamforming (BF), artificial noise (AN) at Alice, and the passive phase shifter at the RIS. The formulated problem is hard to handle due to the complicated secrecy rate expression as well as the infinite constraints introduced by the CSI uncertainties. By utilizing the Taylor expansion, we transformed the fractional programming into a convex problem, while all the constraints are approximated via the successive convex approximation and constrained concave-convex procedure. Then, by using the extended S-Lemma, we transform the infinite constraints into linear matrix inequality, which is convex. Finally, an alternate optimization (AO) algorithm was proposed to solve the reformulated problem. Simulation results demonstrated the performance of the proposed design.

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“…As a powerful supplement to the upper layer encryption techniques, the physical layer security (PLS) technology uses the physical layer characteristics of the channel itself to improve the secrecy performance. While ensuring the secure and reliable communication of the legitimate receiver, it tries to avoid the effective eavesdropping of eavesdroppers [ 1 , 2 , 3 , 4 ]. PLS technology in UAV communication networks can achieve secure transmission with low energy consumption through the differentiated design of wireless channels.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems

Cheng

Wang

et al. 2022

Entropy

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This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.

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Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks

Cited by 41 publications

References 49 publications

Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks

Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks

Secrecy Energy Efficiency Optimization for Reconfigurable Intelligent Surface-Aided Multiuser MISO Systems

Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems

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