Joint Source-Relay Optimization for MIMO Full-Duplex Bidirectional Wireless Sensor Networks with SWIPT

Liú, Dan; Wen, Zhigang; Liu, Xiaoqing; Shan, Li; Zou, Junwei

doi:10.3390/s19081827

Cited by 5 publications

(8 citation statements)

References 44 publications

(68 reference statements)

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“…Moreover, it failed to support the Bayesian network. Liu et al 35 introduced a joint source relay optimization model to solve the energy harvesting, and the transmitting power issues. It attained lower bit error rate, and reduced the complexity constraints.…”

Section: Motivationmentioning

confidence: 99%

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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Multi‐objective optimization is used for optimizing a number of objectives simultaneously. Mostly, the optimization algorithms considered the previous iterative position to find the next position updates. The main intention of this research is to design and develop a new model to solve the computational complexity, and the resource allocation problem. Based on this perspective, the Taylor series model and its predictive theory are applied to Spider Monkey Optimization (SMO), and a new optimization, named Taylor‐Spider Monkey Optimization (TaySMO) is developed. The proposed TaySMO computes the updated position of the swarm using the local leader phase and the global leader phase. However, a new position update equation is derived to enhance the searching process of the SMO. Here, multiple objectives such as, throughput, power, and fairness index are considered to solve the resource allocation problem. However, the performance of the proposed algorithm is evaluated using the conventional optimization function in terms of fitness function and convergence criteria as the mean square error (MSE) with the neural network learning is 0.3747, congestion rate of the resource allocation problem is 8.736E‐23, and MSE of the spectrum sensing is 8.74E‐23, respectively.

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Section: Motivationmentioning

confidence: 99%

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Menon

Kulkarni

Singh

et al. 2021

Numerical Meth Engineering

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“…Meanwhile, in the iter-th iteration of the algorithm, the optimal solution (a (iter ) s,n ) 2 and (a (iter ) r,n ) 2 can be achievable with ggplab toolbox. Then, by denoting the objective function of (40)…”

Section: Property 1 the Proposed Algorithm 1 Is Convergentmentioning

confidence: 99%

Beamforming design for MIMO full‐duplex SWIPT IoT system under imperfect CSI

Liú

Ling

et al. 2021

IET Communications

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This paper proposes a multiple-input multiple-output full-duplex relay Internet of Things system under imperfect channel state information, where the relay uses time switching relaying protocol to harvest energy. Particularly, due to the uncertainty channel circumstance, the self-interference in full-duplex relay cannot be eliminated completely. For such a system, a joint source-relay beamforming optimization problem which maximizes the system achievable rate subject to the transmit power and harvested energy constraints is studied. In light of the intractability of the problem, a singular value decomposition based geometric programming algorithm is investigated. The considered problem is reformulated to a diagonalized form by using singular value decomposition method. Then, the geometric programming algorithm is adopted to obtain the optimal solution for the reformulated problem. Numerical results validate the effectiveness and superiority of the proposed optimization scheme.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Furthermore, an EH relaying system was studied in [20] for the cases with/without the presence of co-channel interference, where the multiple antennas relay node was powered by the source signal and signals from other sources. Joint optimization of beamforming, transmission power, and harvested energy for the MIMO SWIPT system was investigated in [21]. The broadcasting and interference channel case in the MR SWIPT system with multi-users was analyzed in [22].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, both SWIPT and cooperative jamming technics were adopted in the multi-radio WMN to enhance its power consumption and physical layer security performance, respectively. While [20][21][22][23][24] only adopted the SWIPT, [25][26][27] only adopted the cooperative jamming. Although [28][29][30][31] proposed some beamforming method, considering both SWIPT and cooperative jamming, all of them were designed for the cognitive radio system.…”

mentioning

confidence: 99%

Robust Beamforming Design for SWIPT-Based Multi-Radio Wireless Mesh Network with Cooperative Jamming

Zhao

Geng

et al. 2020

Information

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Wireless mesh networks (WMNs) can provide flexible wireless connections in a smart city, internet of things (IoT), and device-to-device (D2D) communications. The performance of WMNs can be greatly enhanced by adopting a multi-radio technique, which enables a node to communicate with more nodes simultaneously. However, multi-radio WMNs face two main challenges, namely, energy consumption and physical layer secrecy. In this paper, both simultaneous wireless information and power transfer (SWIPT) and cooperative jamming technologies were adopted to overcome these two problems. We designed the SWIPT and cooperative jamming scheme, minimizing the total transmission power by properly selecting beamforming vectors of the WMN nodes and jammer to satisfy the individual signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constrains. Especially, we considered the channel estimate error caused by the imperfect channel state information. The SINR of eavesdropper (Eve) was suppressed to protect the secrecy of WMN nodes. Due to the fractional form, the problem was proved to be non-convex. We developed a tractable algorithm by transforming it into a convex one, utilizing semi-definite programming (SDP) relaxation and S-procedure methods. The simulation results validated the effectiveness of the proposed algorithm compared with the non-robust design.

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Joint Source-Relay Optimization for MIMO Full-Duplex Bidirectional Wireless Sensor Networks with SWIPT

Cited by 5 publications

References 44 publications

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Beamforming design for MIMO full‐duplex SWIPT IoT system under imperfect CSI

Robust Beamforming Design for SWIPT-Based Multi-Radio Wireless Mesh Network with Cooperative Jamming

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