Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT

Hu, Zhenzhen; Wei, Ning; Zhang, Zhongpei

doi:10.1109/access.2017.2737034

Cited by 37 publications

(31 citation statements)

References 31 publications

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“…Thus, in order to obtain the closed-form solutions, the Lagrangian dual decomposition method can be used to solve this problem. The Lagrangian function of the problem (16) can be rewritten by…”

Section: B Feasible Region Analysismentioning

confidence: 99%

“…The authors in [15] investigated the joint power control and time allocation problem to achieve the sum-rate maximization of SUs for wireless powered CRNs, where SUs first harvest energy in the downlink wireless power transfer phase and then use the energy for data transmission in the uplink wireless information transmission phase in a timedivision way. In [16], the optimal RA for the harvested energy Although in the above-mentioned literature, CSI is generally assumed to be imperfect, the PS design is rarely involved. The PS factor is the key metric for balancing the optimality of information transmission and the harvested energy, which needs to be designed carefully.…”

Section: Introductionmentioning

confidence: 99%

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Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

Xu¹,

Sun²,

Yang³

et al. 2020

Preprint

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Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of the most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

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Section: B Feasible Region Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

Xu¹,

Sun²,

Yang³

et al. 2020

Preprint

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“…Hu et. al [22], on the other hand, investigated the objective of EH energy maximization, and the resource allocation problem is formulated to address that goal.…”

Section: Index Termsmentioning

confidence: 99%

“…Hu et. al [22], on the other hand, investigated the objective of EH energy maximization, and the resource allocation problem is formulated to address that goal.Additionally, [23] considered the underlay scheme in CR network and proposed the optimal beamforming design. To address both the SE and EE, a multiple-input single-output (MISO) NOMA CR using SWIPT is considered based on a practical non-linear EH model.…”

mentioning

confidence: 99%

Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT

Sun

Zhou

Zhang

2018

2018 IEEE International Conference on Communications (ICC)

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This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU).Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely minimizing the transmission power of the cognitive base station and maximizing the total harvested energy of the SUs. The optimization problems are difficult to solve mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a one-dimensional search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the gaussian CSI error model is generally better than that using the bounded CSI error model. Index TermsRobust beamforming, non-orthogonal multiple access, non-linear energy harvesting model, cognitive radio, imperfect channel state information.radio signals for replenishing the batteries [15]. There have been two research thrusts on EH using RF technology. One focuses on wirelessly powered networks, where a so-called harvestthen-transmit protocol is applied [16]. The other one uses simultaneous wireless information and power transfer (SWIPT) [18]-[20], which is the focus of this paper. In literature, SWIPT in CR has been extensively studied. Specifically, the work in [21] considered the optimal beamforming design in a MISO CR downlink network where a similar power splitting structure is applied in the user receiver. Hu et. al [22], on the other hand, investigated the objective of EH energy maximization, and the resource allocation problem is formulated to address that goal.Additionally, [23] considered the underlay scheme in CR network and proposed the optimal beamforming design. To address both the SE and EE, a multiple-input single-output (MISO) NOMA CR using SWIPT is considered based on a practical non-linear EH model. Robust beamforming design problems are studied under a pair of channel state information (CSI) error models. The related contributions and the motivation of our work are summarized as follows. A. Related Work and MotivationThe prior contributions related to this paper can be divided into two categories based on the EH model adopted, i.e. the linear [20]-[36] and the non-linear EH model [16], [37]-[41]. In the linear EH model, the power harvested increases linearly with the input power, while the EH under the non-linear m...

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“…It is observed that there is always a trade-off between harvested energy and throughput. A wideband cognitive radio network with simultaneous wireless information and power transfer (SWIPT) is designed for achieving both energy and spectrum efficiency [15]. By considering several constraints on rate, transmit power, interference and sub-channel allocation, the harvested energy was tried to maximize by adopting Lagrangian and Subgradient methods.…”

Section: Recent Energy Harvesting Strategies In Cognitive Radiomentioning

confidence: 99%

RF Energy Harvesting in Cognitive Radio: Towards Green Communication

Sarma

Mastorakis³

2018

MATEC Web Conf.

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There has been a continuous emphasis on an energy efficient communication system design. With the advent of 5G communication technologies, along with a faster and reliable data transfer mechanisms, energy management and conservation is gaining more attention and is becoming a major and indispensable part of communication research. This papers highlights the contemporary technological developments in the field of RF energy harvesting in a cognitive and high data rate network. It has been observed that an efficient RF energy harvesting technology in a cognitive platform definitely leads towards a greener communication paradigm.

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Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT

Cited by 37 publications

References 31 publications

Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

Robust Beamforming Design in a NOMA Cognitive Radio Network Relying on SWIPT

RF Energy Harvesting in Cognitive Radio: Towards Green Communication

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