Energy Utilization Efficient Frame Structure for Energy Harvesting Cognitive Radio Networks

Wu, Chengyu; Shi, Qingjiang; He, Chen; Chen, Yunfei

doi:10.1109/lwc.2016.2591943

Cited by 14 publications

(9 citation statements)

References 9 publications

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“…However, bisection searching method, while being simple in the context, is quite complex the implementation, since there is no exact method of finding the initial upper and lower bounds. In addition, existing studies in literature [30], [31] have demonstrated that updating η (m) as in Step 4 based on Dinkelbach method can converge faster than bisection approaches in many circumstances.…”

Section: B Overview Of the Solution Approachmentioning

confidence: 99%

Energy-Efficient Hybrid Precoding for mmWave Multi-User Systems

Nguyen

Frigon

2018

2018 IEEE International Conference on Communications (ICC)

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This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to transmit antenna connections can be switched off for energy saving. By explicitly considering the effect of each connection on the required power for baseband and RF signal processing, we describe the total power consumption in a sparsity form of the analog precoding matrix. However, these sparsity terms and sparsity-modulus constraints of the analog precoding make the system energy-efficiency maximization problem non-convex and challenging to solve. To tackle this problem, we first transform it into a subtractive-form weighted sum rate and power problem. A compressed sensing-based re-weighted quadratic-form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. We then exploit alternating minimization of the mean-squared error to solve the equivalent problem where the digital precoding vectors and the analog precoding matrix are updated sequentially.The energy efficiency upper bound and a heuristic algorithm are also examined for comparison purposes.Numerical results confirm the superior performances of the proposed algorithm over benchmark energyefficiency hybrid precoding algorithms and heuristic one.

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Section: B Overview Of the Solution Approachmentioning

confidence: 99%

Energy-Efficient Hybrid Precoding for mmWave Multi-User Systems

Nguyen

Frigon

2018

2018 IEEE International Conference on Communications (ICC)

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“…Some of the demerits include lack of standards, lack of interoperability, the overhead of mapping between local and physical locations, etc. [17,18]. Storage Virtualization can be done at the block level and file level.…”

Section: Figure 4 Types Of Infrastructure Virtualizationmentioning

confidence: 99%

Resource Consideration in Internet of Things: A Perspective View

Sahani¹,

Sharma²

2019

IJTSRD

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The ubiquitous computing and its applications at different levels of abstraction are possible mainly by virtualization. Most of its applications are becoming pervasive with each passing day and with the growing trend of embedding computational and networking capabilities in everyday objects of use by a common man. Virtualization provides many opportunities for research in IoT since most of the IoT applications are resource constrained. Therefore, there is a need for an approach that shall manage the resources of the IoT ecosystem. Virtualization is one such approach that can play an important role in maximizing resource utilization and managing the resources of IoT applications. This paper presents a survey of Virtualization and the Internet of Things. The paper also discusses the role of virtualization in IoT resource management.

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“…However, the harvested energy supplies is somewhat dynamic or unstable due to the transmitting power fluctuation of ambient RF sources or unpredictable channel fading. Therefore, it is necessary to maximize the energy efficiency (EE) in RF-CRN, which is defined as the ratio of the amount of average transmitted data bits to the amount of consumed energy [21]. As an important indicator in energyefficient for RF-CRN communications, tremendous works have been done in explicitly solving the EE maximization problem, which is a nonlinear fractional optimization problem [22] under the energy and interference constraints.…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency optimization-based resource allocation for underlay RF-CRN with residual energy and QoS guarantee

Tian

Sun

et al. 2020

J Wireless Com Network

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How to achieve energy-efficient transmission in radio frequency energy harvesting cognitive radio network (RF-CRN) is of great importance when nodes in CRN are self-maintained. This paper presents a radio frequency (RF) energy harvesting hardware-based underlay cognitive radio network (RH-CRN) structure, where a secondary transmitter (ST) first harvests energy from RF signals source originating from the primary network, and then communicates with a secondary receiver (SR) in underlay mode by using the harvested energy. The total consumed energy by the secondary user (SU) must be equal to or less than the total harvested energy referred to as energy causality constraint, In addition, the ST possesses some initial energy which may be the residual energy from the former transmission blocks, and we consider the energy loss of energy harvesting circuit as a systematic factor as well. Our goal is to achieve the maximum energy efficiency (EE) of the secondary network by jointly optimizing transmitting time and power. To guarantee the quality of service (QoS) of secondary transceiver, a minimum requirement of throughput constraint is imposed on the ST in the process of EE maximization. As the EE maximization is a nonlinear fractional programming problem, a quick iterative algorithm based on Dinkelbach’s method is proposed to achieve the optimal resource allocation. Simulation results show that the proposed strategy has fast convergence and can improve the system EE greatly while ensuring the QoS.

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Energy Utilization Efficient Frame Structure for Energy Harvesting Cognitive Radio Networks

Cited by 14 publications

References 9 publications

Energy-Efficient Hybrid Precoding for mmWave Multi-User Systems

Energy-Efficient Hybrid Precoding for mmWave Multi-User Systems

Resource Consideration in Internet of Things: A Perspective View

Energy efficiency optimization-based resource allocation for underlay RF-CRN with residual energy and QoS guarantee

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