scholarly journals Caching deployment based on energy efficiency in device-to-device cooperative networks

2020 ◽  
Vol 16 (12) ◽  
pp. 155014772098465
Author(s):  
Weiguang Wang ◽  
Hui Li ◽  
Yang Liu ◽  
Wei Cheng ◽  
Haoyang Qin
Keyword(s):  
Energy Efficiency ◽  
Communication Networks ◽  
Optimization Problem ◽  
Transformation Method ◽  
Cooperative Networks ◽  
Closed Form Expression ◽  
Maximization Problem ◽  
Quadratic Transformation ◽  
Traffic Demand ◽  
Device To Device

The rapid growth of mobile data traffic demand will cause congestion to the future communication network. The cache-enabled device-to-device communication has been proven to effectively enhance the performance of wireless communication networks. This article investigates the caching deployment problem from the energy efficiency in the cache-enabled device-to-device networks. According to the random geometry theory modeling, the closed form expression of energy efficiency is derived, which measures the average number of successful transmitted file bits per unit time and per unit power consumption. And then we establish an optimization problem to maximize energy efficiency. As the formulated optimization problem is a multiple-ratio fractional programming problem that cannot be solved conveniently, we propose a quadratic transformation method to nest in the energy efficiency maximization problem. To tackle this problem, an iterative optimization algorithm is proposed to optimize the caching policy and network energy efficiency. The simulation results demonstrate that the proposed policy can achieve higher energy efficiency and hit probability in the cache-enabled device-to-device network.

scholarly journals Energy Efficiency Design for Secure MISO Cognitive Radio Network Based on a Nonlinear EH Model

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ni ◽  
Xinyu Da ◽  
Hang Hu ◽  
Miao Zhang
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Primary User ◽  
Open Architecture ◽  
Maximization Problem ◽  
Transmit Beamforming ◽  
Secrecy Rate ◽  
Single Output

In this work, we investigate the secrecy energy efficiency (SEE) optimization problem for a multiple-input single-output (MISO) cognitive radio (CR) network based on a practical nonlinear energy-harvesting (EH) model. In particular, the energy receiver (ER) is assumed to be a potential eavesdropper due to the open architecture of a CR network with simultaneous wireless information and power transfer (SWIPT), such that the confidential message is prone to be intercepted in wireless communications. The aim of this work is to provide a secure transmit beamforming design while satisfying the minimum secrecy rate target, the minimum EH requirement, and the maximum interference leakage power to primary user (PU). In addition, we consider that all the channel state information (CSI) is perfectly known at the secondary transmitter (ST). We formulate this beamforming design as a SEE maximization problem; however, the original optimization problem is not convex due to the nonlinear fractional objective function. To solve it, a novel iterative algorithm is proposed to obtain the globally optimal solution of the primal problem by using the nonlinear fractional programming and sequential programming. Finally, numerical simulation results are presented to validate the performance of the proposed scheme.

scholarly journals Precoding Design for Energy Efficiency Maximization in MIMO Half-Duplex Wireless Sensor Networks with SWIPT

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4923 ◽  
Author(s):  
Liang Xue ◽  
Jin-Long Wang ◽  
Jie Li ◽  
Yan-Long Wang ◽  
Xin-Ping Guan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Harvesting ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Half Duplex ◽  
Energy Constrained

This paper explores the energy efficiency (EE) maximization problem in single-hop multiple-input multiple-output (MIMO) half-duplex wireless sensor networks (WSNs) with simultaneous wireless information and power transfer (SWIPT). Such an energy efficiency maximization problem is considered in two different scenarios, in which the number of energy-harvesting (EH) sensor nodes are different. In the scenario where the single energy-harvesting sensor node is applied, the modeled network consists of two multiple-antenna transceivers, of which the energy-constrained energy-harvesting sensor node harvests energy from the signals transmitted from the source by a power splitting (PS) scheme. In the scenario of multiple EH sensor nodes, K energy-constrained sensor nodes are applied and the same quantity of antennas are equiped on each of them. The optimization problem is formulated to maximize the energy efficiency by jointly designing the transceivers’ precoding matrices and the PS factor of the energy-harvesting sensor node. The considered constraints are the required harvested energy, the transmission power limit and the requirement on the data rate. The joint design of the precoding matrices and the PS factor can be formulated as an optimization problem, which can be transformed into two sub-problems. An alternating algorithm based on Dinkelbach is proposed to solve the two sub-problems. The convergence of the proposed alternating algorithm, the solution optimality and the computational complexity are analyzed in the paper. Simulation results demonstrate the convergence and effectiveness of our proposed algorithm for realizing the maximum energy efficiency.

scholarly journals Analisis Kinerja Jaringan Hybrid Kooperatif Device-to-Device 5G menggunakan Teknik Pemilihan Relay Reaktif

2020 ◽  
Vol 8 (1) ◽  
pp. 178
Author(s):  
MUHAMMAD RAUDHI AZMI ◽  
MELINDA MELINDA ◽  
NASARUDDIN NASARUDDIN
Keyword(s):  
Energy Efficiency ◽  
Cooperative Communication ◽  
Communication System ◽  
Relay Selection ◽  
Network Models ◽  
Cooperative Networks ◽  
Hybrid Networks ◽  
Reactive Relay Selection ◽  
Device To Device ◽  
Speed Performance

ABSTRAK Sistem komunikasi kooperatif device-to-device (D2D) merupakan salah satu strategi peningkatan kecepatan data dan efisiensi energi. Pada sistem kooperatif, mekanisme relay merupakan faktor penting dalam meneruskan informasi. Model jaringan merupakan isu penting untuk meningkatkan kecepatan, kinerja dan efisiensi energi pada sistem. Oleh sebab itu, makalah ini menganalisis kinerja jaringan hybrid kooperatif D2D pada teknologi 5G menggunakan teknik pemilihan relay reaktif atau reactive relay selection (RRS). Beberapa parameter kinerja penting telah simulasikan, seperti model jaringan, SNR, BER dan throughput. Hasil simulasi menunjukkan bahwa jaringan kooperatif hybrid-relay dengan RRS menghasilkan nilai BER yang lebih kecil dan throughput yang lebih tinggi dibandingkan model multi-relay dan multi-hop relay. Dengan demikian, jaringan hybrid-relay dengan pemilihan relay lebih efisien dibandingkan jaringan lainnya. Kata kunci: sistem komunikasi kooperatif, pemilihan relay reaktif, multi-hop, multi-relay, hybrid-relay. ABSTRACT A device-to-device (D2D) cooperative communication system is one of the strategies to increase data speed and energy efficiency. In a cooperative system, the relay mechanism is an important factor in forwarding information. A network model is an important issue to increase the speed, performance and energy efficiency of the system. Therefore, this paper analyzes the performance of D2D cooperative hybrid networks on 5G technology using reactive relay selection (RRS) techniques. Some important performance parameters have been simulated, such as network models, SNR, BER and throughput. Simulation results show that hybridrelay cooperative networks with RRS produce smaller BER values and higher throughput compared to multi-relay and multi-hop relay models. Thus, hybridrelay networks with relay selection are more efficient than other networks. Keywords: cooperative communication system, reactive relay selection, multihop, multi-relay, hybrid-relay.

scholarly journals Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Wenbin Gao
Keyword(s):  
Energy Efficiency ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Spectrum Sensing ◽  
Closed Form Expression ◽  
Initial Time ◽  
Maximization Problem ◽  
Power Splitting ◽  
Transmission Power ◽  
Af Relay

In this paper, we propose a joint sensing duration and transmission power allocation scheme to maximize the energy efficiency (EE) of the secondary user (SU) in a cooperative cognitive sensor network (CSN). At the initial time slot of the frame, the secondary transmitter (ST) performs energy harvesting (EH) and spectrum sensing simultaneously using power splitting (PS) protocol. The modified goodness of fit (GoF) spectrum sensing algorithm is employed to detect the licensed spectrum, which is not sensitive to an inaccurate noise power estimate. Based on the imperfect sensing results, the ST will act as an amplify-and-forward (AF) relay and assist in transmission of the primary user (PU) or transmit its own data. The SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement. Since the SU’s EE function is a nonconvex problem and difficult to solve, we transform the original problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique by applying a nonlinear fractional programming. The closed-form expression of the ST’s transmission power is also derived through Karush-Kuhn-Tucker (KKT) and gradient method. Simulation results show that our scheme is superior to the existing schemes.

scholarly journals Energy Efficiency Maximization for UAV-Assisted Emergency Communication Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Haibin Niu ◽  
Xinyu Zhao ◽  
Liming Hou ◽  
Dongjun Ma
Keyword(s):  
Energy Efficiency ◽  
Communication Networks ◽  
Nonconvex Optimization ◽  
Bandwidth Allocation ◽  
Optimization Problem ◽  
Energy Savings ◽  
Low Cost ◽  
Optimization Technique ◽  
Base Station ◽  
Effective Capacity

Using unmanned aerial vehicles (UAVs) in emergency communications is a promising technology because of their flexible deployment, low cost, and high mobility. However, due to the limited energy of the onboard battery, the service duration of the UAV is greatly limited. In this paper, we study an emerging energy-efficient UAV emergency network, where a UAV works as an aerial base station to serve a group of users with different statistical quality-of-service (QoS) constraints in the downlink. In particular, the energy efficiency of the UAV is defined as the sum effective capacity of the downlink users divided by the energy consumption of the UAV, which includes the energy consumed by communication and the energy consumed by hovering. Then, we formulate an optimization problem to maximize the energy efficiency of the UAV by jointly optimizing the UAV’s altitude, downlink transmit power, and bandwidth allocation while meeting a statistical delay QoS requirement for each user. The formulated optimization problem is a nonlinear nonconvex optimization problem of fractional programming, which is difficult to solve. In order to deal with the nonconvex optimization problem, the following two steps are used. First, we transform the fractional objective function into a tractable subtractive function. Second, we decompose the original optimization problem into three subproblems, and then, we propose an efficient iterative algorithm to obtain the energy efficiency maximization value by using the Dinkelbach method, the block coordinate descent, and the successive convex optimization technique. Extensive simulation results show that our proposed algorithm has significant energy savings compared with a benchmark scheme.

scholarly journals Energy Efficiency Maximization with Optimal Beamforming in Secure MISO CRNs with SWIPT

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liang Xue ◽  
Yao Ma ◽  
Miao Zhang ◽  
Wanqiang Qin ◽  
Jin-Long Wang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Optimization Problem ◽  
Type Inequality ◽  
Optimal Solution ◽  
Maximization Problem ◽  
Subtraction Problem ◽  
Channel State ◽  
Convex Optimization Problem ◽  
Single Output ◽  
Optimal Beamforming

In this paper, the optimal beamforming problem of multi-input single-output (MISO) cognitive radio (CR) downlink networks with simultaneous wireless information and power transfer is studied. Due to the nonconvexity of the objective function, the considered nonconvex optimization problem is firstly transformed to an equivalent subtraction problem and then an approximated convex optimization problem is obtained by using the successive convex approximation (SCA). When the instantaneous channel state information (CSI) of the eavesdropping link is unknown to the legitimate transmitter, another interruption-constrained energy efficiency optimization problem is proposed and the Bernstein-type inequality (BTI) is used to conservatively approximate the probability constraint. The paper proposes a two-level iterative algorithm based on Dinkelbach to find the optimal solution of the EE maximization problem. Numerical results validate the effectiveness and convergence of the proposed algorithm.

scholarly journals Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2550
Author(s):  
Saidiwaerdi Maimaiti ◽  
Gang Chuai ◽  
Weidong Gao ◽  
Jinxi Zhang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Optimization Problem ◽  
Power Optimization ◽  
Mimo System ◽  
Optimization Technique ◽  
Mixed Integer ◽  
Optimization Scheme ◽  
Quadratic Transformation ◽  
Convex Problem

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

scholarly journals Machine Learning for Communications

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Video Streaming ◽  
Communication Networks ◽  
Data Analytics ◽  
Data Traffic ◽  
Traffic Demand ◽  
Performance Requirements

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

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