scholarly journals Adaptive Power Allocation and Splitting with Imperfect Channel Estimation in Energy Harvesting Based Self-Organizing Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Kisong Lee ◽  
JeongGil Ko
Keyword(s):  
Energy Transfer ◽  
Energy Harvesting ◽  
Channel Estimation ◽  
Power Allocation ◽  
Wireless Channel ◽  
Network Systems ◽  
Adaptive Power ◽  
Continuous Power ◽  
Self Organizing Networks ◽  
Self Organizing

As miniature-sized embedded computing platforms are ubiquitously deployed to our everyday environments, the issue of managing their power usage becomes important, especially when they are used in energy harvesting based self-organizing networks. One way to provide these devices with continuous power is to utilize RF-based energy transfer. Previous research in RF-based information and energy transfer builds up on the assumption that perfect channel estimation is easily achievable. However, as our preliminary experiments and many previous literature in wireless network systems show, making perfect estimations of the wireless channel is extremely challenging due to their quality fluctuations. To better reflect reality, in this work, we introduce an adaptive power allocation and splitting (APAS) scheme which takes imperfect channel estimations into consideration. Our evaluation results show that the proposed APAS scheme achieves near-optimal performances for transferring energy and data over a single RF transmission.

scholarly journals Efficient Gradient Updating Strategies with Adaptive Power Allocation for Federated Learning over Wireless Backhaul

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6791
Author(s):  
Yunji Yang ◽  
Yonggi Hong ◽  
Jaehyun Park
Keyword(s):  
Power Allocation ◽  
Large Change ◽  
Wireless Channel ◽  
Channel Fading ◽  
Wireless Backhaul ◽  
Small Channel ◽  
Backhaul Link ◽  
Adaptive Power ◽  
Deep Learning Model ◽  
Multiple Clients

In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shared for all the distributed clients and it is trained through the federated learning over wireless backhaul connected to the main server. However, during the training phase, local gradients need to be transferred from multiple clients to the server over wireless backhaul link and can be distorted due to wireless channel fading. To overcome it, an efficient gradient updating method is proposed, in which the gradients are combined such that the effective SNR is maximized at the server. In addition, when the backhaul links for all clients have small channel gain simultaneously, the server may have severely distorted gradient vectors. Accordingly, we also propose a binary gradient updating strategy based on thresholding in which the round associated with all channels having small channel gains is excluded from federated learning. Because each client has limited transmission power, it is effective to allocate more power on the channel slots carrying specific important information, rather than allocating power equally to all channel resources (equivalently, slots). Accordingly, we also propose an adaptive power allocation method, in which each client allocates its transmit power proportionally to the magnitude of the gradient information. This is because, when training a deep learning model, the gradient elements with large values imply the large change of weight to decrease the loss function.

scholarly journals Target Localization and Power Allocation Using Wireless Energy Harvesting Sensors

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2592
Author(s):  
Song Chen ◽  
Dunge Liu ◽  
Yubin Zhao
Keyword(s):  
Energy Harvesting ◽  
Power Allocation ◽  
Wireless Sensors ◽  
Target Localization ◽  
Mixed Integer ◽  
Estimation Accuracy ◽  
Integer Programming Problem ◽  
Continuous Power ◽  
Wireless Energy Harvesting ◽  
Accuracy Problem

As radio-frequency (RF) based wireless energy harvesting technology can provide remote and continuous power to low-power devices, e.g., wireless sensors, it may be a substitute for batteries and extend the lifetime of the wireless sensor networks. In this paper, we propose a wireless energy harvesting localization system (WEHLoc), which contains batteryless wireless sensors as anchors and an energy access point (E-AP) to transfer power to the anchors. We consider a passive target localization scenario, in which the anchors monitor the target and send the sensed ranging data back to the E-AP. Additionally, we formulate the optimal estimation accuracy problem which is a 0–1 mixed-integer programming problem and relates to the energy beam, target transmitted power, and deployed anchor density. Then, we develop the power allocation scheme of the E-AP to solve the objective. In order to reduce the complexity, we propose a heuristic method that converts the maximum estimation accuracy problem into the energy efficiency problem and use linear programming to solve them. The simulations demonstrate that WEHLoc can be massively deployed in a wide area, and the estimation error and the power consumption are relatively low.

Application of Fractal Methods to Ensure the Cyber-Resilience of Self-Organizing Networks

2019 ◽  
Vol 22 (4) ◽  
pp. 336-341
Author(s):  
D. V. Ivanov ◽  
D. A. Moskvin
Keyword(s):  
Information Security ◽  
Network Structure ◽  
Self Regulation ◽  
Security Threats ◽  
Self Organizing Networks ◽  
Self Organizing ◽  
Fractal Methods

In the article the approach and methods of ensuring the security of VANET-networks based on automated counteraction to information security threats through self-regulation of the network structure using the theory of fractal graphs is provided.

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

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