Downlink Beamforming with Imperfect Channel Information for a TDD-Based OFDMA System

2009 ◽  
Vol E92-B (9) ◽  
pp. 2967-2971
Author(s):  
Eunchul YOON ◽  
Joontae KIM
Keyword(s):  
Ofdma System ◽  
Downlink Beamforming ◽  
Channel Information

scholarly journals Optimizing a Secure Two-Way Network with Non-Linear SWIPT, Channel Uncertainty, and a Hidden Eavesdropper

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1222
Author(s):  
Pham Viet Tuan ◽  
Pham Ngoc Son ◽  
Tran Trung Duy ◽  
Sang Quang Nguyen ◽  
Van Quang Binh Ngo ◽  
...  
Keyword(s):  
Base Stations ◽  
Artificial Noise ◽  
Power Splitting ◽  
Transmission Power ◽  
Channel State ◽  
Downlink Beamforming ◽  
Non Linear ◽  
Channel Information ◽  
Downlink Channels

In this paper, the optimization of downlink beamforming vectors, uplink transmission power, and power-splitting factors is investigated for a secure two-way SWIPT network in the presence of a hidden eavesdropper and non-linear energy harvesting circuits over both perfect and imperfect channels. The eavesdropper is inactive, so its channel information is not available at the base stations (BSs). The purpose of artificial noise is to create downlink interference with the hidden eavesdropper as much as possible, while satisfying the quality of service for two-way communications. For perfect downlink channels, the semidefinite relaxation (SDR) technique is exploited, and the optimal matrices are proven to satisfy rank-1 conditions, thus providing the optimal beamforming vectors. For imperfect downlink channel state information, we propose an iterative algorithm with a penalty function to obtain the approximate rank-1 matrices. On uplink, we attain the optimal transmission power for users receiving maximum ratio transmission beamforming at the BSs. Eventually, the numerical experiments show the superiority of the proposed scheme, compared to a conventional scheme, in terms of signal-to-interference-plus-noise ratio at the eavesdropper.

Interference Suppression with Downlink Beamforming for a TDD-Based OFDMA System

2009 ◽  
Vol E92-B (2) ◽  
pp. 666-670 ◽  
Author(s):  
Eunchul YOON ◽  
Sun-Yong KIM ◽  
Suhan CHOI ◽  
Hichan MOON
Keyword(s):  
Interference Suppression ◽  
Ofdma System ◽  
Downlink Beamforming

scholarly journals Model-driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

2021 ◽  
pp. 1-1
Author(s):  
Juping Zhang ◽  
Minglei You ◽  
Gan Zheng ◽  
Ioannis Krikidis ◽  
Liqiang Zhao
Keyword(s):  
Model Driven ◽  
Downlink Beamforming ◽  
Channel Information ◽  
Mimo Downlink

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Low-Complexity Resource Allocation Algorithm for Multicell OFDMA System

2013 ◽  
Vol E96.B (5) ◽  
pp. 1218-1221 ◽  
Author(s):  
Qingli ZHAO ◽  
Fangjiong CHEN ◽  
Sujuan XIONG ◽  
Gang WEI
Keyword(s):  
Resource Allocation ◽  
Low Complexity ◽  
Ofdma System ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

scholarly journals Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System

2021 ◽  
Vol 11 (5) ◽  
pp. 2198
Author(s):  
Junwoo Jung ◽  
Jaesung Lim ◽  
Sungyeol Park ◽  
Haengik Kang ◽  
Seungbok Kwon
Keyword(s):  
Detection Probability ◽  
Probability Of Detection ◽  
Lower Probability ◽  
Symbol Sequence ◽  
Ofdma System ◽  
Standard Map ◽  
Detection Probabilities ◽  
Low Probability ◽  
Transmitted Symbol ◽  
Basic Symbol

A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by the basic symbol-by-symbol (SBS)-based FH pattern, we proposed two FH patterns, namely chaotic standard map (CSM) and cat map for FH-OFDMA systems. In our previous work, through analysis of complexity to regenerate the transmitted symbol sequence, at the point of adversary detectors, we found that the CSM had a lower probability of intercept than the cat map and SBS. It is possible when a detector already knows symbol and frame structures, and the detector has been synchronized to the FH-OFDMA system. Unlike the previous work, here, we analyze whether the CSM provides greater LPD capability than the cat map and SBS by detection probability using spectrum sensing technique. We analyze the detection probability of the CSM and provide detection probabilities of the cat map and SBS compared to the CSM. Based on our analysis of the detection probability and numerical results, it is evident that the CSM provides greater LPD capability than both the cat map and SBS-based FH-OFDMA systems.

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

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