In ultra-dense networks （UDN）, the local precoding scheme for time-division duplex coordinated multiple point transmission （TDD-CoMP） can have a good performance with no feedback by using reciprocity between uplin...In ultra-dense networks （UDN）, the local precoding scheme for time-division duplex coordinated multiple point transmission （TDD-CoMP） can have a good performance with no feedback by using reciprocity between uplink and dovallink. However, if channel is time-varying, the channel difference would cause codeword mismatch between transmitter and receiver, which leads to performance degradation. In this paper, a linear interpolation method is proposed for TDD-CoMP system to estimate the uplink channel at the receiver, which would reduce the channel difference caused by time delay and decrease the probability of codeword mismatch between both sides. Moreover, to mitigate severe inter-cell interference and increase the coverage and throughput of celledge users in UDN, a two-codebook scheme is used to strengthen cooperation between base stations （BSs）, which can outperform the global precoding scheme with less overhead. Simulations show that the proposed scheme can significantly improve the link performance compared to the global precoding scheme.展开更多
Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and control...Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.展开更多
文摘In ultra-dense networks （UDN）, the local precoding scheme for time-division duplex coordinated multiple point transmission （TDD-CoMP） can have a good performance with no feedback by using reciprocity between uplink and dovallink. However, if channel is time-varying, the channel difference would cause codeword mismatch between transmitter and receiver, which leads to performance degradation. In this paper, a linear interpolation method is proposed for TDD-CoMP system to estimate the uplink channel at the receiver, which would reduce the channel difference caused by time delay and decrease the probability of codeword mismatch between both sides. Moreover, to mitigate severe inter-cell interference and increase the coverage and throughput of celledge users in UDN, a two-codebook scheme is used to strengthen cooperation between base stations （BSs）, which can outperform the global precoding scheme with less overhead. Simulations show that the proposed scheme can significantly improve the link performance compared to the global precoding scheme.
文摘Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.
