Search Results for author:
Found 9 papers, 2 papers with code
Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism
The example results show that considering the carbon trading cost and demand response under the tiered carbon trading mechanism, the total operating cost of IES is reduced by 5. 69% and the carbon emission is reduced by 17. 06%, which significantly improves the reliability, economy and low carbon performance of IES.
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC).
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements.
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i. e., predicting the angular parameters of users.
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance.
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems
Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems.
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI).
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneuverability of UAVs which can adapt to heterogeneous requirements of different applications.
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications
To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy.
