Larry-LiuTengyu's repositories
MultiStage-Grassmannian-DNN
Code of "Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification", IEEE SPL 2020
LSTMChannelStateEstimation
Using LSTM Model(s) to estimate the state of a deep fading channel
Deep-Learning-Power-Allocation-in-Massive-MIMO
This is the code package related to the follow scientific article: Luca Sanguinetti, Alessio Zappone, Merouane Debbah 'Deep-Learning-Power-Allocation-in-Massive-MIMO' presented at the Asilomar Conference on Signals, Systems, and Computers, 2018. http://www.asilomarsscconf.org
Paper-with-Code-of-Wireless-communication-Based-on-DL
无线与深度学习结合的论文代码整理/Wireless based on deep learning papers' code
ResourceAllocationDelayedCSI
Spectrum and power allocation for vehicular communications with delayed CSI feedback, IEEE Wireless Communications Letters
Conv_LSTM_TeleCommunications
Statistical Analysis and predicting future TeleCommunication Data by SVR, ARIMA, LSTM, Conv_LSTM
Limited-Feedback-Channel-Estimation-in-Massive-MIMO-Systems
Today, plenty of cellular systems utilize frequency-division duplexing (FDD). Downlink training for channel state information in FDD is difficult since training and feedback overhead is proportional to the number of antennas at the base station, which is large in a Massive MIMO systems. To deal with the limited feedback mechanism of downlink channel in FDD Massive MIMO system, we can adopt the double directional model. This is applicable for the 5G systems to get high capacity and data rate. We analyse and test the performance of the Limited feedback channel with DD model via the MATLAB and we had the better performance rather than other models.
wireless-communication
Recognizing the feature of the wireless channel fingerprint based on two algorithms（an application of pattern recognition）.针对无线信道“指纹”特征建模，包括“指纹”特征参数的建立、匹配识别、连续特征参数的“区域划分”等问题，用无线信道参数的提取算法、BP神经元网络算法和我们建立的微元试探法对模型进行分析求解
Channel-Estimation-OFDM-
-Investigated the efficiency of different estimators to estimate and track channel parameters based on the Mean Squared Error (MSE) performance. The estimators employed in the simulation are LS and MMSE estimators and their performance in the transfer domain was evaluated. MATLAB was used for the simulation of the communication link and analyzing the error between the estimated channel parameters and actual modeled channel parameters.