چکیده مقاله
In this study, we introduce a new method for channel estimation in MIMO OFDM systems by combining Convolutional Neural Networks CNNs with Long Short Term Memory LSTM networks We start by preprocessing pilot signals and transforming them into spatialfrequency and angle delay domains Our dual path CNN processes these transformed signals to extract key features and improve accuracy To handle varying conditions over time, we use LSTM layers to capture dependencies between consecutive OFDM blocks We also include a residual connection to preserve crucial information from initial estimates Our experiments show that this hybrid CNN LSTM approach performs better than traditional methods, especially in noisy environments and with pilot contamination
کلیدواژهها
نویسندگان
Nima Rafeimehr
Department of Electrical engineering, Iran University of Science and Technology
شیوه ارجاع
Rafeimehr, Nima,1403,Optimized MIMO-OFDM Channel Estimation: Hybrid CNN-LSTM Approach,The 3th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences.,Mashhad
ارائهشده در
مجموعه مقالات سومین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات29 شهریور 1403 · مشهد