
{"id":26394,"date":"2024-02-25T14:24:33","date_gmt":"2024-02-25T15:24:33","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-opendpd-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%db%8c%d8%a7%d8%af%da%af%db%8c%d8%b1%db%8c-%d9%88-%d9%85%d8%ad%da%a9-%d8%b2%d8%af%d9%86-%d9%85%d9%86%d8%a8\/"},"modified":"2024-02-25T14:24:34","modified_gmt":"2024-02-25T15:24:34","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-opendpd-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%db%8c%d8%a7%d8%af%da%af%db%8c%d8%b1%db%8c-%d9%88-%d9%85%d8%ad%da%a9-%d8%b2%d8%af%d9%86-%d9%85%d9%86%d8%a8","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-opendpd-%db%8c%da%a9-%da%86%d8%a7%d8%b1%da%86%d9%88%d8%a8-%db%8c%d8%a7%d8%af%da%af%db%8c%d8%b1%db%8c-%d9%88-%d9%85%d8%ad%da%a9-%d8%b2%d8%af%d9%86-%d9%85%d9%86%d8%a8\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 OpenDPD: \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0645\u062d\u06a9 \u0632\u062f\u0646 \u0645\u0646\u0628\u0639 \u0628\u0627\u0632 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc \u062a\u0642\u0648\u06cc\u062a\u200c\u06a9\u0646\u0646\u062f\u0647 \u062a\u0648\u0627\u0646 \u0628\u0627\u0646\u062f \u067e\u0647\u0646 \u0648 \u067e\u06cc\u0634 \u0627\u0639\u0648\u062c\u0627\u062c \u062f\u06cc\u062c\u06cc\u062a\u0627\u0644"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>OpenDPD: An Open-Source End-to-End Learning &#038; Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 OPENDPD: \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648 \u0645\u0639\u06cc\u0627\u0631 \u067e\u0627\u06cc\u0627\u0646 \u0628\u0647 \u0645\u0646\u0628\u0639 \u0628\u0627\u0632 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0633\u0627\u0632\u06cc \u062a\u0642\u0648\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u0642\u062f\u0631\u062a \u067e\u0647\u0646\u0627\u06cc \u0628\u0627\u0646\u062f \u0648 \u067e\u06cc\u0634 \u0627\u0632 \u062a\u0642\u0637\u06cc\u0631 \u062f\u06cc\u062c\u06cc\u062a\u0627\u0644<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Yizhuo Wu, Gagan Deep Singh, Mohammadreza Beikmirza, Leo de Vreede, Morteza Alavi, Chang Gao<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>5<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Machine Learning,Signal Processing,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u067e\u0631\u062f\u0627\u0632\u0634 \u0633\u06cc\u06af\u0646\u0627\u0644 ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 16 January, 2024; originally announced January 2024. , Comments: To be published at the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>16 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0631\u0633\u0627\u0644 \u0634\u062f.\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u0634\u0627\u0631 \u062f\u0631 \u0633\u0645\u067e\u0648\u0632\u06cc\u0648\u0645 \u0628\u06cc\u0646 \u0627\u0644\u0645\u0644\u0644\u06cc IEEE \u062f\u0631 \u0645\u062f\u0627\u0631\u0647\u0627 \u0648 \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627 (ISCAS) \u060c \u0633\u0646\u06af\u0627\u067e\u0648\u0631 \u0645\u0646\u062a\u0634\u0631 \u0634\u062f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">With the rise in communication capacity, deep neural networks (DNN) for digital pre-distortion (DPD) to correct non-linearity in wideband power amplifiers (PAs) have become prominent. Yet, there is a void in open-source and measurement-setup-independent platforms for fast DPD exploration and objective DPD model comparison. This paper presents an open-source framework, OpenDPD, crafted in PyTorch, with an associated dataset for PA modeling and DPD learning. We introduce a Dense Gated Recurrent Unit (DGRU)-DPD, trained via a novel end-to-end learning architecture, outperforming previous DPD models on a digital PA DPA in the new digital transmitter (DTX) architecture with unconventional transfer characteristics compared to analog PAs. Measurements show our DGRU-DPD achieves an ACPR of -44.69\/-44.47 dBc and an EVM of -35.22 dB for 200 MHz OFDM signals. OpenDPD code, datasets, and documentation are publicly available at https:\/\/github.com\/lab-emi\/OpenDPD.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0628\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0638\u0631\u0641\u06cc\u062a \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u060c \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0639\u0645\u06cc\u0642 (DNN) \u0628\u0631\u0627\u06cc \u0642\u0628\u0644 \u0627\u0632 \u062a\u0642\u0631\u06cc\u0628 \u062f\u06cc\u062c\u06cc\u062a\u0627\u0644 (DPD) \u0628\u0631\u0627\u06cc \u0627\u0635\u0644\u0627\u062d \u063a\u06cc\u0631\u062e\u0637\u06cc \u062f\u0631 \u062a\u0642\u0648\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u0647\u0627\u06cc \u0642\u062f\u0631\u062a \u067e\u0647\u0646\u0627\u06cc \u0628\u0627\u0646\u062f (PA) \u0628\u0631\u062c\u0633\u062a\u0647 \u0634\u062f\u0647 \u0627\u0646\u062f.\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u062f\u0631 \u0633\u06cc\u0633\u062a\u0645 \u0639\u0627\u0645\u0644 \u0647\u0627\u06cc \u0645\u0633\u062a\u0642\u0644 \u0627\u0632 \u0645\u0646\u0628\u0639 \u0628\u0627\u0632 \u0648 \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0628\u0631\u0627\u06cc \u0627\u06a9\u062a\u0634\u0627\u0641 \u0633\u0631\u06cc\u0639 DPD \u0648 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0645\u062f\u0644 DPD \u0647\u062f\u0641 \u060c \u06cc\u06a9 \u062e\u0644\u0627\u0621 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f.\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0645\u0646\u0628\u0639 \u0628\u0627\u0632 \u060c OpenDPD \u060c \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u062f\u0631 Pytorch \u060c \u0628\u0627 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0631\u062a\u0628\u0637 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0633\u0627\u0632\u06cc PA \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc DPD \u0627\u0631\u0627\u0626\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.\u0645\u0627 \u06cc\u06a9 \u0648\u0627\u062d\u062f \u0645\u06a9\u0631\u0631 \u06af\u06cc\u062a \u0634\u062f\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 (DGRU) -DPD \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u060c \u06a9\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u0645\u0639\u0645\u0627\u0631\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u067e\u0627\u06cc\u0627\u0646 \u0628\u0647 \u067e\u0627\u06cc\u0627\u0646 \u0628\u0647 \u067e\u0627\u06cc\u0627\u0646 \u0645\u06cc \u0631\u0633\u062f \u060c \u0627\u0632 \u0645\u062f\u0644 \u0647\u0627\u06cc DPD \u0642\u0628\u0644\u06cc \u062f\u0631 \u06cc\u06a9 DITICE DPA DIGITAL \u062f\u0631 \u0645\u0639\u0645\u0627\u0631\u06cc \u0641\u0631\u0633\u062a\u0646\u062f\u0647 \u062f\u06cc\u062c\u06cc\u062a\u0627\u0644 \u062c\u062f\u06cc\u062f (DTX) \u0628\u0627 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0627\u0646\u062a\u0642\u0627\u0644 \u063a\u06cc\u0631 \u0645\u062a\u0639\u0627\u0631\u0641 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u0622\u0646\u0627\u0644\u0648\u06af \u060c \u0628\u0647\u062a\u0631 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u06cc\u0645.PAS\u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0647\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 DGRU -DPD \u0645\u0627 ACPR \u0627\u0632 -44.69\/-44.47 DBC \u0648 EVM -35.22 dB \u0631\u0627 \u0628\u0631\u0627\u06cc \u0633\u06cc\u06af\u0646\u0627\u0644 \u0647\u0627\u06cc 200 \u0645\u06af\u0627\u0647\u0631\u062a\u0632 OFDM \u0628\u0647 \u062f\u0633\u062a \u0645\u06cc \u0622\u0648\u0631\u062f.\u06a9\u062f OPENDPD \u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u0645\u0633\u062a\u0646\u062f\u0627\u062a \u0628\u0647 \u0635\u0648\u0631\u062a \u0639\u0645\u0648\u0645\u06cc \u062f\u0631 https:\/\/github.com\/lab-emi\/opendpd \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0627\u0633\u062a.<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc OpenDPD: An Open-Source End-to-End Learning &#038; Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion 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