Pablo M. Aviles, Diego Lloria, J. A. Belloch, Sandra Roger, A. Lindoso, Maximo Cobos
{"title":"多核平台上TSDCE MIMO信道估计算法的加速","authors":"Pablo M. Aviles, Diego Lloria, J. A. Belloch, Sandra Roger, A. Lindoso, Maximo Cobos","doi":"10.1145/3544538.3544662","DOIUrl":null,"url":null,"abstract":"The use of Multi-Processor System-on-Chip (MPSoC) is becoming widespread in a huge number of signal processing systems, including wireless communications and vehicular technology applications. In those scenarios, when Multiple-Input Multiple-Output (MIMO) communication schemes are considered, the system usually has to deal with a high number of communication links that involve sensors and antennas from different vehicles and users. The use of MIMO systems with a high number of antennas increases the complexity of many signal processing algorithms which could benefit from computationally efficient implementations. The Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC is a well-positioned platform to manage computationally-demanding communication systems. This platform holds a dual-core ARM Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end Field Programmable Gate Array (FPGA). In particular, this work aims to evaluate the computational performance of the Transformed Spatial Domain Channel Estimation (TSDCE), a novel millimeter-wave MIMO channel estimation algorithm, on the proposed embedded platform. This work focuses firstly on developing an efficient sequential implementation that runs on the ARM Cortex-A53, so that we can afterwards leverage the use of the multi-core system to accelerate the sequential performance.","PeriodicalId":347531,"journal":{"name":"Proceedings of the 11th Euro American Conference on Telematics and Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Acceleration of the TSDCE MIMO Channel Estimation Algorithm on a Multi-core Platform\",\"authors\":\"Pablo M. Aviles, Diego Lloria, J. A. Belloch, Sandra Roger, A. Lindoso, Maximo Cobos\",\"doi\":\"10.1145/3544538.3544662\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of Multi-Processor System-on-Chip (MPSoC) is becoming widespread in a huge number of signal processing systems, including wireless communications and vehicular technology applications. In those scenarios, when Multiple-Input Multiple-Output (MIMO) communication schemes are considered, the system usually has to deal with a high number of communication links that involve sensors and antennas from different vehicles and users. The use of MIMO systems with a high number of antennas increases the complexity of many signal processing algorithms which could benefit from computationally efficient implementations. The Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC is a well-positioned platform to manage computationally-demanding communication systems. This platform holds a dual-core ARM Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end Field Programmable Gate Array (FPGA). In particular, this work aims to evaluate the computational performance of the Transformed Spatial Domain Channel Estimation (TSDCE), a novel millimeter-wave MIMO channel estimation algorithm, on the proposed embedded platform. This work focuses firstly on developing an efficient sequential implementation that runs on the ARM Cortex-A53, so that we can afterwards leverage the use of the multi-core system to accelerate the sequential performance.\",\"PeriodicalId\":347531,\"journal\":{\"name\":\"Proceedings of the 11th Euro American Conference on Telematics and Information Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th Euro American Conference on Telematics and Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3544538.3544662\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th Euro American Conference on Telematics and Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3544538.3544662","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Acceleration of the TSDCE MIMO Channel Estimation Algorithm on a Multi-core Platform
The use of Multi-Processor System-on-Chip (MPSoC) is becoming widespread in a huge number of signal processing systems, including wireless communications and vehicular technology applications. In those scenarios, when Multiple-Input Multiple-Output (MIMO) communication schemes are considered, the system usually has to deal with a high number of communication links that involve sensors and antennas from different vehicles and users. The use of MIMO systems with a high number of antennas increases the complexity of many signal processing algorithms which could benefit from computationally efficient implementations. The Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC is a well-positioned platform to manage computationally-demanding communication systems. This platform holds a dual-core ARM Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end Field Programmable Gate Array (FPGA). In particular, this work aims to evaluate the computational performance of the Transformed Spatial Domain Channel Estimation (TSDCE), a novel millimeter-wave MIMO channel estimation algorithm, on the proposed embedded platform. This work focuses firstly on developing an efficient sequential implementation that runs on the ARM Cortex-A53, so that we can afterwards leverage the use of the multi-core system to accelerate the sequential performance.