Stefan A. Damjancevic, Samuel Ajay Dasgupta, E. Matús, Dmitry Utyanksy, P. V. D. Wolf, G. Fettweis
{"title":"基于矢量数字信号处理器的3GPP 5G物联网柔性信道估计","authors":"Stefan A. Damjancevic, Samuel Ajay Dasgupta, E. Matús, Dmitry Utyanksy, P. V. D. Wolf, G. Fettweis","doi":"10.1109/SiPS52927.2021.00011","DOIUrl":null,"url":null,"abstract":"The new 5G Reduced Capability (RedCap) protocol offers up to 88x and 528x higher data rates and dynamic pilot placements compared to previous Cat-M and NB-IoT standards, respectively. This leads to high application variability of IoT devices and therefore poses a challenge for the implementation of channel estimation (CE), especially under weak radio signal conditions. However, due to the computational complexity of optimal methods, practical suboptimal approaches with denoising capability are preferred in low-power devices. This work investigates the performance and implementation aspects of practical IoT CE denoising techniques on a vector digital signal processor (vDSP). This solution enables adaptation to the new IoT workload requirements with a 15.9x speed-up compared to the non-vectorised approach at 99.2% processor efficiency. In addition, for the purpose of solution adaptation to various IoT standards, the clock frequency requirements for the complete channel estimation chain are analysed with respect to different processor configurations.","PeriodicalId":103894,"journal":{"name":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flexible Channel Estimation for 3GPP 5G IoT on a Vector Digital Signal Processor\",\"authors\":\"Stefan A. Damjancevic, Samuel Ajay Dasgupta, E. Matús, Dmitry Utyanksy, P. V. D. Wolf, G. Fettweis\",\"doi\":\"10.1109/SiPS52927.2021.00011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The new 5G Reduced Capability (RedCap) protocol offers up to 88x and 528x higher data rates and dynamic pilot placements compared to previous Cat-M and NB-IoT standards, respectively. This leads to high application variability of IoT devices and therefore poses a challenge for the implementation of channel estimation (CE), especially under weak radio signal conditions. However, due to the computational complexity of optimal methods, practical suboptimal approaches with denoising capability are preferred in low-power devices. This work investigates the performance and implementation aspects of practical IoT CE denoising techniques on a vector digital signal processor (vDSP). This solution enables adaptation to the new IoT workload requirements with a 15.9x speed-up compared to the non-vectorised approach at 99.2% processor efficiency. In addition, for the purpose of solution adaptation to various IoT standards, the clock frequency requirements for the complete channel estimation chain are analysed with respect to different processor configurations.\",\"PeriodicalId\":103894,\"journal\":{\"name\":\"2021 IEEE Workshop on Signal Processing Systems (SiPS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Workshop on Signal Processing Systems (SiPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SiPS52927.2021.00011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Workshop on Signal Processing Systems (SiPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SiPS52927.2021.00011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Flexible Channel Estimation for 3GPP 5G IoT on a Vector Digital Signal Processor
The new 5G Reduced Capability (RedCap) protocol offers up to 88x and 528x higher data rates and dynamic pilot placements compared to previous Cat-M and NB-IoT standards, respectively. This leads to high application variability of IoT devices and therefore poses a challenge for the implementation of channel estimation (CE), especially under weak radio signal conditions. However, due to the computational complexity of optimal methods, practical suboptimal approaches with denoising capability are preferred in low-power devices. This work investigates the performance and implementation aspects of practical IoT CE denoising techniques on a vector digital signal processor (vDSP). This solution enables adaptation to the new IoT workload requirements with a 15.9x speed-up compared to the non-vectorised approach at 99.2% processor efficiency. In addition, for the purpose of solution adaptation to various IoT standards, the clock frequency requirements for the complete channel estimation chain are analysed with respect to different processor configurations.