{"title":"基于采样的MIMO检测的低复杂度实现","authors":"Rui Ding, Xiqi Gao, X. You","doi":"10.1109/ICNNSP.2008.4590442","DOIUrl":null,"url":null,"abstract":"A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A low-complexity implementation of sampling-based MIMO detection\",\"authors\":\"Rui Ding, Xiqi Gao, X. You\",\"doi\":\"10.1109/ICNNSP.2008.4590442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.\",\"PeriodicalId\":250993,\"journal\":{\"name\":\"2008 International Conference on Neural Networks and Signal Processing\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Neural Networks and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNNSP.2008.4590442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Neural Networks and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNNSP.2008.4590442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A low-complexity implementation of sampling-based MIMO detection
A low-complexity implementation of sequential Monte Carlo (SMC) sampling-based detector is developed for multiple-input multiple-output (MIMO) communication systems. Unlike previous reports about SMC sampling that widely sequentially draw samples and process each sample independently, we present a novel sampling method which collaboratively processes all samples and extracts information from a collection of samples to establish the sampling space to draw next samples. Simultaneously, the proposed method adopts a reselection step to save storage resource and decrease computation burden. Simulations indicate that the proposed solution decreases the necessary amount of samples and improves the system performance compared with the classical SMC detector. The revised detector is also compared with sphere decoding (SD) that has the comparable computation burden, and simulation result shows that it can obtain the same performance as SD with lower complexity.