2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)最新文献_第5页

A Two-stage Adaptive Weight-adjusting Interference Cancellation Demodulation Technology Based on SLIC and CWIC for NOMA 基于SLIC和CWIC的两级自适应加权干扰对消解调技术

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00013

Ruo-Nan Du

{"title":"A Two-stage Adaptive Weight-adjusting Interference Cancellation Demodulation Technology Based on SLIC and CWIC for NOMA","authors":"Ruo-Nan Du","doi":"10.1109/CONF-SPML54095.2021.00013","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00013","url":null,"abstract":"Gbps business will become an important part of the future mobile communications systems. It has been shown that non-orthogonal multiple access (NOMA) based on power multiplexing could potentially offer a robust performance in the spectrum utilization efficiency. However, when the terminal performs demodulation, the difference in user power superposition and the non-uniformity of user distribution may lead to some severe problems such as intensive excessive power or insufficient signal-to-noise ratio (SNR) under different scenarios, the performance of the communication system is reduced. Therefore, in this paper, a two-stage adaptive weight-adjusting interference cancellation (AWIC) demodulation technology based on symbol level based interference cancellation (SLIC) and code word level interference cancellation (CWIC) has been developed and presented. Moreover, we analyzed the downlink transmission performance of NOMA, innovated the multi-stage adaptive weight-adjusting serial interference cancellation (SIC) demodulation technology, and adjusted the depth of the demodulation algorithm according to the posterior decoding performance feedback. It improves NOMA demodulation performance under a low SNR environment and reduced the complexity under a high SNR environment. According to the computer simulations, under the average bit error rate (BER) of $3times 10^{-2}$, the improved NOMA interference cancellation approach proposed in this paper has a 5.09 dB performance improvement compared to SLIC and 9.8 dB compared to CWIC.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transmission Channel modeling and analysis of Intelligent Reflecting Surface 智能反射面传输通道建模与分析

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00045

Guoyuan Li

{"title":"Transmission Channel modeling and analysis of Intelligent Reflecting Surface","authors":"Guoyuan Li","doi":"10.1109/CONF-SPML54095.2021.00045","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00045","url":null,"abstract":"Wireless communication has developed rapidly in recent years. The fifth-generation(5G) era has arrived in an all-round way, and 5G technology has achieved a wide range of applications and brought great convenience to our life. With the all-round development of 5G technology, we begin to look forward to the future sixth-generation(6G) technology. In many conferences and reports, we have put the 6G technology research on the agenda. As an effective way to control the wireless environment channel manually, Intelligent Reflecting Surface(IRS)1 has become one of the 6G developable technologies and is getting more and more attention. IRS assisted wireless channel and the change of channel model are important research contents in the future wireless communication. Channel model is divided into large-scale model and small-scale model, among which the research of large-scale model is the most important one in the future technical research. The model of IRS Assisted Wireless Channel is still relatively basic in the current research, so this paper aims to further explore and research. In this paper, based on the model of path loss in free space, we analyze the path model of IRS assisted wireless communication transmission. We divide the path loss model into two scenarios: near field and far field. At the end of the article, we use matlab to simulate the theoretical model numerically, and plot the change of path loss results with different distances. We find that the simulation results are basically consistent with the theoretical model.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130196003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Airbnb Pricing Based on Statistical Machine Learning Models 基于统计机器学习模型的Airbnb定价

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00042

Yinyihong Liu

引用次数: 1

Performance Review of Generative Adversarial Network for a Bi-directional Task 双向任务生成对抗网络的性能评价

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00017

Chao Wu

引用次数: 0

Estimate the Housing Price Under the Impact Of COVID-19 and Possible Migration Due to the Demand for Density 估算新冠疫情影响下的房价以及因密度需求而可能出现的人口迁移

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00033

Qingyuan Jiang

引用次数: 0

Stabilization with the Idea of Notch Filter in Automatic Control System 陷波滤波思想在自动控制系统中的镇定

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00025

Jailin Chen

引用次数: 0

Transfer Learning on Interstitial Lung Disease Classification 间质性肺疾病分类的迁移学习

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00046

Zhi Yi, Yuyang Wang

{"title":"Transfer Learning on Interstitial Lung Disease Classification","authors":"Zhi Yi, Yuyang Wang","doi":"10.1109/CONF-SPML54095.2021.00046","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00046","url":null,"abstract":"For the treatment of Interstitial Lung Disease, it is crucial to have an early diagnosis. However, doctors still have a lot of controversy in the diagnosis of lung nodules even with today’s highly developed medical imaging technology. In this article, we summarized the five major challenges we face in medical image recognition and systematically listed the applications from traditional image recognition technology to deep learning in lung CT image recognition. Compared to the traditional convolutional neural network built and trained from scratch, it is beneficial to apply transfer learning to the recognition of lung nodules. Transfer learning focus on transferring knowledge from previous well-trained task to target learning task. Transferring means pretrained networks utilize fine-tuning to reduce iteration times of weight so that it can cope with the problem of lack of high quality images. Various experiments demonstrate that transfer learning performances better than traditional convolutional neural network under complicated circumstances of image recognition such as medical images. In this article, transfer learning is classified into 3 types: inductive transfer learning, transductive transfer learning and unsupervised transfer learning. The main difference between them is label quantity of target training set. Inductive transfer learning highly depends on feature engineering. Compared to it, training sets of two remaining has few labels. However, transductive transfer learning and unsupervised transfer learning are unstable while facing sophisticated cases.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Review on LiDAR-based SLAM Techniques 基于激光雷达的SLAM技术综述

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00040

Leyao Huang

引用次数: 17

Advances in the Deep Brain Stimulation for Parkinson’s Disease 脑深部电刺激治疗帕金森病的研究进展

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00018

Ping Xia

引用次数: 2

Research on Battery Monitoring Technology Based on Internet of Things 基于物联网的电池监测技术研究

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00037

Yiran Hu

引用次数: 0