Multi-stage detection using constellation structure

被引:0
|
作者
Sen Gupta, Ananya [1 ]
Singer, Andrew C. [1 ]
机构
[1] Univ Illinois, Dept Elect & Comp Engn, Coordinated Sci Lab, Urbana, IL 61801 USA
基金
美国国家科学基金会;
关键词
D O I
10.1109/ACSSC.2006.354815
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A structural approach to multi-stage detection is proposed where the joint information between interfering users is utilized to give better soft decisions on the user bits in each stage or iteration. The key idea is to use the structure of the multi-user signal constellation to achieve higher performance in terms of the bit-error-rate, particularly at high SNR, and reduce the possibility of error propagation or limit cycles. A maximal asymptotic efficiency detector is employed as the first stage to generate better initial estimates of the user bits, followed by a local maximum likelihood kernel for subsequent stages. The proposed detector is particularly attractive for heavily correlated or over-loaded multi-user systems. The proposed multi-stage detector also includes the possibility of known memoryless non-linearity in the system, e.g., the non-linearity introduced due to saturation by the downlink RF amplifier in satellite communication systems.
引用
收藏
页码:585 / +
页数:2
相关论文
共 50 条
  • [31] A Novel Multi-Stage Approach for Hierarchical Intrusion Detection
    Verkerken, Miel
    D'hooge, Laurens
    Sudyana, Didik
    Lin, Ying-Dar
    Wauters, Tim
    Volckaert, Bruno
    De Turck, Filip
    IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2023, 20 (03): : 3915 - 3929
  • [32] A multi-stage neural network for automatic target detection
    Howard, A
    Padgett, C
    Liebe, CC
    IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 231 - 236
  • [33] Multi-Stage CNN Architecture for Face Mask Detection
    Chavda, Amit
    Dsouza, Jason
    Badgujar, Sumeet
    Damani, Ankit
    2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,
  • [34] Multi-Stage Contextual Deep Learning for Pedestrian Detection
    Zeng, Xingyu
    Ouyang, Wanli
    Wang, Xiaogang
    2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2013, : 121 - 128
  • [35] Network intrusion detection by a multi-stage classification system
    Cordella, LP
    Limongiello, A
    Sansone, C
    MULTIPLE CLASSIFIER SYSTEMS, PROCEEDINGS, 2004, 3077 : 324 - 333
  • [36] Design of multi-stage combline BPF in laminated structure
    Awai, I
    Yamaguchi, H
    Endo, K
    Yamashita, Y
    APMC 2001: ASIA-PACIFIC MICROWAVE CONFERENCE, VOLS 1-3, PROCEEDINGS, 2001, : 735 - 740
  • [37] FREE TRAINING OBJECT DETECTION BASED ON MULTI-STAGE FUSION USING BELIEF FUNCTIONS
    Farhat, Mariem
    Mhiri, Slim
    Tagina, Moncef
    2016 INTERNATIONAL SYMPOSIUM ON SIGNAL, IMAGE, VIDEO AND COMMUNICATIONS (ISIVC), 2016, : 153 - 158
  • [38] Generating Vectors from Images using Multi-Stage Edge Detection for Robotic Artwork
    Nag, Sukanya
    Bhattacharjee, Deepsikha
    Bhaumik, Archisman
    Deb, Suman
    2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2021), 2021, : 651 - 656
  • [39] Substructuring Technique for Damage Detection Using Statistical Multi-Stage Artificial Neural Network
    Bakhary, Norhisham
    Hao, Hong
    Deeks, Andrew J.
    ADVANCES IN STRUCTURAL ENGINEERING, 2010, 13 (04) : 619 - 639
  • [40] Text Detection Using Multi-Stage Region Proposal Network Sensitive to Text Scale
    Nagaoka, Yoshito
    Miyazaki, Tomo
    Sugaya, Yoshihiro
    Omachi, Shinichiro
    SENSORS, 2021, 21 (04) : 1 - 15