Research on multiple classified signal detection algorithms based on mimo-ofdm system |
| |
| Affiliation: | 1. ZhouKou Normal University, Zhoukou, Henan 466001, China;2. Zhoukou Vocational and Technical College, Zhoukou, Henan 466001, China;2. The Department of Basic Education, Shanghai Urban Construction Vocational College, Shanghai, 201415, China;1. Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China;2. School of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, China;3. Obstetrics and Gynecology Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China;1. Assistant Professor, Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India;2. Professor, Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India;1. Pediatrics, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116023, China;2. Department of Rheumatology and Immunology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116023, China |
| |
| Abstract: | OFDM is a high-speed signal transmission technology in wireless environment. In wireless channels, the frequency response curve is generally non-flat. MIMO technology can effectively increase system capacity, improve system performance, and improve network coverage and transmission reliability. OFDM technology can effectively resist intersymbol crosstalk caused by multipath propagation by adding a certain length of redundant guard interval in front of transmission symbols. According to the channel estimation and signal detection algorithms in MIMO OFDM systems, this paper will choose a better algorithm to implement MIMOOFDM systems and channel estimation and signal detection algorithms. Theoretical analysis and simulation results show that the optimal detection algorithm has the best performance, but its complexity is exponentially related to the number of antennas and modulation order. The architecture for dense texture analysis is developed based on Field Programmable Gate Array FPGA device which is a general engine for high-speed image processing. In order to maximize the performance, our architecture is designed for minimizing consumed resources. As explained in subsection our architecture is composed of two main operations, namely the AH based sum and difference of images and the calculus of texture features |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
