Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system |
| |
| Authors: | Hui-Ping Yin Hai-Peng Ren |
| |
| Institution: | 1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China;2. State Key Laboratory of Synthetical Automation of Process Industries, Northeastern University, Shenyang 110819, Liaoning, China;1. Institute of Dynamics and Control Science, Shandong Normal University, Jinan 250014, China;2. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;1. School of Mathematics, Southeast University, Nanjing 210096, PR China;2. School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing 210023, PR China;1. School of Physics and Electronic Engineering, Taishan University, No. 525 Dongyue Street, Tai’an City, China;2. Open Studio for Marine High Frequency Communications, Pilot National Laboratory for Marine Science and Technology, Qingdao, China;3. College of Computer Science and Technology, Qingdao University, Qingdao 266071, China;4. School of Science and Information Science, Qingdao Agricultural University, Qingdao 266109, China;5. College of Information Science and Engineering, Ocean University of China, Qingdao, China;1. Navigation College, Dalian Maritime University, Dalian, Liaoning 116026, China;2. SIASUN Robot & Automation CO.,Ltd. Shenyang Liaoning 110169, China;3. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China |
| |
| Abstract: | To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
