Nonlinear Decoding In Mimo Communication Systems

In recent years, the wireless communication develops quickly, mobile phone gets universal and various new businesses of mobile multi-media seems ready to come out, more and more applications need high data rate access. Because of the affection of noise and signal fading, in order to reach high data rate access and high quality, wireless communication needs new technology to improve the link reliability and enhance the spectrum efficiency. MIMO(multiple input and multiple output)can enhance the spectrum efficiency and increase channel capacity greatly, and reduce the multipath affection without spectrum band and power increasing. In the wideband city area network (eg.WiMAX), the mobile communication long-term evolution and the Ultra-wide band etc. will adopt the MIMO antenna technique.Nonlinear signal processing technology has obtained the wide attention in the beamforming, it can efficiently ues the higher order statistic information which in traditional linear technology processing discarded, thus more effective estimate various system parameters. The domestic and foreign researches of using the nonlinear decoding technology in the MIMO communication system are at the startstage. By improving the MIMO system decoding performance, the nonlinear technology can make the contribution for the communication development and application. But in the treating processes, bigger calculation complication obstructs its application. Through using the method of sparsing the signal space dimension, can effectively reduce non-linear processing computation complex, thus lays the foundation in the actual project widely. This thesis has done some research under this background.Firstly, this thesis introduced the development of mobile communication, then given out the key technology of next generation communication. MIMO and STBC are given out in the next chapter. Some general linear decoding criterions and algorithms are introduced subsequently. The nonlinear algorithm is given out in the fourth chapter, and payed much attention to the decoding improvement in the array receive and the MIMO system in the quasi-static Rayleigh fading channel. The method of sparsing the signal space dimensions which effectively reducing non-linear processing computation complex is reaserched in the fifth chapter. The nonlinear multiuser detection schemes are studied in the next chapter, considering the different parameter condition. This thesis has proven the validity and superiority of the nonlinear algorithm through the massive simulations. The full text summary and the future forecast are given out in the last chapter.