Research On MIMO-OFDM Channel Estimation

With the rapid development of wireless mobile communication, more efficient, safe and reliable multimedia data services arises at the historic moment, they have a larger channel capacity and efficient spectrum utilization. Using different diversity of multiple input multiple output (MIMO) technology can significantly improve data transmission speed, channel capacity and spectrum efficiency, and it is in under the condition of no increase system bandwidth and transmission power, Use of multicarrier modulation technology of orthogonal frequency division multiplexing (OFDM) technology can effectively combat frequency selective fading, reason is that several orthogonal subchannels formed its physical channel. However, through the insertion of a cyclic prefix (CP) can effectively eliminate caused by multipath symbol interference (ISI) and inter carrier interference (ICI). The combination of MIMO and OFDM technology of MIMO-OFDM technology can make full use of space resources to improve system capacity, and effectively combat frequency selective fading, so the MIMO-OFDM technology has become a new generation of wireless communication technology research one of the hot technology.Space time detection at the receiving end and the coherent demodulation is the key technology of MIMO-OFDM systems, however, the channel estimation plays a very important role, accurate channel estimation can improve the performance of the whole system. At the same time, the various channel estimation algorithm is arises at the historic moment.First, this paper mainly analyzes the background, research status quo of OFDM and MIMO technology, OFDM and MIMO technology is expounded, and the basic principles of MIMO-OFDM technology and system model, Introduced the characteristics of wireless channel, signal fading and multipath effect, time-varying, etc.Second, the comparison of several typical data aided channel estimation methods:least squares (LS) channel estimation algorithm, linear minimum mean square error (LMMSE) channel estimation algorithm, based on singular value decomposition and the linear minimum mean square error (SVD-LMMSE) channel estimation algorithm, and the performance analysis and simulation verification. then introduced the subspace based blind channel estimation and linear precoding based blind channel estimation.Finally, analysis semi blind channel estimation based on subspace and semi blind channel estimation based on adaptive filter. In channel estimation based on adaptive filter, analyses the least mean square (LMS) semi blind channel estimation algorithm and the recursive least squares (RLS) semi blind channel estimation algorithm, Through the system simulation compare the performance of both. Finally worked out a kind of improved algorithm based on adaptive filter, through the simulation, this algorithm improved the system performance, better than the LMS algorithm, and adapt to more widely.