Ofdm System Channel Estimation In Wireless Communications Technology Research

Owe to the spectral efficiency and robustness against multipath channels, Orthogonal Frequency Division Multiplexing (OFDM) has become one of the major modulation techniques of high data rate communication systems. However, both the multipath channels and the inter carrier interference (ICI) have inceresed the difficulty of the channel estimation. Thus effictive channel estimation becomes one of the key factors that affect the application of OFDM. This paper is mainly about the study of parameter channel estimation of OFDM system in the condition of quasi-static channel environment which is Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT), and OFDM channel estimation of fast time-varying channel based on Linear Model (LM) or Basis Expansion Model (BEM).In chapter two, the technique of nonparameter channel estimation of quasi-static channel is introduced, including constant interpolation, linear interpolation, cubic interpolation, DFT interpolation, and the modified DFT interpolation method which can mitigation part of noise, etc.. And both DFT and modified DFT interpolation methods have attracted much more attention because of their outstanding performance among all of the interpolation method above, as well as low complexty. However, they are based on the channel model of integral path delays, performance of the two DFT interpolation methods will degrade rapidly due to the nonintgral path delays.In Chapter three, the technique of parameter channel estimation of quasi-static channel is studied. That is ESPRIT, which apart the signal or channel subspace and the nosie subspace by utilizing the shifting invariance character of channel subspace of the linear array. Then the multipath delays of the channel can be estimated from the channel subspace, and the channel information can be reconstructed by use of the mutipath delays. We mainly focus on two subspace decomposition method, subspace tracking and enginvalue decomposition, which are need by ESPRIT. And inspired by hopping frequency, the convergence speed of the subspace tracking method is accelerated. During the two subspace decomposition method, subspace tracking method has the robuster performance in case of the channel conditions, such as the multipath numbers and multipath delays, do not variant during a long time. The enginvalue decomposition method does not need such a precondition, but a poorer performance, especially in case of the poorer environment. Moreover, we introduce the subspace tracking method from multi-symbol into single-symbol. Finally, we investigate the optimization of pilot numbers, pilot space and pilot structure. From the study, we find that the pilot space is the key of the performance of ESPRIT. If the pilot space condition is satisfied, the pilot numbers can be as small as possible. As for the pilot structure, groupd equal space pilot structure can be used, as well as the conventional equal space pilot structure, which exposes the application scene of ESPRIT.Chapter four is mainly about the OFDM channel estimation on fast time-varying channel. During a time interval, such as the period of one OFDM symbol, the time-varying of the channel can be approximated by a LM model or BEM model. Both the two models can be used in the gentle time-varying channel, which means the normalized dopple shifting is less than 0.2, and the BEM model can also be used in the condition that the normalized dopple shifting is more than 0.2. In additional, the LM model can be used in nonintegral channel pathdelay condition, the BEM model but not. We firstly focus on the LM model, and a novel method which based on ESPRIT is proposed. Here, the iterative feedback method is needed to mitigate the ICI, so that the channel path delays can be acquired accurately. And because of this, we defend the flaw of conventional method which based on integral channel pathdelays. Secondly, we focus on the BEM model. Instead of estimating the channel impulse, much less numbers of coefficients are estimated, which degrade the channel estimation difficulty greatly. In the simulation, we choose DKL-BEM model and GCE-BEM model which two models have the higher and robust approximation, and Linear Minimum Mean Square Error (LMMSE) estimator is used in DKL-BEM model because both the model and the estimator need to know the statistic of the channel, As to GCE-BEM model Least Square estimator (LS) and Best Linear Unbiased Estimator (BLUE) is used without to know any information of the channel statistic.