In a wireless communication system, system performance is restricted mainly by the radio channel. Propagation path between the base station and the receiver is complicated, line of sight transmission from the simple to the obstacle by reflection, refraction, scattering propagation effects. In the wireless transmission environment, the received signal at the presence of multipath delay time selective fading and frequency offset domain, multipath delay will bring intersymbol interference ( the ISI ), can be reduced by inserting a guard interval; and as timing frequency offset and fading caused by ICI ( the ICI ), in addition to relying on the frequency offset compensation to correct, but also the need for channel estimation, further compensation, i.e. the need for frequency domain equalization and time domain equalization. Therefore, estimation performance directly affects a signal demodulation result of the received signal. Here on equalization not be described in detail.
From the big perspective, the channel estimation is divided into non-blind channel estimation and blind channel estimation. As the name implies, a non-blind channel estimation using a base station and a receiver both require a known pilot sequence for channel estimation, and estimates the channel between the subcarriers between pilot symbols in response to the frequency domain or the use of different time interpolation techniques. Currently non-blind channel is mainly used for estimating comprises least squares ( the LS ) channel estimation, minimum mean square error ( the MMSE ) channel estimation based on DFT channel estimation based on decision feedback channel estimation and the like; and blind channel estimation does not need to have been known pilot sequence, including a desired channel based on the maximum estimation, channel estimation based on the subspace, and the like. Next, the LS channel estimates are introduced.
LS channel estimation is a channel estimation method of least squares criterion. In a wireless system, the received signal may be expressed as:
Wherein, X- represents the originally transmitted signal vector, H represents a channel response vector, the Z represents a noise vector, the Y denotes a reception signal vector.
The least squares criterion, the following objective function:
Wherein 
represents the LS channel estimation result on said object request function on 
the partial derivative and the second partial derivatives of the first order, the first order partial derivative with a
There are second order partial derivative
The second order partial derivative greater than zero, the minimum value of the objective function is present can be seen, so that a first partial derivatives to zero, there is
Can be obtained by the above formula the minimum of the objective function, i.e., the LS channel estimation solution is:
Order 
represents 
elements, 
assuming no the ICI , each subcarrier on the LS channel estimation result can be expressed as
Assumptions 
and 
plural, and 
has been amplitude normalized, there
LS channel estimation algorithm is relatively simple, low computational complexity, but ignore the influence of noise, this two-analysis given below, LS channel estimation mean square error ( the MSE ) of
As can be seen from the above equation MSE signal to noise ratio SNR is inversely proportional means LS channel estimation enhanced noise, especially at low SNR case, the channel estimation accuracy is severely affected. Nevertheless, due to the simple, this method is still practical in large-scale use.