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matlab demudulation example

LDPC编解码的源程序，可以在MATLAB中成功运行(LDPC codec source code, you can successfully run in MATLAB)

说明：  R/S分析，matlab程序，虽然比较繁琐，但还好使，请多多关注(R/S)

Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.Model of SSSC which is used to compensate for the reactive power in transmission line.

Using BPSK modulation scheme,in MIMO system, alamouti code & both OSTB code for a rayleigh fading channel . for 2*1, 2*2, for 4*4, for 8*8

5x12 Font is included in 16 variants: - Assembler and C header (.asm / .h) - Horizontal and Vertical arranged bits (horizontal / vertical) - Mirrored and non-mirrored (MSB / LSB) - Left or top aligned, right-aligned or bottom (1/2). For fonts have the size of a multiple of 8 pixels in the corresponding direction, both versions are identical. Fonts higher than 16 pixels are only available in horizontal arrangement.

基于matlab的例程，用于谐波分析的三谱线插值法(Three-line interpolation for harmonic analysis)

8psk的蒙特卡洛仿真8psk的蒙特卡洛仿真(8psk Monte Carlo simulation with matlab)

turbo 均衡的经典文献matlab实现，经过几次迭代得到信噪比与误码率之间的曲线(turbo balanced classic literature Matlab realized, After several iterative be SNR and bit error rate between the curve)

梯度下降法是一個一階最佳化算法，通常也稱為最速下降法。 要使用梯度下降法找到一個函數的局部極小值，必須向函數上當前點對應梯度（或者是近似梯度）的反方向的規定步長距離點進行疊代搜索。如果相反地向梯度正方向疊代進行搜索，則會接近函數的局部極大值點；這個過程則被稱為梯度上升法。(The gradient descent method is a first-order optimization algorithm, also commonly referred to as the steepest descent method. To find the local minimum of a function using the gradient descent method, an iterative search must be performed to the specified step distance point in the opposite direction of the gradient (or approximate gradient) of the current point on the function. If the search is reversed in the opposite direction to the positive direction of the gradient, it will approach the local maximum point of the function; this process is called the gradient ascent method.)