epsonlq138/matlab_ide

Simulation for "Finite-Alphabet Precoding for Massive MU-MIMO with Low-resolution DACs"

(c) 2018 Chang-Jen Wang and Chao-Kai Wen e-mail: [email protected] and [email protected]

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Information:

• IDE: Iterative discrete estimation
• IDE2: Low-complexity version of Iterative discrete estimation

IDE and IDE2 are efficient algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. For details, please refer to

C. J. Wang, C. K. Wen, S. Jin, and S. H. Tsai, Finite-Alphabet Precoding for Massive MU-MIMO with Low-resolution DACs, IEEE Trans. Wireless Commun., 2018, to appear.

We provide the codes in a way that you can perfrom based on the simulator for "Quantized Precoding for Massive MU-MIMO". Therefore, you can compare severeal different precoding algorithms under the same setting.

How to start a simulation:

• Step 1. Download the simulator for "Quantized Precoding for Massive MU-MIMO":

  https://github.com/quantizedmassivemimo/1bit_precoding

• Step 2. Download our proposed precoders (IDE.m & IDE2.m), which can be found

  https://github.com/Wangchangjen/Matlab_IDE

• Step 3. In precoder_sim.m, find the line

  par.precoder = …

  Replace the line by

  par.precoder = {'IDE','SQUID','IDE2','SDR1','SDRr'}; % select precoding scheme(s) to be evaluated

• Step 4. In precoder_sim.m, find the line

  switch (par.precoder{pp})

  Include the cases

  case 'IDE'

  [x, beta] = IDE(par.s,Hhat,N0);

  case 'IDE2'

  [x, beta] = IDE2(par.s,Hhat,N0);

• Step 5. Now, you are ready to run the precodes:

  precoder_sim

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The simulator returns a plot of the BER as a function of the SNR.