whatshow_phy_detect​_bpic

This detection method is proposed in A Bayesian Receiver With Improved Complexity-Reliability Trade-Off in Massive MIMO Systems.

How to use

All Bayesian PIC-DSC detector codes are uniform in matlab and python as a class of `BPIC`. This class is the whole process of the detection. This section will illustrate the methods of this class following the detection process.

1. BPIC

• @constellation: the constellation, a vector.
• @bso_mean_init: 1st iteration method in **BSO** to calculate the mean. Default: `BPIC.BSO_INIT_MMSE`, others: `BPIC.BSO_INIT_MRC, BPIC.BSO_INIT_ZF` (`BPIC.BSO_INIT_NO` should not be used but you can try)
• @bso_mean_cal: other iteration method in **BSO** to calculate the mean. Default: `BPIC.BSO_MEAN_CAL_MRC` (`BPIC.BSO_MEAN_CAL_ZF` should not be used but you can try)
• @bso_var: use approximate or accurate variance in **BSO**. Default: `BPIC.BSO_VAR_APPRO`, others: `BPIC.BSO_VAR_ACCUR`
• @bso_var_cal: the method in **BSO** to calculate the variance. Default: `BPIC.BSO_VAR_CAL_MRC`, others: `BPIC.BSO_VAR_CAL_MRC` (`BSO_VAR_CAL_ZF` should not be used but you can try)
• @dsc_ise: how to calculate the instantaneous square error. Default: `BPIC.DSC_ISE_MRC`, others: `BPIC.DSC_ISE_NO, BPIC.DSC_ISE_ZF, BPIC.DSC_ISE_MMSE`
• @dsc_mean_prev_sour: the source of previous mean in DSC. Default: `BPIC.DSC_MEAN_PREV_SOUR_BSE`, others: `BPIC.DSC_MEAN_PREV_SOUR_DSC`
• @dsc_var_prev_sour: the source of previous variance in DSC. Default: `BPIC.DSC_VAR_PREV_SOUR_BSE`, others: `BPIC.DSC_VAR_PREV_SOUR_DSC`
• @min_var: the minimal variance.
• @iter_num: the maximal iteration.
• @iter_diff_min: the minimal difference in **DSC** to early stop.
• @detect_sour: the source of detection result. Default: `BPIC.DETECT_SOUR_DSC`, others: `BPIC.DETECT_SOUR_BSE`.

// paper version 1: for BSO, MMSE in 1st iteration but MRC in others
bpic = BPIC(sympool);
// paper version 2: MRC in all iterations
bpic = BPIC(sympool, "bso_mean_init", BSO_MEAN_INIT_MRC); % matlab
bpic = BPIC(sympool, bso_mean_init=BSO_MEAN_INIT_MRC); # python
// other configurations
% matlab
bpic = BPIC(sympool, "bso_mean_init", BPIC.BSO_MEAN_INIT_MMSE, "bso_var", BPIC.BSO_VAR_APPRO, "bso_var_cal", BPIC.BSO_VAR_CAL_MMSE, "dsc_ise", BPIC.DSC_ISE_MMSE, "detect_sour", BPIC.DETECT_SOUR_BSE);
# python
bpic = BPIC(sympool, bso_mean_init=BPIC.BSO_MEAN_INIT_MMSE, bso_var=BPIC.BSO_VAR_APPRO, bso_var_cal=BPIC.BSO_VAR_CAL_MMSE, dsc_ise=BPIC.DSC_ISE_MMSE, detect_sour=BPIC.DETECT_SOUR_BSE);

2. detect: the estimated symbols from Tx

• @y: the received signal, a vector
• @H: the channel matrix, a matrix
• @No: the noise power, a scalar
• @sym_map: whether use hard mapping

// symbol estimation - soft
x_est = bpic.detect(y, H, No);
// symbol estimation - hard
x_est = bpic.detect(y, H, No, "sym_map", true); % matlab
x_est = bpic.detect(y, H, No, sym_map=true); # python