File:MIMO Capacity.png

Summary

Description
English: The source code is also available in our GitHub repository.
Date
Source Own work
Author Kirlf
PNG development
InfoField
Source code
InfoField

Python code

import numpy as np
from numpy import linalg as LA
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt

def waterpouring(Mt, SNR_dB, H_chan):
    SNR = 10**(SNR_dB/10)
    r = LA.matrix_rank(H_chan)
    H_sq = np.dot(H_chan,np.matrix(H_chan, dtype=complex).H)
    lambdas = LA.eigvals(H_sq) 
    lambdas = np.sort(lambdas)[::-1]
    p = 1;
    gammas = np.zeros((r,1))
    flag = True
    while flag == True:
        lambdas_r_p_1 = lambdas[0:(r-p+1)]
        inv_lambdas_sum =  np.sum(1/lambdas_r_p_1)
        mu = ( Mt / (r - p + 1) ) * ( 1 + (1/SNR) * inv_lambdas_sum)
        for idx, item in enumerate(lambdas_r_p_1):
            gammas[idx] = mu - (Mt/(SNR*item))
        if gammas[r-p] < 0: #due to Python starts from 0
            gammas[r-p] = 0 #due to Python starts from 0
            p = p + 1
        else:
            flag = False
    res = []
    for gamma in gammas:
        res.append(float(gamma))
    return np.array(res)

def openloop_capacity(H_chan, SNR_dB):
    SNR = 10**(SNR_dB/10)
    Mt = np.shape(H_chan)[1]
    H_sq = np.dot(H_chan,np.matrix(H_chan, dtype=complex).H)
    lambdas = LA.eigvals(H_sq) 
    lambdas = np.sort(lambdas)[::-1]
    c = 0
    for eig in lambdas:
        c = c + np.log2(1 + SNR*eig/Mt)
    return np.real(c)

def closedloop_capacity(H_chan, SNR_dB):
    SNR = 10**(SNR_dB/10)
    Mt = np.shape(H_chan)[1]
    H_sq = np.dot(H_chan,np.matrix(H_chan, dtype=complex).H)
    lambdas = LA.eigvals(H_sq) 
    lambdas = np.real(np.sort(lambdas))[::-1]
    c = 0
    gammas = waterpouring(Mt, SNR_dB, H_chan)
    for idx, item in enumerate(lambdas):
        c = c + np.log2(1+ SNR*item*gammas[idx]/Mt)
    return np.real(c)

Mr = 4
Mt = 4
counter = 1000
SNR_dBs = [i for i in range(1, 21)]
C_open = np.empty((len(SNR_dBs), counter))
C_closed = np.empty((len(SNR_dBs), counter))

for c in range(counter):
    H_chan = (np.random.randn(Mr,Mt) + 1j*np.random.randn(Mr, Mt))/np.sqrt(2)
    for idx, SNR_dB in enumerate(SNR_dBs):
        C_open[idx, c] = openloop_capacity(H_chan, SNR_dB)
        C_closed[idx, c] = closedloop_capacity(H_chan, SNR_dB)
    
C_open_erg = np.mean(C_open, axis=1)
C_closed_erg = np.mean(C_closed, axis=1)

fig = plt.figure(figsize=(10, 5), dpi=300)
plt.plot(SNR_dBs, C_open_erg, label='Channel Unknown (CU)')
plt.plot(SNR_dBs, C_closed_erg, label='Channel Known (CK)')
plt.title("Ergodic Capacity")
plt.xlabel('SNR (dB)')
plt.ylabel('Capacity (bps/Hz)')
plt.legend()
plt.grid()
plt.savefig('MIMO_Capacity.png')

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Category:CC-BY-SA-4.0#MIMO%20Capacity.png
Category:Self-published work Category:Radio telecommunications Category:MIMO
Category:CC-BY-SA-4.0 Category:MIMO Category:PNG created with Matplotlib code Category:Radio telecommunications Category:Self-published work