Module DigiCommPy.scripts.chapter_4.awgnPerformance

"""
Script: DigiCommPy.chapter_4.awgnPerformance.py
Eb/N0 Vs SER for PSK/QAM/PAM/FSK over AWGN (complex baseband model)

@author: Mathuranathan Viswanathan
Created on Aug 8, 2019
"""
import numpy as np # for numerical computing
import matplotlib.pyplot as plt # for plotting functions
from matplotlib import cm # colormap for color palette
from scipy.special import erfc
from modem import PSKModem,QAMModem,PAMModem,FSKModem
from channels import awgn
from errorRates import ser_awgn

#---------Input Fields------------------------
nSym = 10**6 # Number of symbols to transmit
EbN0dBs = np.arange(start=-4,stop = 12, step = 2) # Eb/N0 range in dB for simulation
mod_type = 'FSK' # Set 'PSK' or 'QAM' or 'PAM' or 'FSK'
arrayOfM = [2,4,8,16,32] # array of M values to simulate
#arrayOfM=[4,16,64,256] # uncomment this line if MOD_TYPE='QAM'
coherence = 'coherent' #'coherent'/'noncoherent'-only for FSK

modem_dict = {'psk': PSKModem,'qam':QAMModem,'pam':PAMModem,'fsk':FSKModem}
colors = plt.cm.jet(np.linspace(0,1,len(arrayOfM))) # colormap
fig, ax = plt.subplots(nrows=1,ncols = 1)

for i, M in enumerate(arrayOfM):
    #-----Initialization of various parameters----
    k=np.log2(M)
    EsN0dBs = 10*np.log10(k)+EbN0dBs # EsN0dB calculation
    SER_sim = np.zeros(len(EbN0dBs)) # simulated Symbol error rates
    inputSyms = np.random.randint(low=0, high = M, size=nSym)
    # uniform random symbols from 0 to M-1
    
    if mod_type.lower()=='fsk':
        modem=modem_dict[mod_type.lower()](M,coherence)#choose modem from dictionary
    else: #for all other modulations
        modem = modem_dict[mod_type.lower()](M)#choose modem from dictionary
    modulatedSyms = modem.modulate(inputSyms) #modulate
    
    for j,EsN0dB in enumerate(EsN0dBs):
        receivedSyms = awgn(modulatedSyms,EsN0dB) #add awgn noise
        
        if mod_type.lower()=='fsk': #demodulate (Refer Chapter 3)
            detectedSyms = modem.demodulate(receivedSyms,coherence)
        else: #demodulate (Refer Chapter 3)
            detectedSyms = modem.demodulate(receivedSyms)
            
        SER_sim[j] = np.sum(detectedSyms != inputSyms)/nSym
    
    SER_theory = ser_awgn(EbN0dBs,mod_type,M,coherence) #theory SER
    ax.semilogy(EbN0dBs,SER_sim,color = colors[i],marker='o',linestyle='',label='Sim '+str(M)+'-'+mod_type.upper())
    ax.semilogy(EbN0dBs,SER_theory,color = colors[i],linestyle='-',label='Theory '+str(M)+'-'+mod_type.upper())

ax.set_xlabel('Eb/N0(dB)');ax.set_ylabel('SER ($P_s$)')
ax.set_title('Probability of Symbol Error for M-'+str(mod_type)+' over AWGN')
ax.legend();fig.show()