#~ table2.append(json.loads(line))
#~ print len(table2)
table3 = []
with open(filename3, 'r') as f:
for line in f:
table3.append(json.loads(line))
#plt.subplot(2,1,1)
plt.semilogy(table1[7], [10**i for i in table1[8]],
'k-o',
label="Polar Code for p=" + str(p))
#plt.semilogy(table2[7],[10**i for i in table2[8]],'b',linestyle=":",label="Polar Code for p="+str(p1))
#plt.semilogy(table1[7],[10**i for i in table1[8]],'r-o',label="1Iter")
#plt.semilogy(table2[9],[10**i for i in table2[10]],'b-o',label="Rateless KRX")
(A_r, BER) = ml.sortAextend(table3[11], table3[12])
plt.semilogy(A_r, [10**i for i in BER],
'g-o',
label="Inc-Frz with PHY-ED$^{*}$")
plt.xlabel('Achieved Rate')
plt.ylabel('Frame Error rate.')
plt.title('FER vs Rate for SW compression \nN=1024,p=' + str(p) +
',Capacity=' + str(pl.CapacityBSC(1, p)))
plt.legend(loc="best")
plt.grid(True)
plt.figtext(0.005, 0.03, "$^{*}$Compound Channel=$\{$0.04,0.15,0.2,0.25$\}$"
) #+filename0+"\n"+filename3)
#~ plt.subplot(2,1,2)
def send_rateless_kRx(UN, N, channel_p, compound_plist_u, derate,
use_adjusted_Rate):
#print "In send rateless..."
#Reliability ordering
#print "R_order"
I_ord = pcon.getreliability_order(N)
#print I_ord
#compound channel
compound_plist = list(compound_plist_u) #best channel first
compound_plist.sort()
Ratelist = pl.getRatelist(compound_plist, derate) #best rate first
Glist = [int(N * r) for r in Ratelist]
#print "Compund Channel"
#print plist
#print Glist
#print "will be working with below to meet R R/2 R/3 R/4 constraint"
Glist = adjustG(Glist)
#print Glist
#given Channel might not be an entry in compound but within bounds
R = pl.getRatelist([channel_p],
derate)[0] #calculates rate for given channel
G = int(R * N)
#print "Actual channel"
#print channel_p
#print G
if use_adjusted_Rate:
G = Glist[compound_plist.index(channel_p)]
#----------------------------------------------------Iterations start
Iterhistory = {} #contains indexes of UN sent in each iteration
decoded = False
# for first Tx
Iter = 0
Iter_UN = UN
Iter_p = compound_plist[0]
Iter_R = Ratelist[0]
Iter_G = Glist[0]
Iter_I = I_ord[:Iter_G]
Iter_UN_ind = range(len(UN))
#Iter_data={}
#print float(len(Iter_I))/N
#print "Forward decoding"
while not decoded:
#print "Iter"+str(Iter)
Iter_UN = [UN[i] for i in Iter_UN_ind]
Iter_D = np.zeros(N - Iter_G, dtype=int).tolist() #frozen data
Iter_XN = ec.polarencodeG(Iter_UN, N, Iter_I, list(Iter_D),
False) #data goes in as per R.I
#--------------------Note channel_p used for flipping
Iter_YN = pl.BSCN(channel_p, Iter_XN)
#-----------------------decoding based on this tx only
Iter_UN_hat = ec.polarSCdecodeG(Iter_YN, N, Iter_p, Iter_I,
list(Iter_D), False)
Iter_UN_decoded = ec.getUN(Iter_UN_hat, Iter_I, False)
#storage needed for final decoding
Iterhistory[Iter] = [Iter_UN_ind, Iter_UN_decoded, Iter_YN]
#pprint(Iterhistory)
#For simulation of Rx knows channel case
#Assuming Rx knows the capacity of the channel
#hence as long as the rate used is above
#the capacity is declares Not decodable
#the rate at which decoding is possible and the rate achieved is same
#print Iterhistory
if not is_decodable_kRx(G, Iter_G):
# picking out all the channels that are suspected to be bad in past
# iterations and putting them for next iteration.
# Note first iteration Whole UN is sent
# in next only suspected bad channels are sent
prev_I = Iter_I
Iter += 1
#New channel params
Iter_p = compound_plist[Iter]
Iter_R = Ratelist[Iter]
Iter_G = Glist[Iter]
Iter_I = I_ord[:Iter_G]
tosend_ind = []
for i in range(Iter):
#picking out the bad channels from prev iterations
sent_ind = Iterhistory[i][0]
sent_ind_last_iter = sent_ind[:Glist[Iter - 1]]
bad_ind = sent_ind_last_iter[Iter_G:]
#print bad_ind
tosend_ind.extend(bad_ind)
Iter_UN_ind = list(set(tosend_ind))
Iter_UN_ind.sort()
#print Iter_UN_ind
else:
#the final reliable good channels is that of last iteration
final_Iter = Iter
final_G = Iter_G
#channel_p is already updated
final_p = Iter_p
final_I = I_ord[:final_G]
decoded = True
#pprint(Iterhistory)
#final decoding
#number retrodecoding needed = iter-1
#print "Final Decoding..."
#print final_p
#print final_G
#print Iter
for Iter in range(final_Iter - 1, -1, -1):
#print "Here"
#print "Iter"+str(Iter)+"-"*10
#print "Prev frozen"
Prev_correct_ind = Iterhistory[Iter + 1][0]
Prev_correct_data = Iterhistory[Iter + 1][1]
#print Prev_correct_ind
#print Prev_correct_data
#print "Inc frozen needed"
IncFreeze_ind_UN = [
i for i in Prev_correct_ind if i in Iterhistory[Iter][0]
]
#basically intersection with some order picking the indexes of the prev iter frozen data needed in this iter i.e, 12
#print IncFreeze_ind_UN
#picking the data i.e. u12
IncFreeze_ind_ind = [
Prev_correct_ind.index(j) for j in IncFreeze_ind_UN
]
IncFreeze_data = [Prev_correct_data[k] for k in IncFreeze_ind_ind]
#print IncFreeze_data
#finding the channels where 12 went in this iter 16 15 14 "13"<--- here
#i.e, removing the top channel_G channels (as they are good) from top Iter_i_G channels
Iter_G = Glist[Iter]
IncFreeze_ind = I_ord[:Iter_G][final_G:]
#print "Inc Frozen Data going in"
#print IncFreeze_ind
#print "Frozen zeroes"
Iter_D = np.zeros(N - Iter_G,
dtype=int).tolist() #frozen data as per iteration
#print len(Iter_D)
Iter_YN = Iterhistory[Iter][2]
Iter_UN_hat = ec.polarIncFrzSCdecodeG(Iter_YN, N, final_p, final_I,
list(Iter_D), IncFreeze_ind,
IncFreeze_data, False)
Iter_UN_decoded = ec.getUN(Iter_UN_hat, final_I, False)
#print Iter_UN_decoded
#history update
Iterhistory_ind_upd = list(Prev_correct_ind)
Iterhistory_ind_upd.extend(
Iterhistory[Iter][0][:final_G]) #ie adding 5,6,11 to 4,10,12
Iterhistory_data_upd = np.hstack(
(Prev_correct_data, Iter_UN_decoded)) #same as extend
#print "Update"
#print Iterhistory_ind_upd
#print Iterhistory_data_upd
#print "sorted"
(Iterhistory[Iter][0],
Iterhistory[Iter][1]) = ml.sortAextend(Iterhistory_ind_upd,
Iterhistory_data_upd.tolist())
#print Iterhistory[Iter][0]
#print Iterhistory[Iter][1]
#pprint(Iterhistory)
final_decoded = Iterhistory[0][1]
achieved_rate = float(len(UN)) / ((final_Iter + 1) * N)
return (achieved_rate, np.array(final_decoded))
def send_rateless_file_kRx(XN,N,channel_p,compound_plist_u,derate,use_adjusted_Rate):
I_ord=pcon.getreliability_order(N)
compound_plist=list(compound_plist_u) #best channel first
compound_plist.sort()
Ratelist = pl.getRatelist(compound_plist,derate) #best rate first
Glist=[int(N*r) for r in Ratelist]
#print "will be working with below to meet R R/2 R/3 R/4 constraint"
Glist=adjustG(Glist)
R=pl.getRatelist([channel_p],derate)[0] #calculates rate for given channel
G=int(R*N)
if use_adjusted_Rate:
G=Glist[compound_plist.index(channel_p)]
#----------------------------------------------------Iterations start
Iterhistory={} #contains indexes of UN sent in each iteration
decoded=False
#------------------for filing Tx side
# reverse arikan :: THIS IS OF SIZE N
UN_N=ec.polarencode(XN,N)
# for first Tx
Iter=0
Iter_p=compound_plist[0]
Iter_R=Ratelist[0]
Iter_G=Glist[0]
Iter_I=I_ord[:Iter_G]
UN=ec.getUN(UN_N,Iter_I,False)
#print Iter_I
#print UN
Iter_UN=UN
Iter_UN_ind=range(len(UN))
F=list(set(range(N))-set(Iter_I))
FD=ec.getUN(UN_N,F,True)
#-------------------------------------------Forward decoding
#in case of first iteration FD, XN is used
#from next iteration more bits from reverse Arikan UN is used
while not decoded:
if Iter==0:
Iter_XN=XN
else:
Iter_UN=[UN[i] for i in Iter_UN_ind]
Iter_D=np.zeros(N-Iter_G,dtype=int).tolist() #frozen data
Iter_XN=ec.polarencodeG(Iter_UN,N,Iter_I,list(Iter_D),False) #data goes in as per R.I
#--------------------Note channel_p used for flipping
Iter_YN=pl.BSCN(channel_p,Iter_XN)
#-----------------------decoding based on this tx only
if Iter==0:
Iter_UN_hat=ec.polarSCdecodeG(Iter_YN,N,Iter_p,Iter_I,list(FD),False)
else:
Iter_UN_hat=ec.polarSCdecodeG(Iter_YN,N,Iter_p,Iter_I,list(Iter_D),False)
Iter_UN_decoded=ec.getUN(Iter_UN_hat,Iter_I,False)
#storage needed for final decoding
Iterhistory[Iter]=[Iter_UN_ind,Iter_UN_decoded,Iter_YN]
#For simulation of Rx knows channel case
#Assuming Rx knows the capacity of the channel
#hence as long as the rate used is above
#the capacity is declares Not decodable
#the rate at which decoding is possible and the rate achieved is same
if not is_decodable_kRx(G,Iter_G):
#print "here"
# picking out all the channels that are suspected to be bad in past
# iterations and putting them for next iteration.
# Note first iteration Whole UN is sent
# in next only suspected bad channels are sent
prev_I=Iter_I
Iter+=1
#New channel params
Iter_p=compound_plist[Iter]
Iter_R=Ratelist[Iter]
Iter_G=Glist[Iter]
Iter_I=I_ord[:Iter_G]
tosend_ind=[]
for i in range(Iter):
#picking out the bad channels from prev iterations
sent_ind=Iterhistory[i][0]
sent_ind_last_iter=sent_ind[:Glist[Iter-1]]
bad_ind=sent_ind_last_iter[Iter_G:]
tosend_ind.extend(bad_ind)
Iter_UN_ind=list(set(tosend_ind))
Iter_UN_ind.sort()
else:
#the final reliable good channels is that of last iteration
final_Iter=Iter
final_G=Iter_G
final_p=Iter_p
final_I=I_ord[:final_G]
#~ if Iter=0:
#~ final_XN=ec.polarencode(Iter_UN_hat,N) #in case first iteration is last
decoded= True
#------------------------------------------------final decoding
#number retrodecoding needed = iter-1
#print Iter
for Iter in range(final_Iter-1,-1,-1):
#print "retro"+str(Iter)
Prev_correct_ind=Iterhistory[Iter+1][0]
Prev_correct_data=Iterhistory[Iter+1][1]
IncFreeze_ind_UN=[i for i in Prev_correct_ind if i in Iterhistory[Iter][0]]
#basically intersection with some order picking the indexes of the prev iter frozen data needed in this iter i.e, 12
#print IncFreeze_ind_UN
#picking the data i.e. u12
IncFreeze_ind_ind=[Prev_correct_ind.index(j) for j in IncFreeze_ind_UN]
IncFreeze_data=[Prev_correct_data[k] for k in IncFreeze_ind_ind]
#print IncFreeze_data
#finding the channels where 12 went in this iter 16 15 14 "13"<--- here
#i.e, removing the top channel_G channels (as they are good) from top Iter_i_G channels
Iter_G=Glist[Iter]
IncFreeze_ind=I_ord[:Iter_G][final_G:]
if Iter==0:
Iter_D=FD
else:
Iter_D=np.zeros(N-Iter_G,dtype=int).tolist() #frozen data as per iteration
Iter_YN=Iterhistory[Iter][2]
Iter_UN_hat=ec.polarIncFrzSCdecodeG(Iter_YN,N,final_p,final_I,list(Iter_D),IncFreeze_ind,IncFreeze_data,False)
Iter_UN_decoded=ec.getUN(Iter_UN_hat,final_I,False)
#history update
Iterhistory_ind_upd=list(Prev_correct_ind)
Iterhistory_ind_upd.extend(Iterhistory[Iter][0][:final_G]) #ie adding 5,6,11 to 4,10,12
Iterhistory_data_upd=np.hstack((Prev_correct_data,Iter_UN_decoded)) #same as extend
(Iterhistory[Iter][0],Iterhistory[Iter][1])=ml.sortAextend(Iterhistory_ind_upd,Iterhistory_data_upd.tolist())
final_decoded= Iterhistory[0][1]
final_XN=ec.polarencode(Iter_UN_hat,N)
achieved_rate=float(len(UN))/((final_Iter+1)*N)
return (achieved_rate,np.array(final_XN))