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DetNet_HD_QPSK.py
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#!/usr/bin/env python
"""
This file is used to train and test the DetNet architecture in the hard decision output scenario.
The constellation used is QPSK and the channel is complex
all parameters were optimized and trained over the 20X30 iid channel, changing the channel might require parameter tuning
Notice that the run time analysis presented in the paper was made on a numpy version of the tensorflow network.
writen by Neev Samuel based on the paper:
"Learning to detect, Neev Samuel,Tzvi Diskin,Ami Wiesel"
contact by neev.samuel@gmail.com
"""
import tensorflow as tf
import numpy as np
import time as tm
import math
import sys
import pickle as pkl
"""
Parameters
K - size of x
N - size of y
snrdb_low - the lower bound of noise db used during training
snr_high - the higher bound of noise db used during training
L - number of layers in DetNet
v_size = size of auxiliary variable at each layer
hl_size - size of hidden layer at each DetNet layer (the dimention the layers input are increased to
startingLearningRate - the initial step size of the gradient descent algorithm
decay_factor & decay_step_size - each decay_step_size steps the learning rate decay by decay_factor
train_iter - number of train iterations
train_batch_size - batch size during training phase
test_iter - number of test iterations
test_batch_size - batch size during testing phase
LOG_LOSS - equal 1 if loss of each layer should be sumed in proportion to the layer depth, otherwise all losses have the same weight
res_alpha- the proportion of the previuos layer output to be added to the current layers output (view ResNet article)
snrdb_low_test & snrdb_high_test & num_snr - when testing, num_snr different SNR values will be tested, uniformly spread between snrdb_low_test and snrdb_high_test
"""
###start here
sess = tf.InteractiveSession()
K = 20
N = 30
snrdb_low = 7.0
snrdb_high = 14.0
snr_low = 10.0 ** (snrdb_low/10.0)
snr_high = 10.0 ** (snrdb_high/10.0)
n0=np.expand_dims(0.5,1)
res_alpha=0.9
L=30
v_size = 1*(2*K)
hl_size = 4*(2*K)
test_iter= 200
test_batch_size=1000
train_iter = 200000
train_batch_iter = 3000 # train batch size
num_snr = 6
snrdb_low_test=8.0
snrdb_high_test=13.0
snrdb_list = np.linspace(snrdb_low_test,snrdb_high_test,num_snr)
snr_list = 10.0 ** (snrdb_list/10.0)
startingLearningRate = 0.0003
decay_factor = 0.97
decay_step = 1000
print('DetNet QPSK')
print(K)
print(N)
print(snrdb_low)
print(snrdb_high)
print(snr_low)
print(snr_high)
print(L)
print(v_size)
print(hl_size)
print(startingLearningRate)
print(decay_factor)
print(decay_step)
print(train_iter)
print(train_batch_iter)
print(test_iter)
print(test_batch_size)
print(res_alpha)
print(num_snr)
print(snrdb_low_test)
print(snrdb_high_test)
"""Data generation for train and test phases
In this example, both functions are the same.
This duplication is in order to easily allow testing cases where the test is over different distributions of data than in the training phase.
e.g. training over gaussian i.i.d. channels and testing over a specific constant channel.
currently both test and train are over i.i.d gaussian channel.
"""
def generate_data_iid_test(B,K,N,snr_low,snr_high):
H_R = np.random.randn(B,N,K)
H_I = np.random.randn(B,N,K)
H_ = np.zeros([B,2*N,2*K])
x_R = np.sign(np.random.rand(B,K) - 0.5)
x_I = np.sign(np.random.rand(B,K) - 0.5)
x_ = np.concatenate((x_R , x_I) , axis = 1)
y_ = np.zeros([B,2*N])
w_R = np.random.randn(B,N)
w_I = np.random.randn(B,N)
w = np.concatenate((w_R , w_I) , axis = 1)
Hy_ = np.zeros([B,2*K])
HH_ = np.zeros([B,2*K,2*K])
SNR_ = np.zeros([B])
x_ind = np.zeros([B,K,4])
for i in range(B):
for ii in range(K):
if x_R[i,ii]==-1 and x_I[i,ii] == -1:
x_ind[i,ii,0] = 1
if x_R[i,ii]==-1 and x_I[i,ii] == 1:
x_ind[i,ii,1] = 1
if x_R[i,ii]==1 and x_I[i,ii] == -1:
x_ind[i,ii,2] = 1
if x_R[i,ii]==1 and x_I[i,ii] == 1:
x_ind[i,ii,3] = 1
for i in range(B):
SNR = np.random.uniform(low=snr_low,high=snr_high)
H = np.concatenate((np.concatenate((H_R[i,:,:], -1*H_I[i,:,:]), axis=1) , np.concatenate((H_I[i,:,:] , H_R[i,:,:]), axis=1) ), axis=0)
tmp_snr=(H.T.dot(H)).trace()/(2*K)
H_[i,:,:]=H
y_[i,:]=(H.dot(x_[i,:])+w[i,:]*np.sqrt(tmp_snr)/np.sqrt(SNR))
Hy_[i,:]=H.T.dot(y_[i,:])
HH_[i,:,:]=H.T.dot( H_[i,:,:])
SNR_[i] = SNR
return y_,H_,Hy_,HH_,x_,SNR_, H_R, H_I, x_R, x_I, w_R, w_I, x_ind
def generate_data_train(B,K,N,snr_low,snr_high):
H_R = np.random.randn(B,N,K)
H_I = np.random.randn(B,N,K)
H_ = np.zeros([B,2*N,2*K])
x_R = np.sign(np.random.rand(B,K) - 0.5)
x_I = np.sign(np.random.rand(B,K) - 0.5)
x_ = np.concatenate((x_R , x_I) , axis = 1)
y_ = np.zeros([B,2*N])
w_R = np.random.randn(B,N)
w_I = np.random.randn(B,N)
w = np.concatenate((w_R , w_I) , axis = 1)
Hy_ = np.zeros([B,2*K])
HH_ = np.zeros([B,2*K,2*K])
SNR_ = np.zeros([B])
x_ind = np.zeros([B,K,4])
for i in range(B):
for ii in range(K):
if x_R[i,ii]==-1 and x_I[i,ii] == -1:
x_ind[i,ii,0] = 1
if x_R[i,ii]==-1 and x_I[i,ii] == 1:
x_ind[i,ii,1] = 1
if x_R[i,ii]==1 and x_I[i,ii] == -1:
x_ind[i,ii,2] = 1
if x_R[i,ii]==1 and x_I[i,ii] == 1:
x_ind[i,ii,3] = 1
for i in range(B):
SNR = np.random.uniform(low=snr_low,high=snr_high)
H = np.concatenate((np.concatenate((H_R[i,:,:], -1*H_I[i,:,:]), axis=1) , np.concatenate((H_I[i,:,:] , H_R[i,:,:]), axis=1) ), axis=0)
tmp_snr=(H.T.dot(H)).trace()/(2*K)
H_[i,:,:]=H
y_[i,:]=(H.dot(x_[i,:])+w[i,:]*np.sqrt(tmp_snr)/np.sqrt(SNR))
Hy_[i,:]=H.T.dot(y_[i,:])
HH_[i,:,:]=H.T.dot( H_[i,:,:])
SNR_[i] = SNR
return y_,H_,Hy_,HH_,x_,SNR_, H_R, H_I, x_R, x_I, w_R, w_I,x_ind
def piecewise_linear_soft_sign(x):
t = tf.Variable(0.1)
y = -1+tf.nn.relu(x+t)/(tf.abs(t)+0.00001)-tf.nn.relu(x-t)/(tf.abs(t)+0.00001)
return y
def affine_layer(x,input_size,output_size,Layer_num):
W = tf.Variable(tf.random_normal([input_size, output_size], stddev=0.01))
w = tf.Variable(tf.random_normal([1, output_size], stddev=0.01))
y = tf.matmul(x, W)+w
return y
def relu_layer(x,input_size,output_size,Layer_num):
y = tf.nn.relu(affine_layer(x,input_size,output_size,Layer_num))
return y
def sign_layer(x,input_size,output_size,Layer_num):
y = affine_layer(x,input_size,output_size,Layer_num)
return y
HY = tf.placeholder(tf.float32,shape=[None,2*K])
X = tf.placeholder(tf.float32,shape=[None,2*K])
HH = tf.placeholder(tf.float32,shape=[None, 2*K , 2*K])
X_IND = tf.placeholder(tf.float32,shape=[None, K , 4])
batch_size = tf.shape(HY)[0]
x_real = X[:,0:K]
x_imag = X[:,K:2*K]
S1=[]
S1.append(tf.zeros([batch_size,2*K]))
S2=[]
S2.append(tf.zeros([batch_size,4*K]))
V=[]
V.append(tf.zeros([batch_size,v_size]))
LOSS=[]
LOSS.append(tf.zeros([]))
BER=[]
BER.append(tf.zeros([]))
delta = tf.Variable(tf.zeros(L*2,1))
for i in range(1,L):
temp1 = tf.matmul(tf.expand_dims(S1[-1],1),HH)
temp1= tf.squeeze(temp1,1)
Z1 = S1[-1] - delta[(i-1) * 2]*HY + delta[(i-1) * 2 + 1]*temp1
Z = tf.concat([Z1, V[-1]], 1)
ZZ = relu_layer(Z,(2*K) + v_size , hl_size,'relu'+str(i))
S2.append(sign_layer(ZZ , hl_size , 4*K,'sign'+str(i)))
S2[i]=(1-res_alpha)*S2[i]+res_alpha*S2[i-1]
S2[i] = tf.clip_by_value(S2[i],0,1)
V.append(affine_layer(ZZ , hl_size , v_size,'aff'+str(i)))
V[i]=(1-res_alpha)*V[i]+res_alpha*V[i-1]
S3 = tf.reshape(S2[i],[batch_size,K,4])
temp_0 = S3[:,:,0]
temp_1 = S3[:,:,1]
temp_2 = S3[:,:,2]
temp_3 = S3[:,:,3]
S1_real = -1.0*temp_0 +\
-1.0*temp_1 +\
1.0*temp_2 +\
1.0*temp_3
S1_im = -1.0*temp_0 +\
1.0*temp_1 +\
-1.0*temp_2 +\
1.0*temp_3
S1.append(tf.concat([S1_real, S1_im], 1))
x_ind_reshaped = tf.reshape(X_IND,[batch_size,4*K])
LOSS.append(np.log(i)*tf.reduce_mean(tf.reduce_mean(tf.square(x_ind_reshaped - S2[-1]),1)))
BER.append(tf.reduce_mean(tf.cast(tf.logical_or(tf.not_equal(x_real,tf.sign(S1[-1][:,0:K])),tf.not_equal(x_imag,tf.sign(S1[-1][:,K:2*K]))), tf.float32)))
Max_Val = tf.reduce_max(S3,axis=2, keep_dims=True)
Greater = tf.greater_equal(S3,Max_Val)
BER2 = tf.round(tf.cast(Greater,tf.float32))
BER3 = tf.not_equal(BER2, X_IND)
BER4 = tf.reduce_sum(tf.cast(BER3,tf.float32),2)
BER5 = tf.cast(tf.greater(BER4,0),tf.float32)
SER = tf.reduce_mean(BER5)
TOTAL_LOSS=tf.add_n(LOSS)
saver = tf.train.Saver()
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(startingLearningRate, global_step, decay_step, decay_factor, staircase=True)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(TOTAL_LOSS)
init_op=tf.global_variables_initializer()
train_flag = True
if train_flag:
sess.run(init_op)
for i in range(train_iter): #num of train iter
batch_Y, batch_H, batch_HY, batch_HH, batch_X , SNR1 , H_R, H_I, x_R, x_I, w_R, w_I,x_ind= generate_data_train(train_batch_iter,K,N,snr_low,snr_high)
train_step.run(feed_dict={HY: batch_HY, HH: batch_HH, X: batch_X,X_IND:x_ind})
if i % 1000== 0 :
TOTAL_LOSS.eval(feed_dict={
HY: batch_HY, HH: batch_HH, X: batch_X,X_IND:x_ind}
)
batch_Y, batch_H, batch_HY, batch_HH, batch_X ,SNR1, H_R, H_I, x_R, x_I, w_R, w_I,x_ind= generate_data_iid_test(train_batch_iter,K,N,snr_low,snr_high)
results = sess.run([LOSS[L-1],BER[L-1]], {HY: batch_HY, HH: batch_HH, X: batch_X,X_IND:x_ind})
print_string = [i]+results
print ' '.join('%s' % x for x in print_string)
sys.stderr.write(str(i)+' ')
#saver.restore(sess, "./DetNet_HD_QPSK/QPSK_HD_model.ckpt")
bers = np.zeros((1,num_snr))
times = np.zeros((1,num_snr))
tmp_bers = np.zeros((1,test_iter))
tmp_times = np.zeros((1,test_iter))
tmp_ber_iter=np.zeros([L,test_iter])
ber_iter=np.zeros([L,num_snr])
for j in range(num_snr):
for jj in range(test_iter):
print('snr_num:')
print(j)
print(jj)
batch_Y, batch_H, batch_HY, batch_HH, batch_X ,SNR1, H_R, H_I, x_R, x_I, w_R, w_I,x_ind= generate_data_iid_test(test_batch_size,K,N,snr_list[j],snr_list[j])
tic = tm.time()
tmp_bers[0,jj] = np.array(sess.run(BER[L-1], {HY: batch_HY, HH: batch_HH, X: batch_X,X_IND:x_ind}))#/(test_batch_size*K)
toc = tm.time()
tmp_times[0][jj] =toc - tic
bers[0][j] = np.mean(tmp_bers[0])
times[0][j] = np.mean(tmp_times[0])/test_batch_size
ber_iter[:,j]=np.mean(tmp_ber_iter,1)
print('snrdb_list')
print(snrdb_list)
print('bers')
print(bers)
print('times')
print(times)
save_path = saver.save(sess, "./DetNet_HD_QPSK/QPSK_HD_model.ckpt")