├── .gitignore
├── LDPC_576_432.alist
├── LDPC_576_432.gmat
├── LICENSE
├── README.md
├── generation_matrix.py
└── main.py
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
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688 | 116 121 145 176 203 233 253 288 306 322 359 377 540 564
689 | 117 122 146 177 204 234 254 265 307 323 360 378 541 565
690 | 118 123 147 178 205 235 255 266 308 324 337 379 542 566
691 | 119 124 148 179 206 236 256 267 309 325 338 380 543 567
692 | 120 125 149 180 207 237 257 268 310 326 339 381 544 568
693 | 97 126 150 181 208 238 258 269 311 327 340 382 545 569
694 | 98 127 151 182 209 239 259 270 312 328 341 383 546 570
695 | 99 128 152 183 210 240 260 271 289 329 342 384 547 571
696 | 100 129 153 184 211 217 261 272 290 330 343 361 548 572
697 | 101 130 154 185 212 218 262 273 291 331 344 362 549 573
698 | 102 131 155 186 213 219 263 274 292 332 345 363 550 574
699 | 103 132 156 187 214 220 264 275 293 333 346 364 551 575
700 | 104 133 157 188 215 221 241 276 294 334 347 365 552 576
701 | 40 56 95 102 194 227 255 269 306 326 391 415 445 553
702 | 41 57 96 103 195 228 256 270 307 327 392 416 446 554
703 | 42 58 73 104 196 229 257 271 308 328 393 417 447 555
704 | 43 59 74 105 197 230 258 272 309 329 394 418 448 556
705 | 44 60 75 106 198 231 259 273 310 330 395 419 449 557
706 | 45 61 76 107 199 232 260 274 311 331 396 420 450 558
707 | 46 62 77 108 200 233 261 275 312 332 397 421 451 559
708 | 47 63 78 109 201 234 262 276 289 333 398 422 452 560
709 | 48 64 79 110 202 235 263 277 290 334 399 423 453 561
710 | 25 65 80 111 203 236 264 278 291 335 400 424 454 562
711 | 26 66 81 112 204 237 241 279 292 336 401 425 455 563
712 | 27 67 82 113 205 238 242 280 293 313 402 426 456 564
713 | 28 68 83 114 206 239 243 281 294 314 403 427 433 565
714 | 29 69 84 115 207 240 244 282 295 315 404 428 434 566
715 | 30 70 85 116 208 217 245 283 296 316 405 429 435 567
716 | 31 71 86 117 209 218 246 284 297 317 406 430 436 568
717 | 32 72 87 118 210 219 247 285 298 318 407 431 437 569
718 | 33 49 88 119 211 220 248 286 299 319 408 432 438 570
719 | 34 50 89 120 212 221 249 287 300 320 385 409 439 571
720 | 35 51 90 97 213 222 250 288 301 321 386 410 440 572
721 | 36 52 91 98 214 223 251 265 302 322 387 411 441 573
722 | 37 53 92 99 215 224 252 266 303 323 388 412 442 574
723 | 38 54 93 100 216 225 253 267 304 324 389 413 443 575
724 | 39 55 94 101 193 226 254 268 305 325 390 414 444 576
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # A-Model-Driven-Deep-Learning-Method-for-Normalized-Min-Sum-LDPC-Decoding
2 |
3 | *(**Paper Link:** https://ieeexplore.ieee.org/abstract/document/9145237)*
4 |
5 | With the applications of deep learning networks booming in physical layer communication, deep-learning-based
6 | channel decoding methods have become a research hotspot. However, the high complexity hinders the application of deep neural network (DNN) on long code. In this paper, we propose a model-driven deep learning method for normalized min-sum (NMS) low-density parity-check (LDPC) decoding. First, we propose a neural normalized min-sum (NNMS) LDPC decoding network. By unfolding the iterative decoding progress between checking nodes (CNs) and variable nodes (VNs) into a feedforward propagation network, we can make use of the advantages of both model-driven deep learning methods and conventional normalized min-sum (CNMS) LDPC decoding method. Second, considering that the NNMS decoder needs large number of multipliers, we propose a shared neural normalized min-sum (SNNMS) decoding network to reduce the number of correction factors. Experimental results show that the BER performance of the proposed NNMS decoder is 1.5dB better than the conventional LDPC decoder, using fewer iterations. Furthermore, the proposed SNNMS decoder outperforms the proposed NNMS decoder and reduces the computation complexity.
7 |
8 |
9 | This repository contains the code for Nerual Normalized Min Sum Network(NNMS) and Shared Neural Normalized Min Sum (SNNMS).
10 | main.py: you can choose two kinds of neural decoder: NNMS and SNNMS.
11 | Generation_matrix.py: load LDPC_576_432.alist and LDPC_576_432.gamt to generate check matrix H and generator matrix G.
12 |
13 | *If you encounter problems during the use of the code, you can contact us through the following email address*
14 | - *wangq@tju.edu.cn*
15 | - *sf_wang@tju.edu.cn*
16 |
--------------------------------------------------------------------------------
/generation_matrix.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import tensorflow as tf
3 |
4 | class Code:
5 | def __init__(self):
6 | self.num_edges = 0
7 | # self.n = n
8 | # self.k = k
9 |
10 | def load_code(H_filename, G_filename):
11 | # parity-check matrix; Tanner graph parameters
12 | # H_filename = format('./LDPC_matrix/LDPC_576_432.alist')
13 | # G_filename = format('./LDPC_matrix/LDPC_576_432.gmat')
14 |
15 |
16 | with open(H_filename) as f:
17 | # get n and m (n-k) from first line
18 | n,m = [int(s) for s in f.readline().split(' ')]
19 | k = n-m
20 | #################################################################################################################
21 | var_degrees = np.zeros(n).astype(np.int) # degree of each variable node
22 | chk_degrees = np.zeros(m).astype(np.int) # degree of each check node
23 |
24 | # initialize H
25 | H = np.zeros([m,n]).astype(np.int)
26 | max_var_degree, max_chk_degree = [int(s) for s in f.readline().split(' ')]
27 | f.readline() # ignore two lines
28 | f.readline()
29 |
30 | # create H, sparse version of H, and edge index matrices
31 | # (edge index matrices used to calculate source and destination nodes during belief propagation)
32 | var_edges = [[] for _ in range(0,n)]
33 | for i in range(0,n):
34 | row_string = f.readline().split(' ')
35 | var_edges[i] = [(int(s)-1) for s in row_string[:-1]]
36 | var_degrees[i] = len(var_edges[i])
37 | H[var_edges[i], i] = 1
38 |
39 | chk_edges = [[] for _ in range(0,m)]
40 | for i in range(0,m):
41 | row_string = f.readline().split(' ')
42 | chk_edges[i] = [(int(s)-1) for s in row_string[:-1]]
43 | chk_degrees[i] = len(chk_edges[i])
44 |
45 | # H = np.loadtxt(H_filename).astype(np.int)
46 | # chk_degrees = np.sum(H, axis=1) # assume each check node has the sum degree
47 | # var_degrees = np.sum(H, axis=0)
48 | ################################################################################################################
49 | d = [[] for _ in range(0,n)]
50 | edge = 0
51 | for i in range(0,n):
52 | for j in range(0,var_degrees[i]):
53 | d[i].append(edge)
54 | edge += 1
55 |
56 | u = [[] for _ in range(0,m)]
57 | edge = 0
58 | for i in range(0,m):
59 | for j in range(0,chk_degrees[i]):
60 | v = chk_edges[i][j]
61 | for e in range(0,var_degrees[v]):
62 | if (i == var_edges[v][e]):
63 | u[i].append(d[v][e])
64 |
65 | num_edges = H.sum()
66 |
67 | if G_filename == "":
68 | G = []
69 | else:
70 | #if "BCH" in H_filename: # dear God please fix this
71 | if "LDPC" in H_filename: # dear God please fix this
72 | G = np.loadtxt(G_filename).astype(np.int)
73 | G = G.transpose()
74 | else:
75 | P = np.loadtxt(G_filename,skiprows=2)
76 | G = np.vstack([P.transpose(), np.eye(k)]).astype(np.int)
77 |
78 | code = Code()
79 | code.H = H
80 | code.G = G
81 | code.var_degrees = var_degrees
82 | code.chk_degrees = chk_degrees
83 | code.num_edges = num_edges
84 | code.u = u
85 | code.d = d
86 | code.n = n
87 | code.m = m
88 | code.k = k
89 | return code
90 |
91 |
92 |
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
1 | # Belief propagation using TensorFlow
2 | # Run as follows:
3 | # python main.py 0 0 5 1 100 10000000000000000 10 LDPC_576_432.alist LDPC_576_432.gmat laskdjhf 0/1 100 SNNMS/NNMS
4 | import numpy as np
5 | import tensorflow as tf
6 | import sys
7 | from tensorflow.python.framework import ops
8 | from generation_matrix import load_code
9 | import os
10 |
11 | DEBUG = False
12 | TRAINING = True
13 | SUM_PRODUCT = False
14 | MIN_SUM = not SUM_PRODUCT
15 | #ALL_ZEROS_CODEWORD_TRAINING = False
16 | ALL_ZEROS_CODEWORD_TRAINING = False
17 | ALL_ZEROS_CODEWORD_TESTING = False
18 |
19 |
20 | NO_SIGMA_SCALING_TRAIN = False
21 | NO_SIGMA_SCALING_TEST = False
22 | # NO_SIGMA_SCALING_TRAIN = True
23 | # NO_SIGMA_SCALING_TEST = True
24 | np.set_printoptions(precision=3)
25 |
26 | print("My piD: " + str(os.getpid()))
27 |
28 | if SUM_PRODUCT:
29 | print("Using Sum-Product algorithm")
30 | if MIN_SUM:
31 | print("Using Min-Sum algorithm")
32 |
33 | if ALL_ZEROS_CODEWORD_TRAINING:
34 | print("Training using only the all-zeros codeword")
35 | else:
36 | print("Training using random codewords (not the all-zeros codeword)")
37 |
38 | if ALL_ZEROS_CODEWORD_TESTING:
39 | print("Testing using only the all-zeros codeword")
40 | else:
41 | print("Testing using random codewords (not the all-zeros codeword)")
42 |
43 | if NO_SIGMA_SCALING_TRAIN:
44 | print("Not scaling train input by 2/sigma")
45 | else:
46 | print("Scaling train input by 2/sigma")
47 |
48 | if NO_SIGMA_SCALING_TEST:
49 | print("Not scaling test input by 2/sigma")
50 | else:
51 | print("Scaling test input by 2/sigma")
52 | # python main.py 0 0 5 1 100 10_000_000_000_000_000 5 LDPC_576_432.alist LDPC_576_432.gmat laskdjhf 1 100 SNNMS
53 | # 1 2 3 4 5 6 7 8 9 10 11 12 13
54 | seed = int(sys.argv[1])
55 | np.random.seed(seed)
56 | snr_lo = float(sys.argv[2])
57 | snr_hi = float(sys.argv[3])
58 | snr_step = float(sys.argv[4])
59 | min_frame_errors = int(sys.argv[5])
60 | max_frames = float(sys.argv[6])
61 | num_iterations = int(sys.argv[7])
62 | H_filename = sys.argv[8]
63 | G_filename = sys.argv[9]
64 | output_filename = sys.argv[10]
65 | L = float(sys.argv[11])
66 | steps = int(sys.argv[12])
67 | provided_decoder_type = sys.argv[13]
68 |
69 | if ALL_ZEROS_CODEWORD_TESTING: G_filename = ""
70 | code = load_code(H_filename, G_filename)
71 |
72 |
73 | H = code.H
74 | G = code.G
75 | var_degrees = code.var_degrees
76 | chk_degrees = code.chk_degrees
77 | num_edges = code.num_edges
78 | u = code.u
79 | d = code.d
80 | n = code.n
81 | m = code.m
82 | k = code.k
83 |
84 | class Decoder:
85 | def __init__(self, decoder_type="RNOMS", random_seed=0, learning_rate = 0.001, relaxed = False):
86 | self.decoder_type = decoder_type
87 | self.random_seed = random_seed
88 | self.learning_rate = learning_rate
89 | self.relaxed = relaxed
90 |
91 | # decoder parameters
92 | batch_size = 120#120
93 | tf_train_dataset = tf.placeholder(tf.float32, shape=(n,batch_size))
94 | tf_train_labels = tf.placeholder(tf.float32, shape=(n,batch_size))#tf.placeholder(tf.float32, shape=(num_iterations,n,batch_size))
95 |
96 | #### decoder functions ####
97 |
98 | # compute messages from variable nodes to check nodes
99 | def compute_vc(cv, iteration, soft_input):
100 | weighted_soft_input = soft_input
101 |
102 | edges = []
103 | for i in range(0, n):
104 | for j in range(0, var_degrees[i]):
105 | edges.append(i)
106 | reordered_soft_input = tf.gather(weighted_soft_input, edges)
107 |
108 | vc = []
109 | edge_order = []
110 | #if decoder.decoder_type == "NNMS":
111 | # normalizedsecond = tf.nn.softplus(decoder.B_vc[iteration])
112 | # cv = tf.multiply(cv, tf.tile(tf.reshape(normalizedsecond, [-1, 1]), [1, batch_size]))
113 | for i in range(0, n): # for each variable node v
114 | for j in range(0, var_degrees[i]):
115 | # edge = d[i][j]
116 | edge_order.append(d[i][j])
117 | extrinsic_edges = []
118 | for jj in range(0, var_degrees[i]):
119 | if jj != j: # extrinsic information only
120 | extrinsic_edges.append(d[i][jj])
121 | # if the list of edges is not empty, add them up
122 | if extrinsic_edges:
123 | temp = tf.gather(cv,extrinsic_edges)
124 | #if decoder.decoder_type == "SNNMS":
125 | # normalizedsecond = tf.nn.softplus(decoder.B_vc[iteration])
126 | # temp = tf.multiply(temp, tf.tile(tf.reshape(normalizedsecond, [-1, 1]), [1, batch_size]))
127 | temp = tf.reduce_sum(temp,0)
128 | else:
129 | temp = tf.zeros([batch_size])
130 | if SUM_PRODUCT: temp = tf.cast(temp, tf.float32)#tf.cast(temp, tf.float64)
131 | vc.append(temp)
132 |
133 | vc = tf.stack(vc)
134 | new_order = np.zeros(num_edges).astype(np.int)
135 | new_order[edge_order] = np.array(range(0,num_edges)).astype(np.int)
136 | vc = tf.gather(vc,new_order)
137 | vc = vc + reordered_soft_input
138 | return vc
139 |
140 | # compute messages from check nodes to variable nodes
141 | def compute_cv(vc, iteration):
142 | cv_list = []
143 | prod_list = []
144 | min_list = []
145 |
146 | if SUM_PRODUCT:
147 | vc = tf.clip_by_value(vc, -10, 10)
148 | tanh_vc = tf.tanh(vc / 2.0)
149 | edge_order = []
150 | for i in range(0, m): # for each check node c
151 | for j in range(0, chk_degrees[i]):
152 | # edge = u[i][j]
153 | edge_order.append(u[i][j])
154 | extrinsic_edges = []
155 | for jj in range(0, chk_degrees[i]):
156 | if jj != j:
157 | extrinsic_edges.append(u[i][jj])
158 | if SUM_PRODUCT:
159 | temp = tf.gather(tanh_vc,extrinsic_edges)
160 | temp = tf.reduce_prod(temp,0)
161 | temp = tf.log((1+temp)/(1-temp))
162 | cv_list.append(temp)
163 | if MIN_SUM:
164 | temp = tf.gather(vc,extrinsic_edges)
165 | temp1 = tf.reduce_prod(tf.sign(temp),0)
166 | temp2 = tf.reduce_min(tf.abs(temp),0)
167 | prod_list.append(temp1)
168 | min_list.append(temp2)
169 |
170 | if SUM_PRODUCT:
171 | cv = tf.stack(cv_list)
172 | if MIN_SUM:
173 | prods = tf.stack(prod_list)
174 | mins = tf.stack(min_list)
175 | if decoder.decoder_type == "SNNMS":
176 | # offsets = tf.nn.softplus(decoder.B_cv[iteration]) # normalized FNOMS
177 | # mins = tf.nn.relu(mins - tf.tile(tf.reshape(offsets,[-1,1]),[1,batch_size]))
178 | # normalized = tf.nn.softplus(decoder.B_cv[iteration]) # add normalized
179 | normalized = tf.nn.softplus(decoder.B_cv[iteration]) # add normalized softplus---> log(1+exp(x))
180 | mins = tf.multiply(mins, tf.tile(tf.reshape(normalized, [-1, 1]), [1, batch_size]))
181 | elif decoder.decoder_type == "NNMS":
182 | # offsets = tf.nn.softplus(decoder.B_cv[iteration])
183 | # mins = tf.nn.relu(mins - tf.tile(tf.reshape(offsets,[-1,1]),[1,batch_size]))
184 | # normalized = tf.nn.softplus(decoder.B_cv[iteration])
185 | normalized = tf.nn.softplus(decoder.B_cv[iteration])
186 | mins = tf.multiply(mins, tf.tile(tf.reshape(normalized, [-1, 1]), [1, batch_size]))
187 | cv = prods * mins
188 |
189 | new_order = np.zeros(num_edges).astype(np.int)
190 | new_order[edge_order] = np.array(range(0,num_edges)).astype(np.int)
191 | cv = tf.gather(cv,new_order)
192 |
193 | if decoder.decoder_type == "RNSPA" or decoder.decoder_type == "RNNMS":
194 | cv = cv * tf.tile(tf.reshape(decoder.W_cv,[-1,1]),[1,batch_size])
195 | elif decoder.decoder_type == "FNSPA" or decoder.decoder_type == "FNNMS":
196 | cv = cv * tf.tile(tf.reshape(decoder.W_cv[iteration],[-1,1]),[1,batch_size])
197 | return cv
198 |
199 | # combine messages to get posterior LLRs
200 |
201 |
202 | def marginalize(soft_input, iteration, cv):
203 | weighted_soft_input = soft_input
204 |
205 | soft_output = []
206 | for i in range(0,n):
207 | edges = []
208 | for e in range(0,var_degrees[i]):
209 | edges.append(d[i][e])
210 |
211 | temp = tf.gather(cv,edges)
212 | temp = tf.reduce_sum(temp,0)
213 | soft_output.append(temp)
214 |
215 | soft_output = tf.stack(soft_output)
216 |
217 | soft_output = weighted_soft_input + soft_output
218 | return soft_output
219 |
220 | def continue_condition(soft_input, soft_output, iteration, cv, m_t, loss, labels):
221 | condition = (iteration < num_iterations)
222 | return condition
223 |
224 | def belief_propagation_iteration(soft_input, soft_output, iteration, cv, m_t, loss, labels):
225 | # compute vc
226 | vc = compute_vc(cv,iteration,soft_input)
227 |
228 | # filter vc
229 | if decoder.relaxed:
230 | m_t = R * m_t + (1-R) * vc
231 | vc_prime = m_t
232 | else:
233 | vc_prime = vc
234 |
235 | # compute cv
236 | cv = compute_cv(vc_prime,iteration)
237 |
238 | # get output for this iteration
239 | soft_output = marginalize(soft_input, iteration, cv)
240 | iteration += 1
241 |
242 | # L = 0.5
243 | print("L = " + str(L))
244 | CE_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=-soft_output, labels=labels)) / num_iterations
245 | MSE_loss = tf.reduce_mean(tf.square(soft_output - labels)) / num_iterations
246 | new_loss = L * CE_loss + (1 - L) * MSE_loss
247 | loss = loss + new_loss
248 |
249 | return soft_input, soft_output, iteration, cv, m_t, loss, labels
250 |
251 | # builds a belief propagation TF graph
252 | def belief_propagation_op(soft_input, labels):
253 | return tf.while_loop(
254 | continue_condition, # iteration < max iteration?
255 | belief_propagation_iteration, # compute messages for this iteration
256 | [
257 | soft_input, # soft input for this iteration
258 | soft_input, # soft output for this iteration
259 | tf.constant(0,dtype=tf.int32), # iteration number
260 | tf.zeros([num_edges,batch_size],dtype=tf.float32), # cv
261 | tf.zeros([num_edges,batch_size],dtype=tf.float32), # m_t
262 | tf.constant(0.0,dtype=tf.float32), # loss
263 | labels
264 | ]
265 | )
266 |
267 | #### end decoder functions ####
268 | global_step = tf.Variable(0, trainable=False)
269 | starter_learning_rate = 0.001
270 | learning_rate = starter_learning_rate # provided_decoder_type="normal", "FNNMS", "SNNMS", ...
271 | decoder = Decoder(decoder_type=provided_decoder_type, random_seed=1, learning_rate = learning_rate, relaxed = False)
272 | print("\n\nDecoder type: " + decoder.decoder_type + "\n\n")
273 | if decoder.relaxed: print("relaxed")
274 | else: print("not relaxed")
275 |
276 | if SUM_PRODUCT:
277 | if decoder.decoder_type == "FNSPA":
278 | decoder.W_cv = tf.Variable(tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0, seed=decoder.random_seed))
279 |
280 | if decoder.decoder_type == "RNSPA":
281 | decoder.W_cv = tf.Variable(tf.truncated_normal([num_edges],dtype=tf.float32,stddev=1.0, seed=decoder.random_seed))#tf.Variable(0.0,dtype=tf.float32)#
282 |
283 | if MIN_SUM:
284 |
285 | if decoder.decoder_type == "SNNMS":
286 | # SNNMS
287 | # decoder.B_cv = tf.Variable(tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0/num_edges))
288 | # decoder.B_vc = tf.Variable(tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0/num_edges))
289 | decoder.B_cv = tf.Variable(tf.truncated_normal([num_iterations],dtype=tf.float32,stddev=1.0))
290 | decoder.B_vc = tf.Variable(tf.truncated_normal([num_iterations], dtype=tf.float32, stddev=1.0))
291 |
292 | if decoder.decoder_type == "NNMS":
293 | # NNMS
294 | decoder.B_cv = tf.Variable(tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0/num_edges))
295 | decoder.B_vc = tf.Variable(tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0))#tf.Variable(1.0 + tf.truncated_normal([num_iterations, num_edges],dtype=tf.float32,stddev=1.0/num_edges))
296 | #decoder.B_cv = tf.Variable(tf.truncated_normal([num_iterations],dtype=tf.float32,stddev=1.0))
297 | #decoder.B_vc = tf.Variable(tf.truncated_normal([num_iterations], dtype=tf.float32, stddev=1.0))
298 |
299 | if decoder.relaxed:
300 | decoder.relaxation_factors = tf.Variable(0.0,dtype=tf.float32)
301 | R = tf.sigmoid(decoder.relaxation_factors)
302 | # print "single learned relaxation factor"
303 |
304 | # decoder.relaxation_factors = tf.Variable(tf.truncated_normal([num_edges],dtype=tf.float32,stddev=1.0))
305 | # R = tf.tile(tf.reshape(tf.sigmoid(decoder.relaxation_factors),[-1,1]),[1,batch_size])
306 | # print "multiple relaxation factors"
307 |
308 | #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
309 | config = tf.ConfigProto(
310 | device_count = {'CPU': 2,'GPU': 0}
311 | )
312 | with tf.Session(config=config) as session: #tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as session:
313 | # simulate each SNR
314 | SNRs = np.arange(snr_lo, snr_hi+snr_step, snr_step)
315 | if (batch_size % len(SNRs)) != 0:
316 | print("********************")
317 | print("********************")
318 | print("error: batch size must divide by the number of SNRs to train on")
319 | print("********************")
320 | print("********************")
321 | BERs = []
322 | SERs = []
323 | FERs = []
324 |
325 | print("\nBuilding the decoder graph...")
326 | belief_propagation = belief_propagation_op(soft_input=tf_train_dataset, labels=tf_train_labels)
327 | if TRAINING:
328 | training_loss = belief_propagation[5]#tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=belief_propagation[1], labels=tf_train_labels))
329 | loss = training_loss
330 | print("Learning rate: " + str(starter_learning_rate))
331 | optimizer = tf.train.AdamOptimizer(learning_rate=decoder.learning_rate).minimize(loss,global_step=global_step)
332 | print("Done.\n")
333 | init = tf.global_variables_initializer()
334 |
335 | if ALL_ZEROS_CODEWORD_TRAINING:
336 | codewords = np.zeros([n,batch_size])
337 | codewords_repeated = np.zeros([num_iterations,n,batch_size]) # repeat for each iteration (multiloss)
338 | BPSK_codewords = np.ones([n,batch_size])
339 | soft_input = np.zeros_like(BPSK_codewords)
340 | channel_information = np.zeros_like(BPSK_codewords)
341 |
342 | covariance_matrix = np.eye(n)
343 | eta = 0.99
344 | for i in range(0,n):
345 | for j in range(0,n):
346 | covariance_matrix[i,j] = eta**np.abs(i-j)
347 |
348 | session.run(init)
349 |
350 | if TRAINING:
351 | # steps = 10001
352 | print("***********************")
353 | print("Training decoder using " + str(steps) + " minibatches...")
354 | print("***********************")
355 |
356 | step = 0
357 | while step < steps:
358 | # generate random codewords
359 | if not ALL_ZEROS_CODEWORD_TRAINING:
360 | # generate message
361 | messages = np.random.randint(0,2,[k,batch_size])
362 |
363 | # encode message
364 | codewords = np.dot(G, messages) % 2
365 | #codewords_repeated = np.tile(x,(num_iterations,1,1)).shape
366 |
367 | # modulate codeword
368 | BPSK_codewords = (0.5 - codewords.astype(np.float32)) * 2.0
369 |
370 | soft_input = np.zeros_like(BPSK_codewords)
371 | channel_information = np.zeros_like(BPSK_codewords)
372 | else:
373 | codewords = np.zeros([n,batch_size])
374 | #codewords_repeated = np.zeros([num_iterations,n,batch_size]) # repeat for each iteration (multiloss)
375 | BPSK_codewords = np.ones([n,batch_size])
376 | soft_input = np.zeros_like(BPSK_codewords)
377 | channel_information = np.zeros_like(BPSK_codewords)
378 |
379 | # create minibatch with codewords from multiple SNRs
380 |
381 | for i in range(0,len(SNRs)):
382 |
383 |
384 |
385 | sigma = np.sqrt(1. / (2 * (np.float(k)/np.float(n)) * 10**(SNRs[i]/10)))
386 | noise = sigma * np.random.randn(n,batch_size//len(SNRs))
387 | # noise = sigma * np.random.randn(n, batch_size)
388 | start_idx = batch_size*i//len(SNRs)
389 | end_idx = batch_size*(i+1)//len(SNRs)
390 | channel_information[:,start_idx:end_idx] = BPSK_codewords[:, start_idx:end_idx] + noise
391 | # Whether to use channel estimation as initialization of input
392 | if NO_SIGMA_SCALING_TRAIN:
393 | soft_input[:,start_idx:end_idx] = channel_information[:, start_idx:end_idx]
394 | else:
395 | soft_input[:,start_idx:end_idx] = 2.0*channel_information[:, start_idx:end_idx]/(sigma*sigma)
396 |
397 |
398 | # feed minibatch into BP and run SGD
399 | batch_data = soft_input
400 | batch_labels = codewords #codewords #codewords_repeated
401 | feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}
402 | [_] = session.run([optimizer], feed_dict=feed_dict) #,bp_output,syndrome_output,belief_propagation, soft_syndromes
403 |
404 | if decoder.relaxed and TRAINING:
405 | print(session.run(R))
406 |
407 | if step % 10 == 0:
408 | print(str(step) + " minibatches completed")
409 |
410 | step += 1
411 |
412 | print("Trained decoder on " + str(step) + " minibatches.\n")
413 |
414 | # testing phase
415 | print("***********************")
416 | print("Testing decoder...")
417 | print("***********************")
418 | for SNR in SNRs:
419 | # simulate this SNR
420 | sigma = np.sqrt(1. / (2 * (np.float(k)/np.float(n)) * 10**(SNR/10)))
421 | frame_count = 0
422 | bit_errors = 0
423 | frame_errors = 0
424 | frame_errors_with_HDD = 0
425 | symbol_errors = 0
426 | FE = 0
427 |
428 | # simulate frames
429 | while ((FE < min_frame_errors) or (frame_count < 100000)) and (frame_count < max_frames):
430 | frame_count += batch_size # use different batch size for test phase?
431 |
432 | if not ALL_ZEROS_CODEWORD_TESTING:
433 | # generate message
434 | messages = np.random.randint(0,2,[batch_size,k])
435 |
436 | # encode message
437 | codewords = np.dot(G, messages.transpose()) % 2
438 |
439 | # modulate codeword
440 | BPSK_codewords = (0.5 - codewords.astype(np.float32)) * 2.0
441 |
442 | # add Gaussian noise to codeword
443 | noise = sigma * np.random.randn(BPSK_codewords.shape[0],BPSK_codewords.shape[1])
444 |
445 | channel_information = BPSK_codewords + noise
446 |
447 |
448 | # convert channel information to LLR format
449 | if NO_SIGMA_SCALING_TEST:
450 | soft_input = channel_information
451 | else:
452 | soft_input = 2.0*channel_information/(sigma*sigma)
453 |
454 | # run belief propagation
455 | batch_data = soft_input
456 | feed_dict = {tf_train_dataset : batch_data, tf_train_labels : codewords}
457 | soft_outputs = session.run([belief_propagation], feed_dict=feed_dict)
458 | soft_output = np.array(soft_outputs[0][1])
459 | recovered_codewords = (soft_output < 0).astype(int)
460 |
461 | # update bit error count and frame error count
462 | errors = codewords != recovered_codewords
463 | bit_errors += errors.sum()
464 | frame_errors += (errors.sum(0) > 0).sum()
465 |
466 | FE = frame_errors
467 |
468 | # summarize this SNR:
469 | print("SNR: " + str(SNR))
470 | print("frame count: " + str(frame_count))
471 |
472 | bit_count = frame_count * n
473 | BER = np.float(bit_errors) / np.float(bit_count)
474 | BERs.append(BER)
475 | print("bit errors: " + str(bit_errors))
476 | print("BER: " + str(BER))
477 |
478 | FER = np.float(frame_errors) / np.float(frame_count)
479 | FERs.append(FER)
480 | print("FER: " + str(FER))
481 | print("")
482 |
483 | # print summary
484 | print("BERs:")
485 | print(BERs)
486 | print("FERs:")
487 | print(FERs)
488 |
489 |
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