belief-propagation-ldpc 1.0.1

Belief propagation on Tanner graphs. Implements an LLR based LDPC decoder.

Belief Propagation

This repo is aimed at providing an example implementation of belief propagation on Tanner graphs as used by a standard LDPC decoder.

You can also view my associated Medium post.

If you see bug, wish suggest something or contribute please open an issue. Alternatively you can also contact me via email.

For more information on LDPC codes and belief propagation on Tanner graphs visit my GitHub pages site: https://yairmz.github.io/LDPC/.

Installation

pip install belief-propagation-ldpc

---

API

Nodes

One can create nodes (either variable or check nodes) via:

from belief_propagation import VNode, CNode, bsc_llr
v = VNode(name="v0", channel_model=bsc_llr(0.1))
c = CNode(name="c0")

Graphs

Creating a graph:

from belief_propagation import TannerGraph, bsc_llr
import numpy as np
tg = TannerGraph()
# add 10 variable nodes
for i in range(10):
    tg.add_v_node(name="v"+str(i), channel_model=bsc_llr(0.1), ordering_key=i)
# add 5 check nodes
for j in range(5):
    tg.add_c_node(name="c"+str(j), ordering_key=j)
edges = {("v0", "c0"), ("v0", "c1"), ("v1", "c0"), ("v1", "c2"), ("v2", "c0"), ("v2", "c3"), ("v3", "c0"), ("v3", "c4"),
         ("v4", "c1"), ("v4", "c2"), ("v5", "c1"), ("v5", "c3"), ("v6", "c1"), ("v6", "c4"), ("v7", "c2"), ("v7", "c3"),
         ("v8", "c2"), ("v8", "c4"), ("v9", "c3"), ("v9", "c4")}
tg.add_edges_by_name(edges)

# Alternatively given a biadjacency matrix (parity check matrix) a graph can be constructed from it
H = np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
              [1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
              [0, 1, 0, 0, 1, 0, 0, 1, 1, 0],
              [0, 0, 1, 0, 0, 1, 0, 1, 0, 1],
              [0, 0, 0, 1, 0, 0, 1, 0, 1, 1]])
tg = TannerGraph.from_biadjacency_matrix(H, channel_model=bsc_llr(0.1))

A graph may also be converted into a NetworkX Graph object. It can then be easily plotted using NetworkX Draw, or PyVis.

from belief_propagation import TannerGraph, bsc_llr
import numpy as np
H = np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
              [1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
              [0, 1, 0, 0, 1, 0, 0, 1, 1, 0],
              [0, 0, 1, 0, 0, 1, 0, 1, 0, 1],
              [0, 0, 0, 1, 0, 0, 1, 0, 1, 1]])
tg = TannerGraph.from_biadjacency_matrix(H, channel_model=bsc_llr(0.1))
g = tg.to_nx()

import networkx as nx
import matplotlib.pyplot as plt
fig = plt.figure()
top = nx.bipartite.sets(g)[0]
labels = {node: d["label"] for node,d in g.nodes(data=True)}
nx.draw_networkx(g,
                 with_labels=True,
                 node_color=[d["color"] for d in g.nodes.values()],
                 pos=nx.bipartite_layout(g, top),
                 labels=labels)
fig.show()

Belief Propagation

from belief_propagation import BeliefPropagation, TannerGraph, bsc_llr
import numpy as np
# consider a parity check matrix
H = np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
              [1, 0, 0, 0, 1, 1, 1, 0, 0, 0],
              [0, 1, 0, 0, 1, 0, 0, 1, 1, 0],
              [0, 0, 1, 0, 0, 1, 0, 1, 0, 1],
              [0, 0, 0, 1, 0, 0, 1, 0, 1, 1]])

# Use it to construct a Tanner graph. Assume a BSC channel model with probability p=0.1  ofr bit flip
model = bsc_llr(0.1)
tg = TannerGraph.from_biadjacency_matrix(H, channel_model=model)

# let us assume the codeword [1,1,0,0,1,0,0,0,0,0] was sent, but due to a channel error the last bit got flipped
c = np.array([1, 1, 0, 0, 1, 0, 0, 0, 0, 1])
# consequently we get initially H.dot(c) % 2
# array([0, 0, 0, 1, 1])

# let us try to correct the error
bp = BeliefPropagation(tg, H, max_iter=10)
estimate, llr, decode_success = bp.decode(c)
# You can see that the error is corrected