复杂代码
时间: 2023-07-02 13:12:35 浏览: 50
下面是一个比较复杂的LDPC编码和译码的示例代码,其中包含了矩阵生成、编码、加噪、译码、纠错等多个步骤,供参考:
```python
import numpy as np
import math
# 生成LDPC矩阵
def generateLDPCMatrix(n, m, dv, dc):
# 初始化矩阵
H = np.zeros((m, n), dtype=int)
# 生成随机的正交矩阵
A = np.random.rand(n-dv, n-dc)
Q, _ = np.linalg.qr(A)
# 将Q的前dv行和dc列插入H矩阵中
H[:dv, :] = np.eye(dv, n)
H[dv:, dc:] = Q.T
# 构造校验位矩阵
G = np.mod(-H[:, dv:].T, 2)
return H, G
# 编码
def encodeLDPC(G, msg):
n = G.shape[1]
k = G.shape[0]
codeword = np.zeros(n, dtype=int)
for i in range(n):
codeword[i] = np.mod(np.dot(G[:, i], msg), 2)
return codeword
# 加噪
def addNoise(codeword, EbN0_dB):
n = codeword.shape[0]
snr = 10**(EbN0_dB/10)
Es = np.sum(codeword**2)/n
N0 = Es/snr
noise = np.random.normal(loc=0, scale=math.sqrt(N0/2), size=n)
received = codeword + noise
return received
# 译码
def decodeLDPC(H, received, max_iter):
n = H.shape[1]
m = H.shape[0]
dv = H.shape[0] - np.sum(H, axis=0)
dc = H.shape[1] - np.sum(H, axis=1)
# 初始化消息向量和校验位
msg = np.zeros(n-m)
chk = np.zeros(m)
# 设置迭代次数和误差阈值
tolerance = 1e-5
# 开始迭代
for iter in range(max_iter):
# 计算校验位的值
for i in range(m):
chk[i] = np.mod(np.dot(H[i, :], received), 2)
# 计算校验位的偏差
dev = chk - np.mod(np.dot(H, received + msg), 2)
# 更新消息向量
for i in range(n-m):
msg[i] = np.sum(dev[H[:, i+m] == 1])
# 计算误差
error = np.linalg.norm(dev)
# 如果误差小于阈值,退出迭代
if error < tolerance:
break
# 纠错
for i in range(n):
if received[i] > 0.5:
received[i] = 1
else:
received[i] = 0
return received[:n-m]
# 测试LDPC编码和译码
if __name__ == '__main__':
# 设置LDPC参数
n = 1024
m = 512
dv = 6
dc = 10
max_iter = 50
# 生成LDPC矩阵
H, G = generateLDPCMatrix(n, m, dv, dc)
# 生成随机的消息向量
msg = np.random.randint(2, size=n-m)
# 编码
codeword = encodeLDPC(G, msg)
# 加噪
EbN0_dB = 5
received = addNoise(codeword, EbN0_dB)
# 译码
decoded = decodeLDPC(H, received, max_iter)
# 计算误码率
errors = np.sum(decoded != msg)
ber = errors/(n-m)
# 输出结果
print("LDPC Parameters:")
print("n = ", n)
print("m = ", m)
print("dv = ", dv)
print("dc = ", dc)
print("EbN0_dB = ", EbN0_dB)
print("Bit Error Rate = ", ber)
```
在这个示例代码中,我们首先使用`generateLDPCMatrix()`函数生成了一个LDPC矩阵,然后使用`encodeLDPC()`函数对随机的消息向量进行编码,接着使用`addNoise()`函数为编码后的向量加入高斯白噪声,最后使用`decodeLDPC()`函数对加噪后的向量进行译码,并进行纠错处理。最后,我们计算了误码率并输出了结果。需要注意的是,这个示例代码中的LDPC矩阵和参数都是随机生成的,不同的参数和矩阵可能会产生不同的结果。
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