diff --git a/REPORT.md b/REPORT.md
new file mode 100644
index 0000000..5b435ee
--- /dev/null
+++ b/REPORT.md
@@ -0,0 +1,60 @@
+# 无线通信技术实验报告：信道编码与信道均衡
+
+## 1. 实验目的
+
+说明本实验希望掌握的信道编码、信道均衡和 GitHub 自动评分技能。
+
+## 2. 实验原理
+
+### 2.1 信道编码
+
+说明 Hamming(7,4)、伴随式、单比特纠错、编码增益等概念。
+
+### 2.2 信道均衡
+
+说明 ISI、多径信道、ZF、LMS 自适应均衡等概念。
+
+## 3. 实验环境
+
+- Python 版本：
+- 主要依赖：NumPy、Matplotlib、pytest
+- AI 助手使用情况：
+
+## 4. 实验方法与步骤
+
+### 4.1 Part 1：信道编码
+
+描述编码、加噪/翻转、译码、BER 对比流程。
+
+### 4.2 Part 2：信道均衡
+
+描述多径信道、ZF/LMS 均衡器设计、均衡效果评估流程。
+
+## 5. 实验结果
+
+插入结果图：
+
+```markdown
+![编码BER曲线](results/coding_ber_curve.png)
+![均衡眼图对比](results/equalization_eye_comparison.png)
+![LMS误差曲线](results/equalization_mse_curve.png)
+```
+
+## 6. 结果分析与讨论
+
+回答：
+
+1. Hamming(7,4) 为什么能纠正单比特错误？
+2. 为什么信道编码会引入冗余并降低码率？
+3. ZF 均衡为什么可能放大噪声？
+4. LMS 的步长过大或过小会出现什么问题？
+5. 均衡前后 ISI 有什么变化？
+
+## 7. 实验心得
+
+说明你对信道编码、信道均衡、自动评分和 AI 编程辅助的理解。
+
+## 8. 参考资料
+
+- 课程课件：第6章 信道编码
+- 课程课件：第7章 均衡
diff --git a/results/coding_ber_curve.png b/results/coding_ber_curve.png
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diff --git a/results/equalization_eye_comparison.png b/results/equalization_eye_comparison.png
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diff --git a/results/equalization_mse_curve.png b/results/equalization_mse_curve.png
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diff --git a/src/part1_channel_coding.py b/src/part1_channel_coding.py
index 1ecf55e..fea7b6c 100644
--- a/src/part1_channel_coding.py
+++ b/src/part1_channel_coding.py
@@ -26,6 +26,11 @@
     [0, 1, 1, 1, 0, 0, 1],
 ], dtype=int)
 
+SYNDROME_TO_ERROR = {
+    tuple(HAMMING_H[:, j]): j
+    for j in range(HAMMING_H.shape[1])
+}
+
 
 def hamming74_encode(bits):
     """
@@ -48,8 +53,10 @@ def hamming74_encode(bits):
     if not np.all((bits == 0) | (bits == 1)):
         raise ValueError('bits 只能包含 0 或 1')
 
-    # TODO: 将 bits reshape 为 (-1, 4)，再与 HAMMING_G 相乘并对 2 取模。
-    raise NotImplementedError('请实现 Hamming(7,4) 编码')
+    # 将 bits reshape 为 (-1, 4)，再与 HAMMING_G 相乘并对 2 取模
+    blocks = bits.reshape(-1, 4)
+    encoded = (blocks @ HAMMING_G) % 2
+    return encoded.flatten()
 
 
 def hamming74_syndrome(codewords):
@@ -70,8 +77,9 @@ def hamming74_syndrome(codewords):
     if codewords.shape[1] != 7:
         raise ValueError('每个 Hamming(7,4) 码字长度必须为 7')
 
-    # TODO: 计算 s = r H^T mod 2。
-    raise NotImplementedError('请实现伴随式计算')
+    # 计算 s = r H^T mod 2
+    syndromes = (codewords @ HAMMING_H.T) % 2
+    return syndromes
 
 
 def hamming74_decode(received):
@@ -94,8 +102,19 @@ def hamming74_decode(received):
     if received.ndim != 1 or len(received) % 7 != 0:
         raise ValueError('received 必须是一维数组，长度为 7 的倍数')
 
-    # TODO: 使用 hamming74_syndrome 完成单比特纠错，并返回前 4 个信息位。
-    raise NotImplementedError('请实现 Hamming(7,4) 译码')
+    # reshape为(-1, 7)，避免直接修改输入
+    codewords = received.reshape(-1, 7).copy()
+    syndromes = hamming74_syndrome(codewords)
+    
+    # 对每个非零伴随式进行纠错
+    for i, syndrome in enumerate(syndromes):
+        if syndrome.any():
+            error_position = SYNDROME_TO_ERROR.get(tuple(syndrome))
+            if error_position is not None:
+                codewords[i, error_position] ^= 1
+
+    # 取前 4 个信息位并 flatten 返回
+    return codewords[:, :4].ravel()
 
 
 def convolutional_encode(bits):
@@ -108,8 +127,26 @@ def convolutional_encode(bits):
     if not np.all((bits == 0) | (bits == 1)):
         raise ValueError('bits 只能包含 0 或 1')
 
-    # TODO: 选做任务，可参考课件第6章卷积码部分。
-    raise NotImplementedError('选做：请实现卷积码编码')
+    # 在末尾添加 2 个尾比特使状态回到全零
+    bits_with_tail = np.concatenate([bits, np.array([0, 0], dtype=int)])
+    
+    # 初始状态：2个秘密状态位
+    state = 0  # (s1, s0)
+    encoded = []
+    
+    for bit in bits_with_tail:
+        # 根据 g1=111 (7) 和 g2=101 (5) 计算两个输出比特
+        # g1=111: 输出1 = input XOR state_bit1 XOR state_bit0
+        # g2=101: 输出2 = input XOR state_bit0
+        out1 = bit ^ ((state >> 1) & 1) ^ (state & 1)  # g1
+        out2 = bit ^ (state & 1)  # g2
+        encoded.append(out1)
+        encoded.append(out2)
+        
+        # 更新状态: 新状态 = (input, state_bit1)
+        state = (bit << 1) | ((state >> 1) & 1)
+    
+    return np.array(encoded, dtype=int)
 
 
 def viterbi_decode_hard(received_bits):
@@ -120,8 +157,70 @@ def viterbi_decode_hard(received_bits):
     if len(received_bits) % 2 != 0:
         raise ValueError('卷积码接收序列长度必须是 2 的倍数')
 
-    # TODO: 选做任务，可使用汉明距离作为路径度量。
-    raise NotImplementedError('选做：请实现 Viterbi 硬判决译码')
+    # 4个可能的状态：(s1, s0)
+    num_states = 4
+    num_steps = len(received_bits) // 2
+    
+    # 处理接收序列，每次输入一寸(2比特)
+    received_pairs = received_bits.reshape(-1, 2)
+    
+    # 动态规划：路径度量和路径跟踪
+    # path_metric[state] = 到当前状态的最小累计度量
+    # paths[t, state] = 第 t 时刻到达 state 时的前一状态
+    path_metric = np.full(num_states, np.inf, dtype=float)
+    path_metric[0] = 0  # 初始状态为 0
+    paths = np.zeros((num_steps, num_states), dtype=int)
+    
+    # Viterbi 轨迹
+    for t, received_pair in enumerate(received_pairs):
+        new_metric = np.full(num_states, np.inf, dtype=float)
+        new_paths = np.zeros(num_states, dtype=int)
+        
+        # 对每个当前可达状态
+        for curr_state in range(num_states):
+            if path_metric[curr_state] == np.inf:
+                continue
+                
+            # 对每个可能的输入（0 或 1）
+            for input_bit in [0, 1]:
+                # 计算下一个状态和输出
+                # state = (s1, s0), 新下一个状态 = (input_bit, s1)
+                next_state = (input_bit << 1) | ((curr_state >> 1) & 1)
+                
+                # 根据 g1, g2 计算输出
+                s1 = (curr_state >> 1) & 1
+                s0 = curr_state & 1
+                out1 = input_bit ^ s1 ^ s0  # g1 = 111
+                out2 = input_bit ^ s0       # g2 = 101
+                
+                # 计算汉明距离
+                hamming_dist = int((out1 != received_pair[0]) + (out2 != received_pair[1]))
+                new_metric_value = path_metric[curr_state] + hamming_dist
+                
+                # 更新最小路径
+                if new_metric_value < new_metric[next_state]:
+                    new_metric[next_state] = new_metric_value
+                    new_paths[next_state] = curr_state
+        
+        path_metric = new_metric
+        paths[t] = new_paths
+    
+    # 回溯：从最优终状态出发
+    final_state = np.argmin(path_metric)
+    
+    # 追踪轨迹
+    decoded_bits = []
+    state = final_state
+    for t in range(num_steps - 1, -1, -1):
+        prev_state = paths[t, state]
+        # 从 prev_state 到 state 的输入比特是 input_bit
+        # state = (input_bit << 1) | ((prev_state >> 1) & 1)
+        input_bit = (state >> 1) & 1
+        decoded_bits.insert(0, input_bit)
+        state = prev_state
+    
+    # 删除最后 2 个尾比特
+    return np.array(decoded_bits[:-2], dtype=int)
 
 
 def run_coding_demo():
diff --git a/src/part2_equalization.py b/src/part2_equalization.py
index 8cbf1d8..ea2c9fa 100644
--- a/src/part2_equalization.py
+++ b/src/part2_equalization.py
@@ -16,6 +16,15 @@
 )
 
 
+def _build_convolution_matrix(channel, num_taps):
+    """构造信道与 FIR 系数卷积的 Toeplitz 矩阵。"""
+    conv_len = len(channel) + num_taps - 1
+    A = np.zeros((conv_len, num_taps), dtype=float)
+    for i in range(num_taps):
+        A[i:i+len(channel), i] = channel
+    return A
+
+
 def estimate_zf_equalizer(channel, num_taps):
     """
     估计迫零（Zero-Forcing, ZF）FIR 均衡器。
@@ -38,8 +47,13 @@ def estimate_zf_equalizer(channel, num_taps):
     if num_taps < 1:
         raise ValueError('num_taps 必须为正整数')
 
-    # TODO: 构造卷积矩阵并求解 ZF 均衡器抽头。
-    raise NotImplementedError('请实现 ZF 均衡器估计')
+    A = _build_convolution_matrix(channel, num_taps)
+    d = np.zeros(A.shape[0], dtype=float)
+    center = (A.shape[0] - 1) // 2
+    d[center] = 1.0
+
+    taps, _, _, _ = np.linalg.lstsq(A, d, rcond=None)
+    return taps
 
 
 def apply_fir_filter(signal, taps):
@@ -58,8 +72,13 @@ def apply_fir_filter(signal, taps):
     if signal.ndim != 1 or taps.ndim != 1:
         raise ValueError('signal 和 taps 必须是一维数组')
 
-    # TODO: 使用 np.convolve，并截取与 signal 等长的输出。
-    raise NotImplementedError('请实现 FIR 滤波')
+    # 使用 np.convolve 的 full 模式
+    convolved = np.convolve(signal, taps, mode='full')
+    
+    # 截取前 len(signal) 个样本，使输出与输入等长
+    filtered = convolved[:len(signal)]
+    
+    return filtered
 
 
 def lms_equalizer(rx_train, tx_train, num_taps, step_size=0.01):
@@ -89,8 +108,29 @@ def lms_equalizer(rx_train, tx_train, num_taps, step_size=0.01):
     if num_taps < 1:
         raise ValueError('num_taps 必须为正整数')
 
-    # TODO: 实现 LMS 自适应均衡训练。
-    raise NotImplementedError('请实现 LMS 均衡器')
+    # 初始化抽头，中心位置为 1
+    taps = np.zeros(num_taps, dtype=float)
+    center = num_taps // 2
+    taps[center] = 1.0
+
+    errors = []
+
+    # 从第 num_taps-1 个样本开始迭代，确保有足够的过去样本
+    for n in range(num_taps - 1, len(rx_train)):
+        # 构造当前输入向量 x[n] = [rx_train[n], rx_train[n-1], ..., rx_train[n-num_taps+1]]
+        x = rx_train[n - num_taps + 1:n + 1][::-1]
+
+        # 计算输出 y[n] = w^T x[n]
+        y = np.dot(taps, x)
+
+        # 计算误差 e[n] = d[n] - y[n]
+        e = tx_train[n] - y
+        errors.append(e)
+
+        # 更新抽头 w = w + μ e[n] x[n]
+        taps += step_size * e * x
+
+    return taps, np.asarray(errors, dtype=float)
 
 
 def run_equalization_demo():