京都大学 情報学研究科 知能情報学専攻 2024年8月実施 専門科目 S-3
Author
itsuitsuki
Description
大学公表の原題
Q.1
A discrete memoryless channel C consists of two discrete memoryless channels D and E, which are connected serially as shown in the following figure.
X → D Y → E Z X\xrightarrow{D}Y\xrightarrow{E}Z X D Y E Z
The input alphabet of D is Σ a = { a 1 , a 2 , a 3 , a 4 } \Sigma_a = \{a_1, a_2, a_3, a_4\} Σ a = { a 1 , a 2 , a 3 , a 4 } . Both of the output alphabet of D and the input alphabet of E are Σ b = { b 1 , b 2 , b 3 , b 4 } \Sigma_b = \{b_1, b_2, b_3, b_4\} Σ b = { b 1 , b 2 , b 3 , b 4 } . The output alphabet of E is Σ c = { c 1 , c 2 } \Sigma_c = \{c_1, c_2\} Σ c = { c 1 , c 2 } . Let random variables X , Y X, Y X , Y , and Z Z Z be respectively on Σ a , Σ b \Sigma_a, \Sigma_b Σ a , Σ b , and Σ c \Sigma_c Σ c . The channel transition matrix p ( Y ∣ X ) p(Y|X) p ( Y ∣ X ) for D and the channel transition matrix q ( Z ∣ Y ) q(Z|Y) q ( Z ∣ Y ) for E are given as
p ( Y ∣ X ) = ( 1 2 1 2 0 0 0 1 2 1 2 0 0 0 1 2 1 2 1 2 0 0 1 2 ) , and q ( Z ∣ Y ) = ( 1 0 1 0 0 1 0 1 ) . p(Y|X) = \begin{pmatrix} \frac{1}{2} & \frac{1}{2} & 0 & 0 \\ 0 & \frac{1}{2} & \frac{1}{2} & 0 \\ 0 & 0 & \frac{1}{2} & \frac{1}{2} \\ \frac{1}{2} & 0 & 0 & \frac{1}{2} \end{pmatrix}, \quad \text{and} \quad q(Z|Y) = \begin{pmatrix} 1 & 0 \\ 1 & 0 \\ 0 & 1 \\ 0 & 1 \end{pmatrix}. p ( Y ∣ X ) = 2 1 0 0 2 1 2 1 2 1 0 0 0 2 1 2 1 0 0 0 2 1 2 1 , and q ( Z ∣ Y ) = 1 1 0 0 0 0 1 1 .
Answer the following questions.
(1) Compute the channel capacity of D.
(2) Assume that X X X follows the probability distribution r ( a i ) r(a_i) r ( a i ) given below. Compute the mutual information I ( X ; Z ) I(X; Z) I ( X ; Z ) . You must show its derivation.
r ( a 1 ) = r ( a 3 ) = 1 2 , and r ( a 2 ) = r ( a 4 ) = 0. r(a_1) = r(a_3) = \frac{1}{2}, \quad \text{and} \quad r(a_2) = r(a_4) = 0. r ( a 1 ) = r ( a 3 ) = 2 1 , and r ( a 2 ) = r ( a 4 ) = 0.
Q.2
We consider only the AND operation and the XOR (exclusive or) operation ⊕ \oplus ⊕ for the elements in { 0 , 1 } \{0, 1\} { 0 , 1 } . We define a word as an element in { 0 , 1 } k ( k ≥ 1 ) \{0, 1\}^k \ (k \ge 1) { 0 , 1 } k ( k ≥ 1 ) , each of which is represented as a row vector. Consider the liner codes generated with a matrix G G G of k k k rows and n n n columns ( k , n ≥ 1 ) (k, n \ge 1) ( k , n ≥ 1 ) as x = w G \boldsymbol{x} = \boldsymbol{w}G x = w G , where w ∈ { 0 , 1 } k \boldsymbol{w} \in \{0, 1\}^k w ∈ { 0 , 1 } k is a word and x ∈ { 0 , 1 } n \boldsymbol{x} \in \{0, 1\}^n x ∈ { 0 , 1 } n is a codeword. Let C ( G ) = { w G ∣ w ∈ { 0 , 1 } k } C(G) = \{\boldsymbol{w}G \mid \boldsymbol{w} \in \{0, 1\}^k\} C ( G ) = { w G ∣ w ∈ { 0 , 1 } k } . Answer the following questions.
(1) Show that x ⊕ y ∈ C ( G ) \boldsymbol{x} \oplus \boldsymbol{y} \in C(G) x ⊕ y ∈ C ( G ) holds for all x , y ∈ C ( G ) \boldsymbol{x}, \boldsymbol{y} \in C(G) x , y ∈ C ( G ) , where x ⊕ y \boldsymbol{x} \oplus \boldsymbol{y} x ⊕ y is the row vector obtained by element-wise XOR of two row vectors x \boldsymbol{x} x and y \boldsymbol{y} y .
(2) For the set C ( G ) C(G) C ( G ) , prove that
min x , y ∈ C ( G ) and x ≠ y d ( x , y ) = min x ∈ C ( G ) and x ≠ 0 d ( x , 0 ) , \min_{\boldsymbol{x}, \boldsymbol{y} \in C(G) \text{ and } \boldsymbol{x} \neq \boldsymbol{y}} d(\boldsymbol{x}, \boldsymbol{y}) = \min_{\boldsymbol{x} \in C(G) \text{ and } \boldsymbol{x} \neq \boldsymbol{0}} d(\boldsymbol{x}, \boldsymbol{0}), x , y ∈ C ( G ) and x = y min d ( x , y ) = x ∈ C ( G ) and x = 0 min d ( x , 0 ) ,
where d d d is the Hamming distance and 0 = ( 0 , 0 , … , 0 ) \boldsymbol{0} = (0, 0, \dots, 0) 0 = ( 0 , 0 , … , 0 ) .
(3) For the case that G G G is given below, find a matrix F F F of n n n rows and n n n columns such that G ′ = G F G' = GF G ′ = GF generates a systematic code. Moreover, by using the matrices F F F and G ′ G' G ′ , compute the parity check matrix H H H for C ( G ) C(G) C ( G ) .
G = ( 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 ) . G = \begin{pmatrix} 0 & 0 & 0 & 1 & 1 & 0 & 1 \\ 0 & 0 & 1 & 1 & 0 & 1 & 0 \\ 0 & 1 & 1 & 0 & 1 & 0 & 0 \\ 1 & 1 & 0 & 1 & 0 & 0 & 0 \end{pmatrix}. G = 0 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 0 .
题目描述
考虑图示的串联系统,其中离散无记忆信道 D 的输入、输出字母表均为四元集合,信道 E 把四元输入映射为二元输出:
X → D Y → E Z X\xrightarrow{D}Y\xrightarrow{E}Z X D Y E Z
D 与 E 的转移概率矩阵分别为
p ( Y ∣ X ) = ( 1 2 1 2 0 0 0 1 2 1 2 0 0 0 1 2 1 2 1 2 0 0 1 2 ) , q ( Z ∣ Y ) = ( 1 0 1 0 0 1 0 1 ) . p(Y\mid X)=
\begin{pmatrix}
\frac12&\frac12&0&0\\
0&\frac12&\frac12&0\\
0&0&\frac12&\frac12\\
\frac12&0&0&\frac12
\end{pmatrix},
\qquad
q(Z\mid Y)=
\begin{pmatrix}
1&0\\
1&0\\
0&1\\
0&1
\end{pmatrix}. p ( Y ∣ X ) = 2 1 0 0 2 1 2 1 2 1 0 0 0 2 1 2 1 0 0 0 2 1 2 1 , q ( Z ∣ Y ) = 1 1 0 0 0 0 1 1 .
(1)求信道 D 的信道容量;(2)若输入符号 a 1 , a 3 a_1,a_3 a 1 , a 3 的概率各为 1 2 \frac12 2 1 ,而 a 2 , a 4 a_2,a_4 a 2 , a 4 的概率为 0,请写出推导过程并计算串联后输入 X X X 与最终输出 Z Z Z 的互信息 I ( X ; Z ) I(X;Z) I ( X ; Z ) 。
在二元域上考虑由 k × n k\times n k × n 生成矩阵 G G G 定义的线性码。对 k k k 位行向量信息字 w \boldsymbol{w} w ,码字为 x = w G \boldsymbol{x}=\boldsymbol{w}G x = w G ,所有码字组成 C ( G ) C(G) C ( G ) 。
(1)证明任意 x , y ∈ C ( G ) \boldsymbol{x},\boldsymbol{y}\in C(G) x , y ∈ C ( G ) 的按位异或 x ⊕ y \boldsymbol{x}\oplus\boldsymbol{y} x ⊕ y 仍属于 C ( G ) C(G) C ( G ) 。
(2)证明
min x , y ∈ C ( G ) x ≠ y d ( x , y ) = min x ∈ C ( G ) x ≠ 0 d ( x , 0 ) , \min_{\substack{\boldsymbol{x},\boldsymbol{y}\in C(G)\\\boldsymbol{x}\ne\boldsymbol{y}}}
d(\boldsymbol{x},\boldsymbol{y})
=
\min_{\substack{\boldsymbol{x}\in C(G)\\\boldsymbol{x}\ne\boldsymbol{0}}}
d(\boldsymbol{x},\boldsymbol{0}), x , y ∈ C ( G ) x = y min d ( x , y ) = x ∈ C ( G ) x = 0 min d ( x , 0 ) ,
其中 d d d 为 Hamming 距离,0 = ( 0 , 0 , … , 0 ) \boldsymbol{0}=(0,0,\ldots,0) 0 = ( 0 , 0 , … , 0 ) 。
(3)当
G = ( 0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 ) G=
\begin{pmatrix}
0&0&0&1&1&0&1\\
0&0&1&1&0&1&0\\
0&1&1&0&1&0&0\\
1&1&0&1&0&0&0
\end{pmatrix} G = 0 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 0
时,求一个 n × n n\times n n × n 矩阵 F F F ,使 G ′ = G F G'=GF G ′ = GF 生成系统码;再利用 F F F 与 G ′ G' G ′ 计算原码 C ( G ) C(G) C ( G ) 的校验矩阵 H H H 。
Kai
All logarithms in Q.1 have base 2 2 2 , and all matrix operations in Q.2 are over F 2 \mathbb F_2 F 2 .
Q.1
(1)
Every row of p ( Y ∣ X ) p(Y\mid X) p ( Y ∣ X ) has entropy 1 1 1 , so H ( Y ∣ X ) = 1 H(Y\mid X)=1 H ( Y ∣ X ) = 1 for any input distribution. Since Y Y Y has four possible values, H ( Y ) ≤ 2 H(Y)\le2 H ( Y ) ≤ 2 . Uniform input makes the output uniform because each column sums to one, attaining the bound. Therefore
C D = max I ( X ; Y ) = 2 − 1 = 1 bit . C_D=\max I(X;Y)=2-1=1\text{ bit}. C D = max I ( X ; Y ) = 2 − 1 = 1 bit .
(2)
Multiplying the two channel matrices gives
p ( Z ∣ X ) = ( 1 0 1 / 2 1 / 2 0 1 1 / 2 1 / 2 ) . p(Z\mid X)=
\begin{pmatrix}1&0\\1/2&1/2\\0&1\\1/2&1/2\end{pmatrix}. p ( Z ∣ X ) = 1 1/2 0 1/2 0 1/2 1 1/2 .
Only a 1 , a 3 a_1,a_3 a 1 , a 3 occur. Their outputs are deterministically c 1 , c 2 c_1,c_2 c 1 , c 2 , respectively, each with probability 1 / 2 1/2 1/2 . Thus H ( Z ) = 1 H(Z)=1 H ( Z ) = 1 , H ( Z ∣ X ) = 0 H(Z\mid X)=0 H ( Z ∣ X ) = 0 , and I ( X ; Z ) = 1 I(X;Z)=1 I ( X ; Z ) = 1 bit.
Q.2
(1)
Write x = u G \boldsymbol x=\boldsymbol uG x = u G and y = v G \boldsymbol y=\boldsymbol vG y = v G . Distributivity over F 2 \mathbb F_2 F 2 gives
x ⊕ y = ( u ⊕ v ) G ∈ C ( G ) . \boldsymbol x\oplus\boldsymbol y=(\boldsymbol u\oplus\boldsymbol v)G\in C(G). x ⊕ y = ( u ⊕ v ) G ∈ C ( G ) .
(2)
For distinct codewords, x ⊕ y \boldsymbol x\oplus\boldsymbol y x ⊕ y is a nonzero codeword and
d ( x , y ) = d ( x ⊕ y , 0 ) . d(\boldsymbol x,\boldsymbol y)
=d(\boldsymbol x\oplus\boldsymbol y,\boldsymbol0). d ( x , y ) = d ( x ⊕ y , 0 ) .
Conversely, every nonzero codeword occurs in the pair ( x , 0 ) (\boldsymbol x,\boldsymbol0) ( x , 0 ) . The two sets of distances therefore coincide and have equal minima. This statement assumes C ( G ) ≠ { 0 } C(G)\ne\{\boldsymbol0\} C ( G ) = { 0 } ; for the zero code both sets are empty (or both minima are + ∞ +\infty + ∞ under that convention).
(3)
Partition G = [ A B ] G=[A\ B] G = [ A B ] , where A A A consists of the first four columns. It is invertible over F 2 \mathbb F_2 F 2 . Choose
F = diag ( A − 1 , I 3 ) = ( 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 ) . F=\operatorname{diag}(A^{-1},I_3)=
\begin{pmatrix}
0&1&1&1&0&0&0\\
1&1&1&0&0&0&0\\
1&1&0&0&0&0&0\\
1&0&0&0&0&0&0\\
0&0&0&0&1&0&0\\
0&0&0&0&0&1&0\\
0&0&0&0&0&0&1
\end{pmatrix}. F = diag ( A − 1 , I 3 ) = 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 .
Then
G ′ = G F = [ I 4 B ] = ( 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 ) . G'=GF=[I_4\ B]=
\begin{pmatrix}
1&0&0&0&1&0&1\\
0&1&0&0&0&1&0\\
0&0&1&0&1&0&0\\
0&0&0&1&0&0&0
\end{pmatrix}. G ′ = GF = [ I 4 B ] = 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 .
A parity-check matrix for C ( G ′ ) C(G') C ( G ′ ) is H ′ = [ B T I 3 ] H'=[B^T\ I_3] H ′ = [ B T I 3 ] . Since a row codeword transforms as x ′ = x F \boldsymbol x'=\boldsymbol xF x ′ = x F , the original code has parity-check matrix H = H ′ F T H=H'F^T H = H ′ F T :
H = ( 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 1 1 1 0 0 1 ) . \boxed{H=
\begin{pmatrix}
1&0&1&1&1&0&0\\
1&1&1&0&0&1&0\\
0&1&1&1&0&0&1
\end{pmatrix}}. H = 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 0 1 .
Indeed G H T = 0 GH^T=0 G H T = 0 and rank H = 3 \operatorname{rank}H=3 rank H = 3 , so the kernel consists of exactly the 2 4 2^4 2 4 original codewords.