DFT Matrix on GF(4): Exploring G in Soft-decision Decoding of Polar Codes

In summary, the paper discusses the use of Reed-Solomon kernels in soft-decision decoding of polar codes. On page 319, the author mentions that G is a Reed-Solomon kernel and is also a DFT matrix. G is defined on page 321 as a matrix with possible changes in the order of rows, where α is a primitive element of the finite field \mathbf{F}_{2^2}. The author's claim that G is a DFT matrix is unclear, as DFT matrices do not have zeros in their general form. The paper focuses on computational complexity and it is possible that the DFT submatrix of G is sufficient for their purposes. However, it is still unclear how G can be implemented
  • #1
ait.abd
26
0
I am reading the following paper:
Soft-decision decoding of polar codes with Reed-Solomon kernels

On the last line of the page 319 (page 3 of the pdf) the author says "and [itex]G[/itex] is a Reed-Solomon kernel, which is in fact a DFT matrix".

[itex]G[/itex] is defined on the page 321 (page 5 of the pdf) with possible change in the order of rows as
$$
G = \left( \begin{array}{cccc}
1 & 1 & 1 & 0 \\
1 & \alpha & \alpha^2 & 0 \\
1 & \alpha^2 & \alpha & 0 \\
1 & 1 & 1 & 1\end{array} \right),
$$

where [itex]\alpha[/itex] is a primitive element of [itex]\mathbf{F}_{2^2}[/itex].

I do not understand why the author calls [itex]G[/itex] as a DFT matrix, because DFT matrix does not have zeros in its general form. The general form that I am considering is the following:
Wikipedia Link.

Can anyone explain the following:
1. Why [itex]G[/itex] is a DFT matrix?
2. If it is a DFT matrix, how can we implement it using FFT? I am looking for the butterfly structure that will implement it.

Thanks for your time.
 
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  • #2
I can only assume that it is sufficient to consider the DFT submatrix, because the paper is about computational complexity and zeros might not count, as they do not lead to calculation steps.
 

Related to DFT Matrix on GF(4): Exploring G in Soft-decision Decoding of Polar Codes

1. What is a DFT Matrix on GF(4)?

A DFT (Discrete Fourier Transform) Matrix on GF(4) is a matrix that is used in soft-decision decoding of polar codes. It is a special type of matrix that operates on elements in the Galois Field of order 4, also known as GF(4). GF(4) is a finite field of size 4, which means it contains 4 elements: 0, 1, α, and α^2. The DFT Matrix on GF(4) is used to convert the received signal into a different representation, which is then used in the decoding process.

2. What is soft-decision decoding?

Soft-decision decoding is a decoding technique used in error-correcting codes, such as polar codes. It takes into account the reliability of the received signal, rather than simply treating it as a binary value (0 or 1). This means that the decoder can make better decisions about which bits are more likely to be correct, based on the strength of the received signal.

3. How does the DFT Matrix on GF(4) help in soft-decision decoding of polar codes?

The DFT Matrix on GF(4) is used in the soft-decision decoding process to transform the received signal into a different representation. This representation is then used in the decoding algorithm to make more accurate decisions about which bits are more likely to be correct. The DFT Matrix on GF(4) essentially helps in improving the reliability of the received signal, which leads to better decoding performance.

4. What are polar codes?

Polar codes are a type of error-correcting code that was introduced by Erdal Arıkan in 2008. They are based on the concept of channel polarization, which involves transforming a set of noisy channels into a set of noiseless channels. Polar codes have been shown to achieve the capacity of binary-input discrete memoryless channels, making them one of the most powerful error-correcting codes known.

5. What is the significance of exploring G in soft-decision decoding of polar codes?

The matrix G in soft-decision decoding of polar codes refers to the generator matrix, which is used to generate the code words in polar codes. Exploring G is significant because it allows for the optimization of the decoding process, which can lead to better error-correction performance. By exploring different properties of G, researchers can improve the decoding algorithm and make polar codes more efficient and reliable.

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