This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder parameters in hard-decision situations. However, soft-decision systems are applied more and more as the improvement of signal processing techniques. In this paper we propose a method to utilize the soft information to improve the recognition performances in soft-decision communication systems. Besides, we propose a new recognition method based on correlation attack to meet low signal-to-noise ratio situations. Finally we give the simulation results to show the efficiency of the proposed methods.

In digital communication systems, error-correction codes are widely used. To meet high quality of services, new coding schemes are being developed ceaselessly. Therefore, for a communication receiver, it is very difficult to remain compatible with all standards used. But if it is an intelligent receiver, which is able to blindly recognize the coding parameters of a specific transmission context, it can adapt itself to the perpetual evolution of digital communications. Furthermore, the blind recognition techniques are also applied in noncooperative communications. In noncooperative communication contexts, a receiver does not know the coding parameters, so it must blindly recover the encoder before decoding. In this paper we focus on the blind recognition of coding parameters of an encoder which uses convolutional codes as error-correction coding and propose a method to take advantage of the soft information in soft-decision situations.

Some previous literatures discussed the problem of blind recognition of convolutional codes. The authors of [

However, the previous works are all discussed in hard-decision situations. In modern communication systems, more and more soft-decision based algorithms are applied to improve the signal processing performances. For example, the soft-decision based decoding methods always have better performances than hard-decision situations [

In Section

Besides, in Section

Finally we show the efficiency of the proposed algorithm by computer simulations in Section

The details of the GJETP-based recognition of rate

identification of code length

identification of a dual code basis;

identification of the generator matrix.

In the first procedure, the authors of [

Set the minimum and maximum value of

According to

Generation of the observed matrix

Transform the matrix

Obtain the set

If

Output the gap between two consecutive nonzero cardinals,

According to the GJETP algorithm, the reliability of upper part of

Fill the observed matrix

Arrange the rows of

Obtain the hard decisions of the bits in the rearranged

After this processing, the upper rows of the rearranged

Note that, in Step

Let

Calculate the vector

Initialize a vector

Finally we find the maximum element in

After taking the previously mentioned searching method we can find the most probable gap between two consecutive nonzero cardinals, that is, the estimation of the code length. An example as follows further describes the searching procedure.

Figure

The recorded vector

Then we can calculate the vector

Furthermore, we can calculate the vector

This example shows an implementation of the formalized algorithm proposed in this section to estimate the code length more exactly in a low SNR situation.

The GJETP-based dual code method proposed in [

Figure

The recorded vector

The recorded vector

We assume that when recognizing the code length we fill the matrix

Let

Let

Fill the matrix

Do the GJETP processing for

If

If

Let

The previous steps corrected the codeword synchronization. For the following procedure of parity check matrix recognition, we use

If the polynomial-based generator matrices of a

According to the analysis of [

Based on (

It is shown in (

So the recognition problem of a convolutional encoder is equivalent to the recognition of

Furthermore, for a matrix

In this paper we say two vectors

One solution to the recognition of

This scheme takes a very long elapsed time. Here we propose some principles to reduce the searching space.

Reduce the candidates of

According to (

Reduce the candidates of basic parity check vectors.

We can set the first and the last bit of each candidate vector to be 1. Hence, we just need to enumerate the combinations of the middle

In the noisy environment, not all the rows in

In most noisy environments, because of the existence of error bits, (

For a given data matrix

If the data matrix

So we propose the threshold

In (

So the number of rows of the observed data matrix

The threshold

The threshold

To implement the algorithm automatically by a computer program, we propose the following procedure to recognize the parameter

Set the searching range of

List all the composite numbers between

Let

Let

Let

Let

Create a vector:

Calculate the vector

If

If

If

End the searching.

If we can stop the searching from Step

List all the factors of

Let

Let

Fill the data matrix

Calculate the vector

If

If

End the searching.

After the estimation of

Finally, we search all the basic check vectors to recover the basic check matrix

Let

Let

Create a vector

Let

Enumerate all vectors

If

Stop the searching, and output

If

End the searching.

If such recognition procedure can successfully output

In hard-decision situations, we calculate

In this section we show the simulation results of the blind recognition of the convolutional coding parameters by utilizing the method introduced in this paper. The simulations include three parts corresponding to our proposed recognition algorithm on different noise level and different observed matrix size

For the GJETP-based recognition method, we show the false recognition ratios (FRR) in Figure

Recognition performances of GJETP method.

Figure

Recognition performances on different size of

In Figure

Comparison of GJETP method and correlation attack.

This paper proposes the methods of utilizing soft information to improve the recognition performance of convolutional encoder parameters. And we propose a formalized estimation of the parameter

The authors declare that there is no conflict of interests regarding the publication of this paper.