Segment parallel coding method of feedforward convolutional code

Abstract

The invention provides a segment parallel coding method of a feedforward convolutional code. The method is characterized in that: carrying out parallel coding on input information bit sequence by a plurality of serial feedforward convolution encoders, carrying out segmentation on an input information bit sequence to be coded, respectively applying a serial feedforward convolution encoder to code each segment of the information bit sequence, wherein, each segment of the information bit sequence is named as a block, in segmenting, overlapping bits, or after coding, inputting full 0 bits for supplement, generating overlap, according to an overlapped part, connecting code results of each serial feedforward convolution encoder in series, and taking the results as final output of parallel feedforward convolution coding. According to the invention, based on information sequence blocking, a characteristic of a feedforward convolution encoder is fully utilized, resource occupation and power consumption are reduced, and coding throughput is raised.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to communication using feedforward convolutional coding, and is a segmented parallel coding method of feedforward convolutional coding. Background technique [0002] A feed-forward convolutional encoder is a linear finite-state system that serially receives input information bits, processes them and outputs encoded bits serially. There is no feedback connection inside the feed-forward convolutional encoder, and its impulse response is finite. An example of a conventional feed-forward convolutional encoder is in figure 1 shown in . The throughput of the feed-forward convolutional encoder depends on the system clock rate, and the encoding process takes a long time when the length of the information bit sequence is large. Finding an algorithm to increase the throughput of feed-forward convolutional encoders is of interest for increasingly high-speed applications. [0003] Amo...

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Problems solved by technology

However, when the constraint length u of the convolutional code is large, the number of possible initial states will increase exponentially with the increase of u. Considering the resource and power consumption constraints, this scheme will not be available

At the same time, even if the number of initial states is small, such as figure 2 The number of possible initial states in the example is 4. The same resource and power consumption constraints make the number of blocks impossible to be large, which also limits the further improvement of the throughput of the convolutional encoder.

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