Novel use of channel information in a neural convolutional decoder

Advances in Artificial Intelligence

Bigdeli

2009

A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm.

Section: Advances In Artificial Intelligencementioning

confidence: 99%

A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion

Kao

Advances in Artificial Intelligence

Bigdeli

2009

“…In the past several years substantial efforts have been made to apply RNNs in error control coding theory. Initially, these networks were applied for block codes decoding [2,3] and then for convolutional [4][5][6][7] and turbo codes decoding [8]. In [5][6][7], it was shown that the decoding problem could be formulated as a function minimization problem and the gradient descent algorithm was applied to decode convolutional codes of a small code rate, and the developed recurrent artificial neural network (ANN) algorithm did not need any supervision.…”

Section: Introductionmentioning

confidence: 99%

FPGA Prototyping of RNN Decoder for Convolutional Codes

Salčić

EURASIP J. Adv. Signal Process.

Secker

2006

This paper presents prototyping of a recurrent type neural network (RNN) convolutional decoder using system-level design specification and design flow that enables easy mapping to the target FPGA architecture. Implementation and the performance measurement results have shown that an RNN decoder for hard-decision decoding coupled with a simple hard-limiting neuron activation function results in a very low complexity, which easily fits into standard Altera FPGA. Moreover, the design methodology allowed modeling of complete testbed for prototyping RNN decoders in simulation and real-time environment (same FPGA), thus enabling evaluation of BER performance characteristics of the decoder for various conditions of communication channel in real time.

“…These expressions for the general case encoder were applied for an example 1/2-rate encoder and the constraint length L = 3, which is shown in Figure 1 alongside with the unit impulse generator matrix obtained from the general form in (6).…”

Section: Development Of the Soft Decision Neural Network Algorithmmentioning

confidence: 99%

“…For the simple encoder analyzed in [6], defined by the ½rate, the constraint length L=3 and the impulse generator matrix given in Figure 3.…”

Section: Examplementioning

confidence: 99%

“…Artificial neural networks (ANNs) have been applied in various fields of digital communications primarily due to their non-linear processing, possible parallel processing and efficient hardware implementations [1]. Recently, substantial efforts have been made to apply ANNs in error control coding theory, initially, for block codes decoding [2], [3] and recently for convolutional [4], [5], [6], [7] and turbo codes decoding [8]. It was shown that the decoding problem could be formulated as a function minimization problem and the gradient descent algorithm was applied to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Soft Decision Output Convolutional Decoder Based on the Application of Neural Networks

MILCOM 2005 - 2005 IEEE Military Communications Conference

The paper investigates BER characteristics of a new algorithm for decoding convolutional codes based on neural networks. The novelty of the algorithm is in its capability to generate soft output estimates of the message bits encoded. It is shown that the defined noise energy function, which is traditionally used for the soft decoding algorithm of convolutional codes, can be related to the well known log likelihood function. The coding gain is calculated using a developed simulator of a coding communication system that uses a systematic 1/2-rate convolutional code.