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Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper, we discuss several graph-cover-based methods for deriving families of time-invariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code(More)
LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. In this paper, asymptotic methods are used to calculate a lower bound on the free distance for several ensembles of asymptotically good protograph-based LDPC convolutional codes. Further,(More)
—Potentially large storage requirements and long initial decoding delays are two practical issues related to the decoding of low-density parity-check (LDPC) convolutional codes using a continuous pipeline decoder architecture. In this paper, we propose several reduced complexity decoding strategies to lessen the storage requirements and the initial decoding(More)
— LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. However, traditional means of comparing block and convolu-tional codes tied to the implementation complexity of trellis-based decoding are irrelevant for message-passing decoders. In(More)
— We propose a novel code design technique for irregular LDPC convolutional codes. The constructed codes can be encoded continuously in real time with the help of a shift-register based encoder. For moderate values of the syndrome former memory, simulation results show that the constructed codes outperform LDPC block codes with comparable hardware(More)
— While low-density parity-check (LDPC) convolutional codes tend to significantly outperform LDPC block codes with the same processor complexity , large storage requirements and a long initial decoding delay are two issues related to their continuous pipeline decoding architecture [1]. In this paper, we propose reduced complexity decoding strategies to(More)
—We propose a low-cost serial decoder architecture for low-density parity-check convolutional codes (LDPC-CCs). It has been shown that LDPC-CCs can achieve comparable performance to LDPC block codes with constraint length much less than the block length. The proposed serial decoder architecture for LDPC-CCs uses a single decoding processor. Terminated data(More)
Low-density parity-check (LDPC) convolutional codes have been shown to be capable of achieving capacity-approaching performance with iterative message-passing decoding. In the first part of this paper, using asymptotic methods to obtain lower bounds on the free distance to constraint length ratio, we show that several ensembles of regular and irregular LDPC(More)
— Low-density parity-check convolutional codes offer the same good error-correcting performance as low-density parity-check block codes while having the ability to encode and decode arbitrary lengths of data. This makes these codes well suited to certain applications, such as forward error control on packet switching networks. In this paper we propose a(More)
— In this paper asymptotic methods are used to form lower bounds on the free distance to constraint length ratio of several ensembles of regular, asymptotically good, protograph-based LDPC convolutional codes. In particular, we show that the free distance to constraint length ratio of the regular LDPC convolutional codes exceeds that of the minimum distance(More)