In this correspondence, we propose an approach to construct irregular low-density parity-check (LDPC) codes based on quasi-cyclic extension. When decoded iteratively, the constructed irregular LDPC codes exhibit a relatively low error floor in the high signal-to-noise ratio (SNR) region and are subject to relatively few undetected errors. The LDPC codes constructed based on the proposed scheme remain efficiently encodable

A general framework is provided to characterize the link level performance of CDMA systems with interference cancellation. This closed-form residual power analysis accounts for the impact of channel estimation errors due to SNR, channel variation, chip asynchronism, and filter mismatch. Simulations further quantify the link level cancellation performance on more realistic sub-chip multipath channels. This work demonstrates that properly designed channel estimation and signal reconstruction techniques achieve high cancellation efficiency over a variety of multipath fading channels.

In this paper, we study the performance of different graph-based error-correcting codes over Gilbert–Elliot channels. We propose a hybrid coding scheme in which each code bit is checked by both a parity check and a Hamming code. A hybrid code can be represented by a code-to-code graph, which can be used to optimize the code. Asymptotic minimum distance properties of the hybrid code are derived, and it is shown that the expected minimum distance of the hybrid code increases linearly with respect to the code length. Simulation results show that for a typical Gilbert–Elliott channel, hybrid codes can outperform regular low-density parity-check codes by more than an order of magnitude, in terms of bit-error rate.

In this paper, we study the performance of different graph-based error-correcting codes over Gilbert-Elliott (GE) channels. We propose a hybrid coding scheme in which each code bit is checked by both a parity-check code and a Hamming code. A hybrid code can be represented by a code-to-code graph, which can be used to optimize the code. Asymptotic minimum distance properties of the hybrid code are derived, and it is shown that the expected minimum distance of the hybrid code increases linearly with respect to the code length. Simulation results show that for a typical GE channel, hybrid codes can outperform regular low-density parity-check codes by more than an order of magnitude, in terms of bit-error rate.

In this paper, we apply two improved min-sum algorithms, the normalized and offset min-sum algorithms, to the decoding of irregular LDPC codes. We show that the behavior of the two algorithms in decoding irregular LDPC codes is different from that in decoding regular LDPC codes, due to the existence of bit nodes of degree two. We analyze and give explanations to the difference, and propose approaches to improve the performance of the two algorithms.

Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.

This paper presents the study of transmit power adaption and capacity-approaching coding/decoding in multiple-input-multiple-output (MIMO) Rayleigh fading channels under the assumption that perfect channel state information (CSI) is known at both the transmitter and the receiver. We propose three different simple, but powerful, methods for transforming the MIMO fading channel into a set of additive white noise Gaussian (AWGN) channels. We show that the channel capacity can be closely approached by using only a small number of different codes designed for the Gaussian channel.

In this paper, we analyze the performance of two improved BP-based decoding algorithms for LDPC codes, namely the normalized BP-based and the offset BP-based algorithms, by means of density evolution. The numerical calculations show that with one properly chosen parameter for each of these two improved BP-based algorithms, performances very close to that of the BP algorithm can be achieved. Simulation results for LDPC codes with code length moderately long validate the proposed optimization. Finite quantization effects on the BP-based and the offset BP-based decoding algorithms are evaluated.

In this paper, we propose a belief-propagation (BP)-based decoding algorithm which utilizes normalization to improve the accuracy of the soft values delivered by a previously proposed simplified BP-based algorithm. The normalization factors can be obtained not only by simulation, but also, importantly, theoretically. This new BP-based algorithm is much simpler to implement than BP decoding as it requires only additions of the normalized received values and is universal, i.e., the decoding is independent of the channel characteristics. Some simulation results are given, which show this new decoding approach can achieve an error performance very close to that of BP on the additive white Gaussian noise channel, especially for low-density parity check (LDPC) codes whose check sums have large weights. The principle of normalization can also be used to improve the performance of the max-log-MAP algorithm in turbo decoding, and some coding gain can be achieved if the code length is long enough.

We propose a normalized a posteriori probability (APP) based algorithm for the decoding of low-density parity check (LDPC) codes. The normalized APP-based algorithm utilizes normalization to improve the accuracy of the soft values delivered by the simplified APP-based algorithm from one iteration to another during the iterative decoding, and can achieve very good tradeoff between decoding complexity and performance.