Quantized Turbo Equalization for Non-binary LDPC Coded Partial-Response Channels

In this paper, we consider iterative detection and decoding (i.e., turbo equalization) for nonbinary low-density parity-check (LDPC) coded partial-response channels, where a quantizer is present to discretize the continuous received signal. We propose a turbo equalizer that uses the pre-computed quantized channel transition probabilities in the symbol-level BCJR channel detection algorithm, which significantly reduces the computational complexity by avoiding real-time floating-point multiplications. The proposed approach is further extended to nonbinary LDPC coded bit-patterned media recording (BPMR) channels. Simulation results show that with a small number of quantization bits, the proposed receiver approaches closely the performance of the conventional turbo equalizer operating on unquantized signals.

Graph-Based Detection and LDPC Decoding over 2D Intersymbol Interference Channels

In this paper, we propose a fully graph-based iterative detection and decoding scheme for Low-Density Parity-Check (LDPC) coded generalized two-dimensional (2D) intersymbol interference (ISI) channels. The 2D detector consists of a downtrack detector based on the symbol-level sum-product algorithm (SPA) and a bit-level SPA-based crosstrack detector. A LDPC decoder based on simplified check node operations is also proposed to provide soft information for the 2D channel detector. Numerical results show that the proposed receiver achieves better performance as compared with the trellis-based BCJR detector over 2×2 2D channels while at a significantly lower computational complexity.

Continuous phase modulation with 1-bit quantization and oversampling using iterative detection and decoding

A channel with continuous phase modulation and 1-bit ADC with oversampling is considered. Due to oversampling, higher-order modulations yield a higher achievable rate and this study presents methods to approach this with sophisticated channel coding. Iterative detection and decoding is considered, which exploits the soft information extracted from oversampled CPM sequences. Besides the strategy based on conventional channel coding techniques, a tailored bit mapping is proposed where two bits can be detected reliably at high SNR and only the third bit is affected by the uncertainties from the coarse quantization. With this, a convolutional code is only applied to the third bit. Our experiments with a turbo receiver show that the iterative detection and decoding is a promising approach for exploiting the additional information brought by oversampling. Moreover, it is shown that the proposed method based on the tailored bit mapping corresponds to a lower bit error rate in comparison with the case of conventional channel coding.

A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration

Polar coding and sparse code multiple access (SCMA) are key technologies for 5G mobile communication, the joint design of them has a great significance to improve the overall performance of the transmitter-receiver symmetric wireless communication system. In this paper, we firstly propose a pruning iterative joint detection and decoding algorithm (PI-JDD) based on the confidence stability of resource nodes. Branches to be updated are dynamically pruned to avoid redundant iterative, which is able to reduce 24~50% complexity while achieving the approximate error performance of traditional serial joint iterative detection and decoding algorithm S-JIDD. Then, to further reduce the bit error rate (BER) of the receiver, a cyclic redundancy check (CRC) termination mechanism is added at the end of each joint iteration to avoid the convergence error caused by decoding deviation. Simulation results show that the addition of an early stopping criterion can achieve a remarkable performance gain compared with the S-JIDD algorithm. More importantly, the combined algorithm of the two proposed schemes can reduce the computational complexity while achieving better error performance.

A Low-Complexity Double EP-based Detector for Iterative Detection and Decoding in MIMO

<div> <div> <div> <p>We propose a new iterative detection and decoding algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE), as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, i.e., the same as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases </p> </div> </div> </div>