Symbol Flipping Research Articles

Assistant vehicle locating based on DOA estimation of deep unfolded network

In view of the decline in the positioning accuracy of the vehicles with small snapshots, this paper proposes a direction of arrival(DOA) estimation method based on deep unfolded network to assist the global positioning system to achieve high-precision locating. Firstly, the signal emitted from the on board unit installed on the vehicle is received by the array antenna of the road side unit(RSU), then RSU transmits the array data to cloud service platform, in which the traditional fixed point continuation algorithm is improved by using l1 norm instead of l2 norm, and the adaptive outlier point tracking algorithm is used to deal with the problem of symbol flipping caused by noise. Then we unfold the improved fixed point continuous algorithm for obtaining a neural network. In order to improve the DOA estimation performance and convergence speed, this paper uses the black widow optimization algorithm to determine the soft threshold shrinkage parameters. According to the mapping between the signal and the array manifold matrix, the DOA of the target is estimated, and its location can be evaluated from the geometry relation between vehicle and RSU. The simulation results show that the algorithm proposed in this paper has better direction finding performance and improves the vehicle positioning accuracy under small snapshots and high signal-to-noise ratio effectively.

Predictive Syndrome Based Low Complexity Joint Iterative Detection-Decoding Algorithm for Non-Binary LDPC Codes

This paper addresses the problem of decoding non-binary low density parity check codes(LDPC) over finite field GF(q) using symbol flipping approach. To achieve low complexity reliable communication, three new algorithms for improving the bit error rate performance of the non-binary LDPC decoder are presented. The first type is the symbol flipping decoding algorithm using a flipping function based on the channel reliability to identify the least reliable symbol position. In this algorithm, if the predicted symbol value satisfies the check sum, then the value is declared as correct otherwise the value is adjusted and sent back to the QAM detector. Algorithms 2 in this paper is an improvement to iterative joint detection-decoding algorithm by using the method of iterative hard decision based majority logic to select the new candidate symbol value. The feedback value to the QAM detector is adjusted by using Euclidean distance between the current symbol and the newly selected symbol value. Algorithm 3 is a low complexity version of Algorithm 2 which is derived by applying a majority voting scheme. In the majority voting scheme, symbols are short listed first by voting and all the computation are carried out only for the short listed least reliable symbols which significantly lowers the processing complexity. Numerical results and complexity analysis show that the proposed methods have good bit error rate versus complexity trade-off for various applications when compared with some existing algorithms.

Reliability-Based Signal Detection for Universal Filtered Multicarrier

In this letter, the reliability-based signal detection is proposed for universal filtered multicarrier (UFMC) systems. In the proposed detector, first, the initial subcarrier-wise estimation utilizing the even-component of frequency-domain (FD) received signal is performed, and the reliabilities of the estimated signals are measured by the corresponding FD channel magnitudes. Second, the estimated signals with high reliabilities are accepted and peeled off from the received signal. Third, the signals with low reliabilities are re-estimated by two different methods, symbol-flipping (SF) and joint minimum mean square estimation (MMSE), utilizing the additional odd-component of FD received signal after peeling off. Specifically, one or two of these unreliable signals are flipped simultaneously in turn from the initial estimates to their neighbors in the SF re-estimation, and all the unreliable signals are jointly estimated by MMSE in the joint MMSE re-estimation. Simulation results show that the proposed detection can achieve better performance-complexity tradeoff than conventional UFMC detections.

An Improved Symbol-Flipping Algorithm for Nonbinary LDPC Codes and its Application to NAND Flash Memory

This paper proposes a novel symbol-flipping decoding (SFD) algorithm called decision-symbol reliability-based SFD (DRB-SFD) algorithm for nonbinary low-density parity-check (LDPC) codes aiming to improve the error-rate performances of data storage devices with hard-decision channel outputs, e.g., data storage using NAND flash memory. The proposed algorithm generates the reliability information of decision symbols based on a metric for symbol flipping during iterations instead of soft-decision channel outputs. In addition, it quantizes the reliability information into reliability messages that are exchanged between variable and check nodes. The number of quantization levels is carefully chosen to be the same as the field size of coded symbols, which allows the message exchanges to be performed without the additional signal paths. It is also extensively discussed how to decide parameters in the proposed algorithm in an analytic way. We demonstrate that the proposed algorithm featured with the exchanges of reliability messages provides significant performance improvements over existing SFD algorithms on channels with hard-decision outputs.

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection.

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Corrections on “Symbol Flipping Decoding Algorithms Based on Prediction for Non-Binary LDPC Codes” [May 17 1913-1924

Due to a production error, an equation in the above paper [1] appeared incorrectly. Below is the correct version.

Symbol Flipping Decoding Algorithms Based on Prediction for Non-Binary LDPC Codes

This paper constructs an objective function for symbol flipping decoding algorithms, considering not only soft reliability, but also hard reliability. The maximization of this objective function indicates that the flipping metric should involve both the information before and after symbol flipping, while the existing algorithms consider the information before symbol flipping. Theoretical analysis shows that such prediction mechanism, together with hard reliability, can significantly improve the error performance of symbol flipping algorithms. Simulation results show that the proposed algorithms provide effective tradeoff between error performance and complexity for decoding non-binary LDPC codes.

Non-Binary LDPC Decoder Based on Symbol Flipping with Multiple Votes

In this letter, a new algorithm to decode non-binary LDPC (NB-LDPC) codes is proposed. This algorithm is inspired from very low complexity decoders that have been proposed recently, in which only syndrome computations at the check node update are used, while performing symbol-flipping based update at the variable node. Usually, the low complexity decoders based on symbol flipping suffer from a non-negligible performance degradation compared to soft-decision NB-LDPC decoders. Our improved decoder makes use of a list of syndrome computations instead of a single one based on hard-decision, and builds soft information at the variable node input by assigning votes weighted by different amplitudes. Simulations show that using multiple votes with multiple weights yields better performance, while still maintaining the low complexity feature.