Abstract Low-density parity-check (LDPC) codes, particularly nonbinary LDPC (NB-LDPC) codes, are essential for meeting the high peak data rate requirements of the 6th generation systems. However, challenges arise from correlated noise, caused by factors like seasonal variations and inaccuracies in signal-to-noise ratio estimation, which hinder their practical deployment. In this paper, we propose a convolutional neural network-aided iterative detection and decoding (CNN-IDD) method for NB-LDPC coded modulation systems, aiming to enhance performance over correlated noise channels with a slight increase in complexity. In this system, a convolutional neural network (CNN) is integrated with iterative hard-reliability-based algorithms to learn from the correlated noise. The CNN and the hard-decision decoder interact iteratively to mitigate noise effects and improve estimation accuracy. Simulation results show that the proposed scheme achieves up to 1 dB improvement in performance, while maintaining the low complexity advantage of traditional hard-reliability decoding for high-order modulation in NB-LDPC systems over correlated noise channels.
Abstract Low-density parity-check (LDPC) codes, particularly nonbinary LDPC (NB-LDPC) codes, are essential for meeting the high peak data rate requirements of the 6th generation systems. However, challenges arise from correlated noise, caused by factors like seasonal variations and inaccuracies in signal-to-noise ratio estimation, which hinder their practical deployment. In this paper, we propose a convolutional neural network-aided iterative detection and decoding (CNN-IDD) method for NB-LDPC coded modulation systems, aiming to enhance performance over correlated noise channels with a slight increase in complexity. In this system, a convolutional neural network (CNN) is integrated with iterative hard-reliability-based algorithms to learn from the correlated noise. The CNN and the hard-decision decoder interact iteratively to mitigate noise effects and improve estimation accuracy. Simulation results show that the proposed scheme achieves up to 1 dB improvement in performance, while maintaining the low complexity advantage of traditional hard-reliability decoding for high-order modulation in NB-LDPC systems over correlated noise channels.
This paper presents the novel Stochastic decoding-convolutional neural network (SD-CNN) structure, with the goal of enhancing 5G LDPC code’s decoding efficiency in the presence of correlated noise of the channel. Applying the stochastic approach acts as an alternate technique to fixed-point LDPC decoding to enhance the decoder’s hardware efficiency. In order to improve the efficacy of the decoder, we adopted deep learning method such as Convolutional neural network (CNN). CNNs can be employed for denoising purposes in communication systems, where signals may be compromised by diverse forms of noise while being transmitted, with the aim of enhancing signal quality and dependability. The SD-CNN architecture combines a trained CNN with a stochastic decoder of 5G LDPC codes, thereby utilizing the CNN’s capability to accurately estimate channel noise and, consequently, enhance the error correction capabilities of the decoder. The generated output of the trained CNN is then fed back into the stochastic decoder, creating an iterative process between the SD and CNN that ultimately leads to superior decoding performance. For 5G LDPC code word lengthN=3808, with a base code rateR=1/3, the suggested SD-CNN architecture achieves a BER of10−6at 0.6 dB of SNR per bit in the strong correlation of channel noise condition, in comparison to SD, which achieves a BER of10−6at 3.5 dB of SNR per bit. The results demonstrate that there is a 2.9 dB improvement.
With the deployment of 5G networks, the Internet of Things (IoT) has experienced a transformative boost, enabling higher data rates, reduced latency, and the connection of millions of devices across applications like smart cities, healthcare, and industrial automation. However, in real-world scenarios, the performance of Low-Density Parity-Check (LDPC) codes, the preferred channel coding scheme in 5G, is severely affected by noise and fading environments, particularly colored noise, which distorts signals over certain frequency bands. Colored noise introduces correlation in the interference, unlike white noise, thereby posing a challenge in decoding, especially in fading channels such as Rayleigh, Rician, and Nakagami-m. In this work, we propose a novel approach that combines the Iterative Offset Min-Sum (OMS) algorithm with a Convolutional Neural Network (CNN) to enhance LDPC decoding efficiency in 5G-enabled IoT networks. Our proposed OMS-CNN hybrid architecture addresses the limitations imposed by colored noise in fading channels by employing deep learning techniques for accurate noise estimation and mitigation. Furthermore, the OMS algorithm mitigates the overestimation of noise correction, refining the output in iterative decoding steps. Through comprehensive simulations, the OMS-CNN decoder demonstrates substantial improvements over traditional decoding approaches. Specifically, it achieves a performance enhancement of 2.7 dB at a bit error rate (BER) of [Formula: see text] across a range of fading channels. The study examines the decoder's performance in environments characterized by Rayleigh, Rician, and Nakagami-m fading models, highlighting the robustness of the proposed solution under different channel conditions. Additionally, this research explores the influence of parameters such as the correlation coefficient of the noise, the scaling factor in the cost function, and the number of iterations between the CNN and OMS decoding steps.
The tremendous progress of 5G technology has transformed the landscape of the Internet of Things (IoT), allowing for fast data speeds, low delay, and widespread connection that is crucial for a variety of applications, including smart cities and industrial automation. In the context of 5G enabled IoT networks, colored noise introduces varying levels of interference across different frequency bands, which can significantly degrade the performance of 5G LDPC decoding. This paper presents a novel Deep learning approach for 5G channel LDPC code decoding tailored for next-generation IoT applications. The proposed method integrates an Iterative Normalized Min-Sum (NMS) algorithm with a Convolutional Neural Network (CNN) to enhance the performance of LDPC decoding in the presence of colored noise, a common interference in real-world communication channels. Through extensive simulations and analysis, our approach demonstrates a significant performance improvement, achieving a 3.8 dB enhancement at a Bit error rate of10−6. This is achieved by accurately estimating and mitigating channel noise, thereby ensuring reliable data transmission for critical IoT applications. The findings indicate that our approach to decoding technique not only enhances error correction capabilities but also adapts to varying channel conditions, optimizing IoT network performance and efficiency. This research contributes a robust solution to the challenges posed by colored noise in 5G-enabled IoT networks, promoting the deployment of more reliable and efficient IoT systems.
Non-binary low-density parity-check (NB-LDPC) codes tend to exhibit much better performance than their binary counterparts while suffering from very high computational complexity. As a type of hard-decision NB-LDPC decoding algorithm, the iterative reliability-based majority-logic decoding (IRB-MLGD) algorithms are attractive for their low computation complexity, coming with obvious performance degradation. Aiming at improving the error-correction performance, in this paper, we introduce few redundant bits to the original NB-LDPC encoding and the corresponding checksum operations to the improved iterative soft reliability-based (IISRB)-MLGD. Based on the joint coding and decoding method, the obtained redundancy-aided (RA)-IISRB decoding algorithm shows significant performance improvement while maintaining the low-complexity property. Simulation results demonstrate that the proposed algorithm can achieve a more than 1.0dB extra coding gain over the original IISRB with a slight decrease of code rate.
Recently, an iterative belief propagation-convolutional neural network (BP-CNN) decoder has been proposed to process the low-density parity-check (LDPC) decoding under correlated noise. However, in many practical scenarios, correlated noise may have extra special characteristics, such as impulse. Therefore, when used in such scenarios, there will be some space for the performance improvement of the BP-CNN decoder, since its loss functions cannot make full use of the special characteristics of correlated noise. Fortunately, the impulsive property of correlated noise can be effectively described by the symmetric α-stable (SαS) distribution model. Thus, we propose a novel loss function to train a well-behaved CNN model under correlated noise with SαS distributions. In order to take advantage of the features of SαS distributions captured by CNN, the proposed loss function involves a probability density function (PDF) estimation of the residual noise and a similarity detection. The similarity detection uses the Kullback-Leibler (KL) divergence method to compare the similarity between the estimated PDF and the Gaussian distribution. In the BP-CNN decoder, the residual noise is defined as the difference between the actual noise and the estimated noise. The effectiveness of our modifications for the BP-CNN decoder will be demonstrated with simulation results.
Today's modern wireless communication systems demand extensive quality and computational power with an increase in day-to-day data-rate standards. The latest com-munication standards focus on low latency, high throughput, and reliable error correction capabilities which inspired us to explore the 5G New Generation (NR) technology. Low-Density Parity-Check Codes (LDPC) are known to have error correction capabilities that can achieve Shannon's maximum channel ca-pacity. Encoding of LDPC Codes features a sparse property that enables effortless decoding at the receiver end. Advancements in deep learning have helped in estimating the channel noise more accurately. The Iterative Belief Propagation-Convolutional Neural Network (BP-CNN) architecture involves a CNN block which is trained with a quadratic loss function concatenated to a standard BP Decoder. The proposed Iterative BP-CNN architecture with a Batch Normalization layer results in a better performance compared to the Iterative BP-CNN architecture as it standardizes the input to the CNN block and also solves the problem of vanishing gradients during backpropagation.