Channel Coding Research Articles

RIS-Based Over-the-Air Diffractional Channel Coding

RIS-Based Over-the-Air Diffractional Channel Coding

Low-Latency Deep Analog Speech Transmission Using Joint Source Channel Coding

Low-Latency Deep Analog Speech Transmission Using Joint Source Channel Coding

EXIT Charts for Low-Density Algebra-Check Codes.

This paper focuses on the Low-Density Algebra-Check (LDAC) code, a novel low-rate channel code derived from the Low-Density Parity-Check (LDPC) code with expanded algebra-check constraints. A method for optimizing LDAC code design using Extrinsic Information Transfer (EXIT) charts is presented. Firstly, an iterative decoding model for LDAC is established according to its structure, and a method for plotting EXIT curves of the algebra-check node decoder is proposed. Then, the performance of two types of algebra-check nodes under different conditions is analyzed via EXIT curves. Finally, a low-rate LDAC code with enhanced coding gain is constructed, demonstrating the effectiveness of the proposed method.

A Cooperative Communication System With P-LDPC For Internet of Underwater Things

The acoustic communications are extremely challenging in underwater environments due to harsh conditions of this kind of nature. That medium's harshness affects the transmission reliability which in turn crucially requires an adoption of robust channel codes based on cooperative approaches to attain a more reliable data communication. This study proposes a cooperative communication system based on a protograph low-density parity check (P-LDPC) code. The system is elaborated in an Internet of underwater things (IoUT) scenario in which a relay node is acting as a helper that provides side information to destination. Compression and channel coding are offered by the system to cope with requirements of IoT in terms of reliable communication and compressed data between nodes. Furthermore, an algorithm for joint source channel coding is presented based on a modified version of offset min-sum. Moreover, layered decoding is Implemented in order to minimize number of iterations for the P-LDPC code. The results prove that proposed design can achieve a bit error rate (BER) of 5 × 10−6 at signal to noise ratio SNR = 8 dB for a coding rate of 1/3 and block length of 1500 bits. In addition, the proposed system shows a coding gain of 3.6 dB when compared with a non cooperative (no relay) approach. Besides, the proposed cooperative design can offer reduced computations versus the non cooperative system to achieve the same BER at a fixed SNR.

Joint Communication and Channel Discrimination.

We consider a basic joint communication and sensing setup comprising a transmitter, a receiver and a sensor. The transmitter sends a codeword to the receiver through a discrete memoryless channel, and the receiver is interested in decoding the transmitted codeword. At the same time, the sensor picks up a noisy version of the transmitted codeword through one of two possible discrete memoryless channels. The sensor knows the codeword and wishes to discriminate between the two possible channels, i.e., to identify the channel that has generated the output given the input. We study the trade-off between communication and sensing in the asymptotic regime, captured in terms of the channel coding rate against the two types of discrimination error exponents. We characterize the optimal trade-off between the rate and the exponents for general discrete memoryless channels with an input cost constraint.

КӨП ШЕКТІ ДЕКОДЕРДЫ ҚОЛДАНУ АРҚЫЛЫ ЦИФРЛЫҚ РАДИОЖҮЙЕЛЕРДІҢ ЭНЕРГИЯ ТИІМДІЛІГІН АРТТЫРУ ӘДІСТЕРІН ӘЗІРЛЕУ

Energy efficiency in digital radio systems is one of the key challenges in modern communication systems. This is especially relevant in fading environments, where it is necessary to minimize energy consumption while maintaining high data transmission quality. The goal of this research is to develop and evaluate methods for improving the energy efficiency of digital radio systems using multi-threshold decoding (MTD) and convolutional coding. The efficiency of these methods was tested under various operating conditions of radio channels. Effective convolutional codes were selected for MTD, which ensure a high level of data protection during transmission. The study evaluated the energy efficiency of these codes in channels with additive white Gaussian noise. The convolutional coding methods allowed for significant improvements in MTD performance, which led to increased reliability of data transmission. Additionally, iterative demodulation and decoding methods were used, enabling the correction of errors with each iteration. This approach contributed to a significant increase in data transmission accuracy by repeatedly performing the processes of demodulation and decoding, especially in complex conditions. The research results showed that the proposed methods allowed for a 12-18% reduction in system energy consumption. Furthermore, with a signal-to-noise ratio (SNR) of 10 dB, the bit error rate (BER) decreased from 10⁻³ to 8×10⁻⁴, improving error correction efficiency by 15-20%. The accuracy of data transmission in fading environments also improved by 10-15%, enhancing the overall reliability of the system. The practical significance of this research is that the proposed methods can be successfully applied in modern radio systems to increase their energy efficiency and improve communication quality, particularly in challenging transmission conditions.

Capacity Achieving Channel Codes for an Erasure Queue-Channel

Capacity Achieving Channel Codes for an Erasure Queue-Channel

New Design of RS-GRP LDPC Concatenated Codes for Block Fading Channel

New Design of RS-GRP LDPC Concatenated Codes for Block Fading Channel

Theoretical and experimental analysis on ghost imaging with channel coding theorem

Ghost imaging is analyzed from the perspective of information theory, specifically the channel coding theorem. In the theoretical framework, ghost imaging is viewed as a communication process. The concept of channel capacity is used to determine the minimum number of samplings required to achieve an error-free ghost imaging process. Further study reveals that both conventional imaging and ghost imaging processes can be analyzed within the same theoretical framework. The theoretical analysis shows that the quality of ghost imaging can be improved by employing multiple bucket detectors. These predictions are validated through both simulations and experiments.

Retraction Note: Quantum optics and channel coding in imaging: advancements through deep learning

Retraction Note: Quantum optics and channel coding in imaging: advancements through deep learning

Function-Correcting Codes for Symbol-Pair Read Channels

Function-Correcting Codes for Symbol-Pair Read Channels

Task-Oriented Scene Graph-Based Semantic Communications With Adaptive Channel Coding

Task-Oriented Scene Graph-Based Semantic Communications With Adaptive Channel Coding

Broadcast Channel Coding: Algorithmic Aspects and Non-Signaling Assistance

Broadcast Channel Coding: Algorithmic Aspects and Non-Signaling Assistance

Fusion of self-attention mechanism for CSI feedback in massive MIMO systems

In massive MIMO systems, obtaining accurate channel state information (CSI) is crucial for optimal channel coding and beamforming. However, traditional CSI feedback methods require high bandwidth and also consume a large amount of power and computing resources. To address these challenges, several compressed sensing-based techniques have been implemented in recent years. These techniques, however, are often iterative and computationally complex to implement in power-constrained user equipment. In this paper, we propose a novel fusion of the self-attention mechanism, called FSAMNet, to efficiently and accurately implement the CSI feedback task for massive MIMO systems. Our proposed FSAMNet adopts both the residual connections in the attention mechanism and a sequence of depth-separable convolutional layers to enhance the model’s performance and expressive ability. Specifically, we apply a multi-layer self-attention mechanism in the encoder part to achieve feature extraction and compression. In the decoder part, we use multiple convolutional layers and self-attention mechanisms to convert the embedding vector generated by the encoder back into the original image. Experimental results show that the performance of our proposed FSAMNet outperforms conventional benchmark schemes in terms of feedback network performance.

Hybrid Window Decoding for Joint Source Channel Anytime Coding System.

Joint source channel anytime coding (JSCAC) is a kind of joint source channel coding (JSCC) systems based on the causal spatially coupled coding and joint expanding window decoding (JEWD) techniques. JSCAC demonstrates greatly improved error correction performance, as well as higher decoding complexity. This work proposes a joint hybrid window decoding (JHWD) algorithm for JSCAC systems, aiming to reduce the decoding complexity while maintaining comparable error correction performance with the state of the art. Unlike the traditional JEWD technique and its variants, the proposed JHWD algorithm utilizes a hybrid window structure. It achieves this by implementing adaptive local expanding window decoding in the sliding window region, guided by syndrome-check-based detection. The hybrid window decoding characteristics of the proposed JHWD algorithm can both effectively reduce the decoding complexity caused by window expanding, and also mitigate the error propagation caused by window sliding. An improved density evolution algorithm is proposed for the asymptotic performance analysis of the proposed JHWD algorithm. Both the analytical and simulation results indicate that the proposed JHWD decoding scheme is a good low-complexity implementation option for JSCAC systems, which is also suitable for other JSCC systems with the spatially coupled coding structure.

Symbol detection aided channel prediction in fast‐varying massive MIMO systems: Framework and performance analysis

AbstractThe channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

Deep learning-driven channel coding: enhancing performance in LDS-CDMA and NOMA systems

This paper presents an innovative approach to channel coding for LDS-CDMA and NOMA systems through the integration of deep learning techniques. By leveraging neural networks within the channel coding process, the proposed model achieves substantial improvements in error correction, leading to a significant reduction in Bit Error Rate (BER) and computational complexity when compared to traditional methods such as Unpunctured Turbo Trellis Coded Modulation (UTTCM). The results demonstrate that the deep learning-based system is particularly effective in challenging communication environments, such as those with low Signal-to-Noise Ratios (SNR) and high levels of multi-user interference, where it outperforms conventional coding techniques. This research underscores the potential of deep learning to enhance the efficiency and adaptability of wireless communication systems, particularly as networks evolve towards 6G and beyond. The flexibility offered by this approach allows for improved handling of dynamic and complex environments, ensuring higher data throughput and reduced latency. Future work may focus on implementing the proposed model in real-time applications, as well as investigating its synergy with emerging technologies like massive MIMO and NOMA, which could lead to even greater performance enhancements in next-generation communication systems.

Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques

Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques

Jointly polar-coded probabilistic shaping scheme based on many-to-one mapping in IM/DD optical DMT interconnection.

A joint channel coding and probabilistic shaping (PS) scheme is proposed and experimentally demonstrated in an intensity modulation and direct-detection optical discrete multi-tone (DMT) interconnection. The PS-32 quadrature amplitude modulation (PS-32QAM) is probabilistically reshaped from a 6-bit/symbol 64QAM signal based on polar-coded many-to-one (MTO) mapping, enabling a Gaussian-like symbol probability distribution without shaping or forward error correction redundancy. A bit-interleaved polar-coded modulation iterative decoding system based on joint QAM demapping and polar decoding is employed to retrieve the multi-labeled PS symbols. Experimental results indicate that the polar-coded PS-32QAM DMT signal achieves a 1.5-dB receiver power sensitivity improvement and a 2.71-dB fiber nonlinearity tolerance enhancement over 10-km standard single-mode fiber transmission.

A novel superposition coding scheme based on polar code for multi‐user detection in underwater acoustic OFDM communication system

SummarySuperposition coding (SC) is a non‐orthogonal multiple access (NOMA) scheme for the downlink communication system, which allows signals of different users to be stacked and forwarded simultaneously in the same frequency band. The codeword for each user is assigned a different weight at the transmitter to avoid signal conflicts and interference after stacking, and successive interference cancellation (SIC) is adopted to decode. A novel SC scheme based on polar code, suitable for the downlink multi‐user underwater acoustic (UWA) communication system using orthogonal frequency division multiplexing (OFDM), is proposed in this paper. Polar code is utilized to create a nested code structure that is divided into several subsets. We exploit this feature of the polar codes and assign each subset to the corresponding user in the channel coding process. The information bits are placed on the independent subset for each user, and the random bits are placed on the subset of other users while all the users share the same set of frozen bits. Then, the codewords are stacked with different weights. The selection range of power factors can be expanded through the double superposition of the coding and the power domains, and higher system throughput and fairness can be achieved. The simulation and tank experiment results significantly verify the effectiveness of the proposed scheme, making it most suitable for the downlink UWA multi‐user communication systems.