Channel Decoding Research Articles

Agile Detection of DRM Signal via GLRT

AbstractDigital Radio Mondiale (DRM) is a digital Orthogonal Frequency Division Multiplexing (OFDM) broadcasting standard that has been employed all over the world. The operating carrier frequency and time of DRM station change with the scheduling period. The user terminal commonly uses channel decoding and audio decoding to detect the DRM radio. This conventional signal detection method always fail when the station is not working, or the signal‐to‐noise ratio (SNR) is weak, or the designated frequency band is illegally occupied. Besides, the conventional method needs at least one DRM transmission super frame with a duration of 1.2 s, which causes delay and brings additional computations. To solve the challenges faced by conventional method, this paper has proposed an agile and efficient signal detection method based on the Generalized Likelihood Ratio Test. The proposed method coherently integrates the frequency pilots of successive OFDM symbols via discrete Fourier transform, then propose a sufficient statistic to detect the DRM radio. The required number of OFDM symbols, which is much smaller than that of one transmission super frame, is adaptively chosen from the given probabilities of false alarm and correct detection. The computations and SNR requirement of the proposed method are both smaller than the conventional method, which help the user terminal quickly detect the DRM signal over the given frequency band. The proposed method also provides a new perspective for electromagnetic spectrum management.

Data-Driven Neural Polar Decoders for Unknown Channels With and Without Memory

Data-Driven Neural Polar Decoders for Unknown Channels With and Without Memory

Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.

Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.

Design of a turbo-based deep semantic autoencoder for marine Internet of Things

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

High-performance decoding using deep neural networks

Problem statement. Deep neural networks are successfully used in channel coding to improve the quality of decoding. However, modern neural channel decoders cannot achieve high decoding performance and low complexity at the same time. To overcome this problem, it is necessary to develop a decoder with a double residual neuron. By integrating residual input and residual learning into the neural channel decoder architecture, it will significantly improve decoding performance while maintaining low complexity. Goal. Development of a decoder architecture with a dual residual neural network, which provides high decoding performance in channel coding with low model complexity and computational costs. Results. The simulation results show that the proposed decoder with a double residual neural network provides a significant increase in decoding performance. Compared to a neural decoder based on a trust propagation algorithm, a decoder with a dual residual neural network provides an additional 0.5-1.8 dB encoding gain compared to different channel codes. Compared to the hypergraph neural decoder, which has the best error correction capability among all existing neural channel decoders, the dual residual neural network decoder also provides similar or even better decoding performance for different channel codes. Practical significance. The results obtained can be used to improve decoding efficiency and improve performance in channel coding. The work was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation within the framework of the state assignment "youth laboratory" No. FZGM-2024-0003.

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

SummaryAs a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

Multi-channel polarization manipulation based on graphene for encryption communication

Wave-based cryptography, at the vanguard of advancing technologies in advanced information science, is essential for establishing a diverse array of secure cryptographic platforms. The realization of these platforms hinges on the intelligent application of multiplexing techniques, seamlessly combined with appropriate metasurface technology. Nevertheless, existing multi-channel encryption technologies based on metasurfaces face challenges related to information leakage during partial channel decoding processes. In this paper, we present a reprogrammable metasurface for polarization modulation. This metasurface not only allows for the arbitrary customization of linearly polarized reflected waves but also enables real-time amplitude modulation. Here, relying on polarization amplitude control, a fully secure communication protocol is developed precisely in the terahertz (THz) spectrum to achieve real-time information encryption based on polarization modulation metasurfaces where access to information is highly restricted. The proposed metasurface employs the double random phase encryption (DRPE) algorithm for information encryption. It transmits the encrypted data through different polarization channels using two graphene nanoribbons, exclusively controlled by external biasing conditions. Various encryption scenarios have been outlined to fortify information protection against potential eavesdroppers. The simulated results show that this unique technology for hiding images by manipulating the polarization of the reflected wave provides new opportunities for various applications, including encryption, THz communications, THz secure data storage, and imaging.

Improved Quantum Approximate Optimization Algorithm for Low‐Density Parity‐Check Channel Decoding

AbstractQuantum computing shows promise for 6G networks due to its parallel computing capabilities. In the context of the Noisy Intermediate‐Scale Quantum era, the introduction of hybrid quantum‐classical algorithms like Quantum Approximate Optimization Algorithm (QAOA) offer powerful solutions to many combinatorial optimization problems in 6G. This paper focuses on Low‐Density Parity‐Check (LDPC) channel decoding and proposes an improved QAOA algorithm assisted by the learning‐to‐learn strategy. We also investigate the parameter concentration phenomenon in QAOA‐based LDPC decoding to assess the rationality. To evaluate effectiveness, a comprehensive numerical expression for the energy expectation of single‐layer QAOA and propose indicators for transfer performance evaluation is provided. Based on simulation results, the similarity in parameter distribution across specific LDPC configurations is investigated. This similarity facilitates the transfer of training outcomes from smaller to larger‐scale problems for optimization initialization, thereby avoiding the need for retraining. This approach offers insights and potential solutions for rapid, large‐scale channel decoding in 6G networks, despite the current limitations of quantum hardware.

Trapping Sets Search Using the Method of Mixed Integer Linear Programming with a Priori List of Variable Nodes

Purpose of research is to develop a new high-speed method for searching trappin sets in graph codes, ensuring the completeness of the search.Methods. There are two approaches to finding trappin sets. The first, based on the Monte Carlo method with a biased probability estimation using Importance Sampling, involves the use of a decoder. The advantage of this approach is its high performance. The disadvantages are the dependence on decoder parameters and channel characteristics and the finite probability of missing trappin sets. The second approach is based on the use of linear programming methods. The advantage of this approach is the completeness of the resulting list of trappin sets, due to its independence from the decoder parameters and channel characteristics. The disadvantage of this approach is its high computational complexity. In the article, within the framework of the second approach, a new method for searching trappin sets with less computational complexity is proposed. The method involves solving a mixed integer linear programming problem using an a priori list of code vertices participating in the shortest cycles in the code graph. Results. Using the proposed method, a search for trappin sets was performed for several low-density codes. For this purpose, the mathematical linear programming package IBM CPLEX version 12.8 was used, which was run on 32 threads of a 16-core AMD Ryzen 3950X processor with 32GB of RAM (DDR4). In the Margulis code (2640, 1320), using the proposed method, the trappin set TS(6,6) was found in a time of 0.53 s. The speedup provided by the method proposed in the paper compared to the Velazquez-Subramani method is 8252.415 times. Thanks to the high speed and completeness of the search, trappin sets were found for the first time TS(62,16) and TS(52,14) in the Margulis code (4896, 2474 ).Conclusion. The paper proposes a new method for searching trapping sets by solving a mixed integer linear programming problem with an a priori list of code. The method is fast and provides completeness of the search.

Energy efficient noise error pattern generator for guessing decoding in bursty channels

For the hard guessing random additive noise decoding Markov order (GRAND-MO) algorithm, it is crucial to develop an efficient noise error patterns (NEPs) generator to facilitate its application in bursty channels. This paper proposes a practical hardware realization by generating the NEPs in a sequential manner. Based on classification of the four types of NEPs, we propose to iteratively calculate the “1" and the “0" permutations in the same time. Then, the novel “0" permutation regularization and bit flipping techniques are employed, through which the generation of the four types of NEPs is uniformed at the same way. Moreover, the proposed NEPs generator can generate all NEPs by using the “1" burst parameters, and is suitable for the guessing decoding of any linear block codes. Built on field programmable gate array (FPGA) implementation and comparison with existing benchmark, we show the proposed NEPs generator is a power-efficient architecture for realization. This work presents a new solution for the hardware implementation of the NEPs generator in GRAND-MO.

A denoiser for correlated noise channel decoding: Gated-neural network

A denoiser for correlated noise channel decoding: Gated-neural network

Shared Graph Neural Network for Channel Decoding

With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot. Compared with traditional decoding algorithms, GNN-based channel decoding algorithms have a better performance. GNN has good stability and can handle large-scale problems; GNN has good inheritance and can generalize to different network settings. Compared with deep learning-based channel decoding algorithms, GNN-based channel decoding algorithms avoid a large number of multiplications between learning weights and messages. However, the aggregation edges and nodes for GNN require many parameters, which requires a large amount of memory storage resources. In this work, we propose GNN-based channel decoding algorithms with shared parameters, called shared graph neural network (SGNN). For BCH codes and LDPC codes, the SGNN decoding algorithm only needs a quarter or half of the parameters, while achieving a slightly degraded bit error ratio (BER) performance.

Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios

With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of drone power inspection, drone clusters are used as relays for long-distance communication to expand the communication range and achieve data transmission between patrol drones and base stations. Most of the communication occurs in the air-to-air channel between UAVs, which requires high reliability of communication between drone relays. Therefore, the main focus of this paper is on decoding schemes for drone air-to-air channels. Given the limited computing resources and battery capacity of a drone, as well as the large amount of power data that needs to be transmitted between drone relays, this paper aims to design a high-accuracy and low-complexity decoder for LDPC long-code decoding. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization, applying deep learning to traditional LDPC decoding methods. In order to achieve high decoding performance while reducing complexity, this scheme utilizes codebook-based weight quantization and parameter sharing methods to improve the neural-network-normalized minimum sum (NNMS) decoding algorithm. Simulation experimental results show that the proposed method has a better BER performance and low computational complexity. Therefore, the LDPC decoding algorithm designed effectively meets the drone characteristics and the high channel decoding performance requirements. This ensures efficient and reliable data transmission on the data link between drone relays.

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell interference, LA is performed by computing the post-equalization signal-to-interference-noise ratio (SINR) at the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO) receivers that use linear detectors. Another important use of post-equalization SINR is for physical layer (PHY) abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the bit-metric decoding rate (BMDR) of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.

Analysis of Coded Slotted ALOHA With Energy Harvesting Nodes for Perfect and Imperfect Packet Recovery Scenarios

We analyze the performance of Coded Slotted ALOHA (CSA) protocols in scenarios where users are equipped with limited batteries that are recharged through Energy Harvesting (EH). First, we assume a Perfect Packet Recovery Scenario (PPRS) for which the received packets are decoded with no errors when there is no interference. We introduce Battery Outage Probability (BOP) as an extra performance metric; and, we derive the optimal EH-CSA transmission policies, which offer the maximum attainable traffic load while maintaining an asymptotically negligible Packet Loss Ratio (PLR), under specific rate and BOP constraints. We extend our study to Imperfect Packet Recovery Scenario (IPRS) where impairments at the physical layer, including channel estimation and channel decoding errors, will distort messages being passed through the iterative Successive Interference Cancellation (SIC) process. The distorted messages being passed through the SIC process potentially lead to error propagation. In order to track the error propagation process, we define the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accumulated Noise plus Interference Power</i> (ANIP), and analytically track the evolution of its probability distribution. We employ our results to evaluate the bit error rates for different transmission policies for the case of IPRS. We also demonstrate the advantages of the optimal transmission policies through numerical examples for both PPRS and IPRS. Our results show that the optimal EH-CSA policies outperform the policies optimized for standard CSA without EH considerations, and the schemes that are optimal for PPRS are not necessarily optimal for the IPRS case. Furthermore, the EH-CSA optimal policies strictly outperform standard CRDSA when the system is required to support higher traffic loads.

Belief Propagation Optimization for Lossy Compression Based on Gaussian Source

In the Internet of Things, sensor nodes collect environmental information and utilize lossy compression for saving storage space. To achieve this objective, high-efficiency compression of the continuous source should be studied. Different from existing schemes, lossy source coding is implemented based on the duality principle in this work. Referring to the duality principle between the lossy source coding and the channel decoding, the belief propagation (BP) algorithm is introduced to realize lossy compression based on a Gaussian source. In the BP algorithm, the log-likelihood ratios (LLRs) are iterated, and their iteration paths follow the connecting relation between the check nodes and the variable nodes in the protograph low-density parity-check (P-LDPC) code. During LLR iterations, the trapping set is the main factor that influences compression performance. We propose the optimized BP algorithms to weaken the impact of trapping sets. The simulation results indicate that the optimized BP algorithms obtain better distortion–rate performance.

New Structure of Channel Coding: Serial Concatenation of Polar Codes

In this paper, we introduce a new coding and decoding structure for enhancing the reliability and performance of polar codes, specifically at low error rates. We achieve this by concatenating two polar codes in series to create robust error-correcting codes. The primary objective here is to optimize the behavior of individual elementary codes within polar codes. In this structure, we incorporate interleaving, a technique that rearranges bits to maximize the separation between originally neighboring symbols. This rearrangement is instrumental in converting error clusters into distributed errors across the entire sequence. To evaluate their performance, we proposed to model a communication system with seven components: an information source, a channel encoder, a modulator, a channel, a demodulator, a channel decoder, and a destination. This work focuses on evaluating the bit error rate (BER) of codes for different block lengths and code rates. Next, we compare the bit error rate (BER) performance between our proposed method and polar codes.

A comprehensive study on the synchronization procedure in 5G NR with 3GPP-compliant link-level simulator

The 5G New Radio synchronization procedure is the first step that the user must complete to access the mobile network. It mainly consists of the detection of the primary and secondary synchronization signals (PSS and SSS, respectively) and the decoding of the physical broadcast channel (PBCH). Our goal is to provide a comprehensive study of the synchronization procedure and investigate different techniques and approaches, through the implementation of a 5G New Radio-compliant simulator. Of significant interest is the investigation of impairments such as the fading channel, the frequency offset, and the delay spread. The results are provided in terms of detection probability for the PSS and SSS detection, and in terms of block error rate for the PBCH. From the data collected, there is evidence that choosing M-sequences for the PSS leads to an appreciably robust solution against frequency offset. The structure of the Gold sequences for SSS generation can be exploited to reduce the detection complexity, and different approaches can be chosen to improve reliability against delay spread. Moreover, the polar coding for 5G PBCH outperforms the former 4G coding technique, but they are still sensible to frequency offset. Finally, the simulator functionalities are validated through real captures of 5G signals.

Design and evaluation of space‐time coded multiple input multiple output‐orthogonal frequency division multiplexing physical layer

SummaryTwo fundamental aspects of wireless communication, which make it challenging and interesting, are fading and interference. Multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques when utilized together effectively combat fading and interference thereby achieving the requirement of high data rate and high performance. In this paper, a 2 × 1 MIMO‐OFDM baseband model of the physical layer according to IEEE 802.16.1 standard employing 256 subcarriers capable of transmitting 192 Mbps in a 21‐MHz bandwidth frequency selective fading channel is designed. All essential blocks of the communication system like channel coding, modulation, time synchronization, frequency synchronization, channel estimation, residual carrier frequency offset (CFO) estimation, and channel decoding are covered at the algorithm level. A two‐step time synchronization method is used in this work to avoid any ambiguity in frame detection. A long preamble, consisting of three short preambles, is designed to aid in time synchronization, frequency synchronization, and channel estimation. To evaluate the performance of the proposed design, simulations were carried out in MATLAB‐SIMULINK.

Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-Free Massive Random Access

In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This paper develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.