Ultra-reliable and low-latency communication (URLLC) in 6G networks is characterized by very high reliability and very low latency to enable mission-critical applications. The ability of a coding scheme to support diverse use cases requires flexibility on the part of the decoder. High reliability and low latency require decoders with improved error rate performance and reduced complexity. This article investigates candidate universal decoding algorithms for 6G communication scenarios. Universal decoders work on a wide range of error-correcting codes, making them scalable for different communication protocols. This article undertakes the comparative analysis and performance evaluation of the code-agnostic decoding schemes, including automorphism ensemble (AED), guessing random additive noise (GRAND), ordered statistics (OSD), belief propagation (BPD), bit flipping (BFD), and their variants. Simulations are carried out in MATLAB (R2024a) for the error rate performance of decoders, and plots are provided for the comparative analysis from the results of inferred data. The key findings in this paper highlight the competitive advantage of universal decoding techniques in comparison to the standardized CA-SCL decoding of polar code. Consequently, this work will help in identifying more efficient decoding algorithms for potential 6G URLLC applications. We aim to provide an insight into the scalability of universal decoding techniques by exploring their key performance metrics and comparing their performances.
Ultra-reliable and low-latency communication (URLLC) in 6G networks is characterized by very high reliability and very low latency to enable mission-critical applications. The ability of a coding scheme to support diverse use cases requires flexibility on the part of the decoder. High reliability and low latency require decoders with improved error rate performance and reduced complexity. This article investigates candidate universal decoding algorithms for 6G communication scenarios. Universal decoders work on a wide range of error-correcting codes, making them scalable for different communication protocols. This article undertakes the comparative analysis and performance evaluation of the code-agnostic decoding schemes, including automorphism ensemble (AED), guessing random additive noise (GRAND), ordered statistics (OSD), belief propagation (BPD), bit flipping (BFD), and their variants. Simulations are carried out in MATLAB (R2024a) for the error rate performance of decoders, and plots are provided for the comparative analysis from the results of inferred data. The key findings in this paper highlight the competitive advantage of universal decoding techniques in comparison to the standardized CA-SCL decoding of polar code. Consequently, this work will help in identifying more efficient decoding algorithms for potential 6G URLLC applications. We aim to provide an insight into the scalability of universal decoding techniques by exploring their key performance metrics and comparing their performances.
The ultra-reliable low-latency communication (URLLC), one of the three main scenarios in the fifth-generation (5G) mobile communication system, is promising to be used in various emerging applications with stringent latency and performance requirements. Recently, the guessing random additive noise decoding (GRAND) is proposed, which can perform well for the URLLC due to its excellent error-correction performance with short code length. Compared to other variants of the GRAND, the ordered reliability bits GRAND (ORBGRAND) can provide a good trade-off between error-correction performance and hardware overhead. In this work, the ORBGRAND with a reduced schedule for polar codes is implemented for the first time, which utilizes the special structure of the parity-check matrix of polar codes to reduce the decoding latency while keeping the error-correction performance unaltered. Besides, the sorter employed in the ORBGRAND decoder is well optimized to reduce the area occupation. At a target frame error rate of 10−5when using a (128, 105) 5G polar code, the average decoding latency of the proposed decoder is only 2.46 ns. Moreover, compared to the state-of-the-art ORBGRAND decoder, the proposed decoder can achieve 15% higher decoding throughput and 71% higher area efficiency without any error-correction performance loss.
Ultra-reliable low-latency communications (URLLC) scenario for 6G drives the interest in studying and development of existing and new universal decoding algorithms capable of providing near-optimum error-correcting performance for short block length codes with ultimate goal of providing low latency and moderate complexity. In this paper we consider a new universal decoding technique based on randomized likelihood (RL) rule that may be implemented for practical use in the future. U sing this rule we get the message estimate as a single sample of the message vector from a posterior distribution of messages given the received signal. A soft-decision implementation of RL decoding algorithm is suggested. After suggesting further complexity optimization, the new algorithm is compared to other well-known universal decoders, such as ordered-statistics decoding (OSD) and guessing random additive noise decoding (GRAND). The simulation results for short codes with different rates are provided for binary-input additive white Gaussian noise (BAWGN) communication channel.
Projected requirements for 6G ultra-reliable low-latency communications (URLLC) scenario require extremely low latency and efficient error correction akin to maximum likelihood (ML) performance. The quest for codes and decoding methods that balance low latency, energy efficiency, and error correction ability is at its height. Polar codes known for their capacity achievement in symmetric memoryless channels have shown promise in 5G applications. Advancing further existing decoding techniques like successive cancellation list (SCL) algorithm offer a path towards ML performance, whilst at the expense of having increased computational complexity. The introduction of automorphism ensemble (AE) decoding provided a novel approach for parallel decoding, particularly beneficial for codes with rich automorphism group like Reed-Muller (RM) codes. This work introduces a novel modification of AE decoding termed reduced-delay AE (RD-AE) decoder. RD-AE leverages a tailored set of automorphisms with the primary aim at diminishing decoding delay but also offering the flexibility to prioritize either delay reduction or error correction performance.
Error correction techniques traditionally focus on the co-design of restricted code-structures in tandem with code-specific decoders that are computationally efficient when decoding long codes in hardware. Modern applications are, however, driving demand for ultra-reliable low-latency communications (URLLC), rekindling interest in the performance of shorter, higher-rate error correcting codes, and raising the possibility of revisiting universal, code-agnostic decoders. To that end, here we introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can accurately decode any moderate redundancy block-code. It is designed with efficient circuit implementation in mind, and determines accurate decodings while retaining the original hard detection GRAND algorithm's suitability for a highly parallelized implementation in hardware. ORBGRAND is shown to provide excellent soft decision block error performance for codes of distinct classes (BCH, CA-Polar and RLC) with modest complexity, while providing better block error rate performance than CA-SCL, a state of the art soft detection CA-Polar decoder. ORBGRAND offers the possibility of an accurate, energy efficient soft detection decoder suitable for delivering URLLC in a single hardware realization.
CRC codes have long since been adopted in a vast range of applications. The established notion that they are suitable primarily for error detection can be set aside through use of the recently proposed Guessing Random Additive Noise Decoding (GRAND). Hard-detection (GRAND-SOS) and soft-detection (ORBGRAND) variants can decode any short, high-rate block code, making them suitable for error correction of CRC-coded data. When decoded with GRAND, short CRC codes have error correction capability that is at least as good as popular codes such as BCH codes, but with no restriction on either code length or rate.The state-of-the-art CA-Polar codes are concatenated CRC and Polar codes. For error correction, we find that the CRC is a better short code than either Polar or CA-Polar codes. Moreover, the standard CA-SCL decoder only uses the CRC for error detection and therefore suffers severe performance degradation in short, high rate settings when compared with the performance GRAND provides, which uses all of the CA-Polar bits for error correction.Using GRAND, existing systems can be upgraded from error detection to low-latency error correction without re-engineering the encoder, and additional applications of CRCs can be found in IoT, Ultra-Reliable Low Latency Communication (URLLC), and beyond. The universality of GRAND, its ready parallelized implementation in hardware, and the good performance of CRC as codes make their combination a viable solution for low-latency applications.
Abstract This paper presents an intelligent decoding methodology for low-density parity-check (LDPC) and Polar codes (P-C) based on deep reinforcement learning (DRL+Soft) for modern 5G and emerging 6G communication systems. The proposed decoder adapts its decoding strategy in real-time, optimizing bit error rate (BER) performance under varying channel conditions. Evaluation is formulated through performance metrics including BER convergence, reward dynamics, computational complexity (FLOPs), and inference latency. Simulation results show that the DRL+Soft decoder achieves up to a 2.5-fold reduction in computational cost and decreases inference time from 4.8 ms (BP) to 1.6 ms per data block, while maintaining superior BER compared to classical belief propagation (BP) and cyclic redundancy check-aided successive cancellation list (CA-SCL) decoders. The decoder exhibits improved robustness against unpredictable interference and channel impairments, making it suitable for dense device deployments and ultra-reliable low-latency communication (URLLC) scenarios. The methodology also supports reprogramming or retraining of DRL agents without hardware changes, ensuring long-term adaptability for evolving 6G networks. These results demonstrate both theoretical novelty and practical value, providing a scalable and energy-efficient solution for future wireless communication systems.