6G NOMA Achieves Noise-Resilient Decoding with CRC-Aided GRAND for Beyond 5G Networks
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Non-Orthogonal Multiple Access (NOMA) represents a key technology for delivering the massive connectivity and enhanced spectral efficiency demanded by future wireless networks, and researchers are actively seeking ways to improve its robustness. Emirhan Zor, Bora Bozkurt, and Ferkan Yilmaz, from Istanbul Technical University, present a novel NOMA framework that integrates Cyclic Redundancy Check (CRC)-aided Guessing Random Additive Noise Decoding (GRAND) with successive interference cancellation (SIC). This innovative approach overcomes the limitations of conventional SIC methods, which can suffer from error propagation in challenging conditions, by employing GRAND’s noise-centric strategy to systematically identify the correct data.
The team demonstrates that this CRC-aided GRAND-NOMA scheme significantly improves bit error rate performance across various wireless channels and scenarios, highlighting its potential to deliver more reliable and efficient communication in future networks.
The team’s work addresses limitations of conventional SIC methods, which can struggle when power differences between users are small, by employing GRAND’s unique noise-centric strategy to systematically evaluate potential error patterns. The core innovation lies in combining CRC, traditionally used for error detection, with GRAND’s decoding capabilities, creating a system that eliminates the need for separate forward error correction codes and reduces overall system complexity. Researchers engineered an architecture where the strong user benefits from improved decoding performance by applying SIC reinforced by GRAND-based decoding of the weaker user’s signals, minimizing error propagation and increasing data throughput. GRAND approaches decoding by guessing and removing noise sequences from the received signal until a valid message is found, systematically ranking potential error patterns by their likelihood. Extensive simulations, conducted over both ideal additive white Gaussian noise and more realistic Rayleigh fading channels, demonstrate that this CRC-aided GRAND-NOMA framework significantly improves bit error rate performance compared to existing NOMA techniques. This work addresses limitations of conventional SIC methods, which can suffer from error propagation when power differences between users are small, by leveraging GRAND’s noise-centric approach to systematically rank and test potential error patterns.
The team demonstrates that CRC not only detects errors but also aids the decoding process, eliminating the need for separate forward error correction codes and reducing overall system overhead. Researchers engineered an architecture where the strong user benefits from enhanced decoding performance by applying SIC reinforced by GRAND-based decoding of the weaker user’s signals, minimizing error propagation and increasing throughput. Comprehensive simulation results, conducted over both additive white Gaussian noise and Rayleigh fading channels, demonstrate significant improvements in bit error rate performance compared to state-of-the-art NOMA decoding techniques. Specifically, the CRC-aided GRAND-NOMA approach consistently outperforms traditional NOMA systems, delivering enhanced reliability and spectral efficiency. Further analysis shows the impact of user distance and power allocation on system performance, with bit error rate results demonstrating the robustness of the proposed scheme under varying conditions.
The team’s simulations, utilizing both classical GRAND and one-line Ordered Reliability Bits GRAND (ORBGRAND) methods, confirm the benefits of integrating GRAND into NOMA systems.
The team successfully integrated GRAND’s error-pattern search capabilities with CRC to create a decoding strategy that does not require separate forward error correction codes, thereby reducing system overhead. Simulations conducted over both additive white Gaussian noise and Rayleigh fading channels demonstrate that this approach significantly improves bit error rate performance compared to conventional NOMA techniques. The findings position CRC-aided GRAND-NOMA as a promising candidate for future wireless networks, specifically for applications demanding ultra-reliable low latency communications and massive machine-type communications. The authors acknowledge that performance gains are most pronounced at shorter distances, with diminishing differences observed beyond five meters. Future work may focus on extending this framework to multi-user scenarios and exploring its application within more complex multiple-input multiple-output systems, building upon the demonstrated improvements in spectral efficiency and robust multi-user support. 👉 More information🗞 Toward 6G Downlink NOMA: CRC-Aided GRAND for Noise-Resilient NOMA Decoding in Beyond-5G Networks🧠 ArXiv: https://arxiv.org/abs/2512.16860 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Process Tensors Enable Exact Quantum Work Statistics for Driven Open Quantum Systems December 19, 2025 Vector Field Representations Advance Pattern Recognition in Complex, High-Dimensional Systems December 19, 2025 Stronger Quantum Divergences Enable Improved Noisy Channel Characterization December 19, 2025