Quantized Guessing Random Additive Noise Decoding

Quantized Guessing Random Additive Noise Decoding

Guessing Random Additive Noise Decoding (GRAND) has proven to be a universal, maximum likelihood decoder. Multiple extensions of GRAND have been introduced, giving way to a class of universal decoders. GRAND itself describes a hard-detection decoder, so a natural extension was to incorporate the use of soft-information. The result was Soft Guessing Random Additive Noise Decoding (SGRAND). SGRAND assumes access to complete soft information, proving itself to be a maximum-likelihood soft-detection decoder. Physical limitations, however, prevent one from having access to perfect soft-information in practice. This thesis proposes an approximation to the optimal performance of SGRAND, Quantized Guessing Random Additive Noise Decoding (QGRAND). I describe the algorithm and evaluate its performance compared to hard-detection GRAND, SGRAND, and another approach to approximating SGRAND, Ordered Reliability Bits GRAND (ORBGRAND). QGRAND also allows itself to be tailored to an arbitrary number of bits of soft information, and I will show as the number of bits increases so does performance. I then use the GRAND algorithms discussed in order to evaluate error correction potential of different channel codes, particularly Polar Adjusted Convolutional codes, CA-Polar codes, and CRCs.

Guessing Random Additive Noise Decoding

This book gives a detailed overview of a universal Maximum Likelihood (ML) decoding technique, known as Guessing Random Additive Noise Decoding (GRAND), has been introduced for short-length and high-rate linear block codes. The interest in short channel codes and the corresponding ML decoding algorithms has recently been reignited in both industry and academia due to emergence of applications with strict reliability and ultra-low latency requirements . A few of these applications include Machine-to-Machine (M2M) communication, augmented and virtual Reality, Intelligent Transportation Systems (ITS), the Internet of Things (IoTs), and Ultra-Reliable and Low Latency Communications (URLLC), which is an important use case for the 5G-NR standard. GRAND features both soft-input and hard-input variants. Moreover, there are traditional GRAND variants that can be used with any communication channel, and specialized GRAND variants that are developed for a specific communication channel. This book presents a detailed overview of these GRAND variants and their hardware architectures. The book is structured into four parts. Part 1 introduces linear block codes and the GRAND algorithm. Part 2 discusses the hardware architecture for traditional GRAND variants that can be applied to any underlying communication channel. Part 3 describes the hardware architectures for specialized GRAND variants developed for specific communication channels. Lastly, Part 4 provides an overview of recently proposed GRAND variants and their unique applications. This book is ideal for researchers or engineers looking to implement high-throughput and energy-efficient hardware for GRAND, as well as seasoned academics and graduate students interested in the topic of VLSI hardware architectures. Additionally, it can serve as reading material in graduate courses covering modern error correcting codes and Maximum Likelihood decoding for short codes.

Information Theory and Reliable Communication