arXiv subjects

Thibaud Tonnellier

Publications and source records attributed to Thibaud Tonnellier.

12 recordsLinked to original sources

arXiv2022

High-Throughput and Energy-Efficient VLSI Architecture for Ordered Reliability Bits GRAND

Ultra-reliable low-latency communication (URLLC), a major 5G New-Radio use case, is the key enabler for applications with strict reliability and latency requirements. These applications necessitate the use of short-length and high-rate codes. Guessing Random Additive Noise Decoding (GRAND) is a recently proposed Maximum Likelihood (ML) decoding technique for these short-length and high-rate codes. Rather than decoding the received vector, GRAND tries to infer the noise that corrupted the transmitted codeword during transmission through the communication channel. As a result, GRAND can decode any code, structured or unstructured. GRAND has hard-input as well as soft-input variants. Among these variants, Ordered Reliability Bits GRAND (ORBGRAND) is a soft-input variant that outperforms hard-input GRAND and is suitable for parallel hardware implementation. This work reports the first hardware architecture for ORBGRAND, which achieves an average throughput of up to $42.5$ Gbps for a code length of $128$ at a target FER of $10^{-7}$. Furthermore, the proposed hardware can be used to decode any code as long as the length and rate constraints are met. In comparison to the GRANDAB, a hard-input variant of GRAND, the proposed architecture enhances decoding performance by at least $2$ dB. When compared to the state-of-the-art fast dynamic successive cancellation flip decoder (Fast-DSCF) using a 5G polar $(128,105)$ code, the proposed ORBGRAND VLSI implementation has $49\times$ higher average throughput, $32\times$ times more energy efficiency, and $5\times$ more area efficiency while maintaining similar decoding performance.

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arXiv2021

High-Throughput VLSI architecture for Soft-Decision decoding with ORBGRAND

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed approximate Maximum Likelihood (ML) decoding technique that can decode any linear error-correcting block code. Ordered Reliability Bits GRAND (ORBGRAND) is a powerful variant of GRAND, which outperforms the original GRAND technique by generating error patterns in a specific order. Moreover, their simplicity at the algorithm level renders GRAND family a desirable candidate for applications that demand very high throughput. This work reports the first-ever hardware architecture for ORBGRAND, which achieves an average throughput of up to $42.5$ Gbps for a code length of $128$ at an SNR of $10$ dB. Moreover, the proposed hardware can be used to decode any code provided the length and rate constraints. Compared to the state-of-the-art fast dynamic successive cancellation flip decoder (Fast-DSCF) using a 5G polar $(128,105)$ code, the proposed VLSI implementation has $49\times$ more average throughput while maintaining similar decoding performance.

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arXiv2020

On Systematic Polarization-Adjusted Convolutional (PAC) Codes

Polarization-adjusted convolutional (PAC) codes were recently proposed and arouse the interest of the channel coding community because they were shown to approach theoretical bounds for the (128,64) code size. In this letter, we propose systematic PAC codes. Thanks to the systematic property, improvement in the bit-error rate of up to 0.2 dB is observed, while preserving the frame-error rate performance. Moreover, a genetic-algorithm based construction method targeted to approach the theoretical bound is provided. It is then shown that using the proposed construction method systematic and non-systematic PAC codes can approach the theoretical bound even for higher code sizes such as (256,128).

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arXiv2020

Practical Dynamic SC-Flip Polar Decoders: Algorithm and Implementation

SC-Flip (SCF) is a low-complexity polar code decoding algorithm with improved performance, and is an alternative to high-complexity (CRC)-aided SC-List (CA-SCL) decoding. However, the performance improvement of SCF is limited since it can correct up to only one channel error ($ω=1$). Dynamic SCF (DSCF) algorithm tackles this problem by tackling multiple errors ($ω\geq 1$), but it requires logarithmic and exponential computations, which make it infeasible for practical applications. In this work, we propose simplifications and approximations to make DSCF practically feasible. First, we reduce the transcendental computations of DSCF decoding to a constant approximation. Then, we show how to incorporate special node decoding techniques into DSCF algorithm, creating the Fast-DSCF decoding. Next, we reduce the search span within the special nodes to further reduce the computational complexity. Following, we describe a hardware architecture for the Fast-DSCF decoder, in which we introduce additional simplifications such as metric normalization and sorter length reduction. All the simplifications and approximations are shown to have minimal impact on the error-correction performance, and the reported Fast-DSCF decoder is the only SCF-based architecture that can correct multiple errors. The Fast-DSCF decoders synthesized using TSMC $65$nm CMOS technology can achieve a $1.25$, $1.06$ and $0.93$ Gbps throughput for $ω\in \{1,2,3\}$, respectively. Compared to the state-of-the-art fast CA-SCL decoders with equivalent FER performance, the proposed decoders are up to $5.8\times$ more area-efficient. Finally, observations at energy dissipation indicate that the Fast-DSCF is more energy-efficient than its CA-SCL-based counterparts.

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arXiv2020

High-Throughput VLSI Architecture for GRAND

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed universal decoding algorithm for linear error correcting codes. Since GRAND does not depend on the structure of the code, it can be used for any code encountered in contemporary communication standards or may even be used for random linear network coding. This property makes this new algorithm particularly appealing. Instead of trying to decode the received vector, GRAND attempts to identify the noise that corrupted the codeword. To that end, GRAND relies on the generation of test error patterns that are successively applied to the received vector. In this paper, we propose the first hardware architecture for the GRAND algorithm. Considering GRAND with ABandonment (GRANDAB) that limits the number of test patterns, the proposed architecture only needs $2+\sum_{i=2}^{n} \left\lfloor\frac{i}{2}\right\rfloor$ time steps to perform the $\sum_{i=1}^3 \binom{n}{i}$ queries required when $\text{AB}=3$. For a code length of $128$, our proposed hardware architecture demonstrates only a fraction ($1.2\%$) of the total number of performed queries as time steps. Synthesis result using TSMC 65nm CMOS technology shows that average throughputs of $32$ Gbps to $64$ Gbps can be achieved at an SNR of $10$ dB for a code length of $128$ and code rates rate higher than $0.75$, transmitted over an AWGN channel. Comparisons with a decoder tailored for a $(79,64)$ BCH code show that the proposed architecture can achieve a slightly higher average throughput at high SNRs, while obtaining the same decoding performance.

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arXiv2020

Operation Merging for Hardware Implementations of Fast Polar Decoders

Polar codes are a class of linear block codes that provably achieves channel capacity. They have been selected as a coding scheme for the control channel of enhanced mobile broadband (eMBB) scenario for $5^{\text{th}}$ generation wireless communication networks (5G) and are being considered for additional use scenarios. As a result, fast decoding techniques for polar codes are essential. Previous works targeting improved throughput for successive-cancellation (SC) decoding of polar codes are semi-parallel implementations that exploit special maximum-likelihood (ML) nodes. In this work, we present a new fast simplified SC (Fast-SSC) decoder architecture. Compared to a baseline Fast-SSC decoder, our solution is able to reduce the memory requirements. We achieve this through a more efficient memory utilization, which also enables to execute multiple operations in a single clock cycle. Finally, we propose new special node merging techniques that improve the throughput further, and detail a new Fast-SSC-based decoder architecture to support merged operations. The proposed decoder reduces the operation sequence requirement by up to $39\%$, which enables to reduce the number of time steps to decode a codeword by $35\%$. ASIC implementation results with 65 nm TSMC technology show that the proposed decoder has a throughput improvement of up to $31\%$ compared to previous Fast-SSC decoder architectures.

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arXiv2019

Deep-Learning-Aided Successive-Cancellation Decoding of Polar Codes

A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding being a specific case of it. The DL-SCL decoder works by allowing additional rounds of SCL decoding when the first SCL decoding attempt fails, using a novel bit-flipping metric. The proposed bit-flipping metric exploits the inherent relations between the information bits in polar codes that are represented by a correlation matrix. The correlation matrix is then optimized using emerging deep-learning techniques. Performance results on a polar code of length 128 with 64 information bits concatenated with a 24-bit cyclic redundancy check show that the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to 66% fewer multiplications and up to 36% fewer additions, without any need to perform transcendental functions, and by providing almost the same error-correction performance in comparison with the state of the art.

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arXiv2019

Efficient Flicker-Free FEC Codes using Knuth's Balancing Algorithm for VLC

Visible light communication (VLC) provides a short-range optical wireless communication through light-emitting diode (LED) lighting. Light beam flickering and dimming are among the challenges to be addressed in VLC. Conventional methods for generating flicker-free codes in VLC are based on run-length limited codes that have poor error correction performance, use lookup tables which are memory consuming, and have low transmission rates. In this paper, we propose an efficient construction of flicker-free forward error correction codes to tackle the issue of flickering in VLC. Our simulation results show that by using polar codes and at a dimming ratio of 50%, the proposed system generates flicker-free codes without using lookup tables, while having lower complexity and higher transmission rates than the standard VLC methods. For an information block length of 256, the error correction performance of the proposed scheme is $1.8$ dB and $0.9$ dB better than that of the regular schemes at the bit error rate of $10^{-6}$ for a rate of 0.44 and 0.23, respectively.

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arXiv2019

Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes

Dynamic successive cancellation flip (DSCF) decoding of polar codes is a powerful algorithm that can achieve the error correction performance of successive cancellation list (SCL) decoding, with a complexity that is close to that of successive cancellation (SC) decoding at practical signal-to-noise ratio (SNR) regimes. However, DSCF decoding requires costly transcendental computations which adversely affect its implementation complexity. In this paper, we first show that a direct application of common approximation schemes on the conventional DSCF decoding results in significant error-correction performance loss. We then introduce a training parameter and propose an approximation scheme which completely removes the need to perform transcendental computations in DSCF decoding, with almost no error-correction performance degradation.

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arXiv2019

Asymmetric Construction of Low-Latency and Length-Flexible Polar Codes

Polar codes are a class of capacity-achieving error correcting codes that have been selected for use in enhanced mobile broadband in the 3GPP 5th generation (5G) wireless standard. Most polar code research examines the original Arikan polar coding scheme, which is limited in block length to powers of two. This constraint presents a considerable obstacle since practical applications call for all code lengths to be readily available. Puncturing and shortening techniques allow for flexible polar codes, while multi-kernel polar codes produce native code lengths that are powers of two and/or three. In this work, we propose a new low complexity coding scheme called asymmetric polar coding that allows for any arbitrary block length. We present details on the generator matrix, frozen set design, and decoding schedule. Our scheme offers flexible polar code lengths with decoding complexity lower than equivalent state-of-the-art length-compatible approaches under successive cancellation decoding. Further, asymmetric decoding complexity is directly dependent on the codeword length rather than the nearest valid polar code length. We compare our scheme with other length matching techniques, and simulations are presented. Results show that asymmetric polar codes present similar error correction performance to the competing schemes, while dividing the number of SC decoding operations by up to a factor of 2 using the same codeword length

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arXiv2019

Fast Decoding of Multi-Kernel Polar Codes

Polar codes are a class of linear error correction codes which provably attain channel capacity with infinite codeword lengths. Finite length polar codes have been adopted into the 5th Generation 3GPP standard for New Radio, though their native length is limited to powers of 2. Utilizing multiple polarizing matrices increases the length flexibility of polar codes at the expense of a more complicated decoding process. Successive cancellation (SC) is the standard polar decoder and has time complexity $\mathcal{O}(N \log N)$ due to its sequential nature. However, some patterns in the frozen set mirror simple linear codes with low latency decoders, which allows for a significant reduction in SC latency by pruning the decoding schedule. Such fast decoding techniques have only previously been used for traditional Arikan polar codes, causing multi-kernel polar codes to be an impractical length-compatibility technique with no fast decoders available. We propose fast simplified successive cancellation decoding node patterns, which are compatible with polar codes constructed with both the Arikan and ternary kernels, and generalization techniques. We outline efficient implementations, made possible by imposing constraints on ternary node parameters. We show that fast decoding of multi-kernel polar codes has at least 72% reduced latency compared with an SC decoder in all cases considered where codeword lengths are (96, 432, 768, 2304).

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arXiv2018

Neural Belief Propagation Decoding of CRC-Polar Concatenated Codes

Polar codes are the first class of error correcting codes that provably achieve the channel capacity at infinite code length. They were selected for use in the fifth generation of cellular mobile communications (5G). In practical scenarios such as 5G, a cyclic redundancy check (CRC) is concatenated with polar codes to improve their finite length performance. This is mostly beneficial for sequential successive-cancellation list decoders. However, for parallel iterative belief propagation (BP) decoders, CRC is only used as an early stopping criterion with incremental error-correction performance improvement. In this paper, we first propose a CRC-polar BP (CPBP) decoder by exchanging the extrinsic information between the factor graph of the polar code and that of the CRC. We then propose a neural CPBP (NCPBP) algorithm which improves the CPBP decoder by introducing trainable normalizing weights on the concatenated factor graph. Our results on a 5G polar code of length 128 show that at the frame error rate of 10^(-5) and with a maximum of 30 iterations, the error-correction performance of CPBP and NCPBP are approximately 0.25 dB and 0.5 dB better than that of the conventional CRC-aided BP decoder, respectively, while introducing almost no latency overhead.

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