Mots-clés de l'auteur: polar codes ; successive cancellation list decoding ; hardware implementation ; VLSI ; approximate computing ; faulty decoding ; LDPC codes ; unrolled decoding.

A new class of provably capacity achieving error-correction codes, polar codes are suitable for many problems, such as lossless and lossy source coding, problems with side information, multiple access channel, etc. The first comprehensive book on the implementation of decoders for polar codes, the authors take a tutorial approach to explain the practical decoder implementation challenges and trade-offs in either software or hardware. They also demonstrate new trade-offs in latency, throughput, and complexity in software implementations for high-performance computing and GPGPUs, and hardware implementations using custom processing elements, full-custom application-specific integrated circuits (ASICs), and field-programmable-gate arrays (FPGAs). Presenting a good overview of this research area and future directions, High-Speed Decoders for Polar Codes is perfect for any researcher or SDR practitioner looking into implementing efficient decoders for polar codes, as well as students and professors in a modern error correction class. As polar codes have been accepted to protect the control channel in the next-generation mobile communication standard (5G) developed by the 3GPP, the audience includes engineers who will have to implement decoders for such codes and hardware engineers designing the backbone of communication networks.

This book introduces turbo error correcting concept in a simple language, including a general theory and the algorithms for decoding turbo-like code. It presents a unified framework for the design and analysis of turbo codes and LDPC codes and their decoding algorithms. A major focus is on high speed turbo decoding, which targets applications with data rates of several hundred million bits per second (Mbps).

This book takes a practical hands-on approach to developing low complexity algorithms and transforming them into working hardware. It follows a complete design approach – from algorithms to hardware architectures - and addresses some of the challenges associated with their design, providing insight into implementing innovative architectures based on low complexity algorithms.The reader will learn: - Modern techniques to design, model and analyze low complexity LDPC algorithms as well as their hardware implementation - How to reduce computational complexity and power consumption using computer aided design techniques - All aspects of the design spectrum from algorithms to hardware implementation and performance trade-offs - Provides extensive treatment of LDPC decoding algorithms and hardware implementations - Gives a systematic guidance, giving a basic understanding of LDPC codes and decoding algorithms and providing practical skills in implementing efficient LDPC decoders in hardware - Companion website containing C-Programs and MATLAB models for simulating the algorithms, and Verilog HDL codes for hardware modeling and synthesis

Low-Density Parity-check (LDPC) codes have attracted considerable attention due to their capacity approaching performance over AWGN channel and highly parallelizable decoding schemes. They have been considered in a variety of industry standards for the next generation communication systems. In general, LDPC codes achieve outstanding performance with large codeword lengths (e.g., N>1000 bits), which lead to a linear increase of the size of memory for storing all the soft messages in LDPC decoding. In the next generation communication systems, the target data rates range from a few hundred Mbit/sec to several Gbit/sec. To achieve those very high decoding throughput, a large amount of computation units are required, which will significantly increase the hardware cost and power consumption of LDPC decoders. LDPC codes are decoded using iterative decoding algorithms. The decoding latency and power consumption are linearly proportional to the number of decoding iterations. A decoding approach with fast convergence speed is highly desired in practice. This thesis considers various VLSI design issues of LDPC decoder and develops efficient approaches for reducing memory requirement, low complexity implementation, and high speed decoding of LDPC codes. We propose a memory efficient partially parallel decoder architecture suited for quasi-cyclic LDPC (QC-LDPC) codes using Min-Sum decoding algorithm. We develop an efficient architecture for general permutation matrix based LDPC codes. We have explored various approaches to linearly increase the decoding throughput with a small amount of hardware overhead. We develop a multi-Gbit/sec LDPC decoder architecture for QC-LDPC codes and prototype an enhanced partially parallel decoder architecture for a Euclidian geometry based LDPC code on FPGA. We propose an early stopping scheme and an extended layered decoding method to reduce the number of decoding iterations for undecodable and decodable sequence received from channel. We also propose a low-complexity optimized 2-bit decoding approach which requires comparable implementation complexity to weighted bit flipping based algorithms but has much better decoding performance and faster convergence speed.

"Polar codes have received a great deal of attention in the past few years to the extent that they are selected to be included in the 5th Generation of Wireless Communications Standard (5G). Specifically, polar codes were selected as the coding scheme for the Enhanced Mobile Broadband (eMBB) control channel which requires codes of short length. The main bottleneck in the deployment of polar codes in 5G is the design of a decoder which can achieve good error-correction performance, with low hardware implementation cost and high throughput. Successive-Cancellation (SC) decoding was the first algorithm under which polar codes could achieve capacity when the code length is very high. However, for finite practical code lengths, SC decoding falls short in providing a reasonable error-correction performance because of its sub-optimality with respect to the Maximum-Likelihood (ML) decoder. Sphere Decoding (SD) is an algorithm that can achieve the performance of ML decoding with a very high complexity. In order to close the gap between SC and ML decoding, Successive-Cancellation List (SCL) decoding keeps a list of candidates and selects the one with the best Path Metric (PM). Although SCL provides a good error-correction performance, it comes at the cost of higher complexity and lower throughput. In this thesis, we first propose a low complexity SD algorithm which provides a good trade-off between the error-correction performance and the complexity of the decoder for polar codes of short lengths. We then propose algorithms to speed up the SCL decoders. We prove that while these algorithms have much higher throughput than the conventional SCL decoder, they incur no error-correction performance loss. We further propose several techniques to reduce the area occupation in the hardware implementation of SC and SCL decoders by reducing their memory requirements. We solve the flexibility issue of fast SC-based decoders and introduce a completely rate-flexible scheme. Hardware architectures for the proposed algorithms are presented and comparisons with state of the art are made. Finally, we evaluate the performance of polar codes in 5G and we show that polar codes can be used in practical applications by proposing a blind detection scheme with polar codes." --

Since the rediscovery of low-density parity-check (LDPC) codes in the late 1990s, tremendous progress has been made in code construction and design, decoding algorithms, and decoder implementation of these capacity-approaching codes. Recently, LDPC codes are considered for applications such as high-speed satellite and optical communications, the hard disk drives, and high-density flash memory based storage systems, which require that the codes are free of error-floor down to bit error rate (BER) as low as 10−12 to 10−15. FPGAs are usually used to evaluate the error performance of codes, since one can exploit the finite word length and extremely high internal memory bandwidth of an FPGA. Existing FPGA-based LDPC decoders fail to utilize the configurability and read-first mode of embedded memory in the FPGAs, and thus result in limited throughput and codes sizes. Four optimization techniques, i.e., vectorization, folding, message relocation, and circulant permutation matrix (CPM) sharing, are proposed to improve the throughput, scalability, and efficiency of FPGA-based decoders. Also, a semi-automatic CAD tool called QCSYN (Quasi-Cyclic LDPC decoder SYNthesis) is designed to shorten the implementation time of decoders. Using the above techniques, a high-rate (16129,15372) code is shown to have no error-floor down to the BER of 10−14. Also, it is very difficult to construct codes that do not exhibit an error floor down to 10−15 or so. Without detailed knowledge of dominant trapping sets, a backtracking-based reconfigurable decoder is designed to lower the error floor of a family of structurally compatible quasi-cyclic LDPC codes by one to two orders of magnitudes. Hardware reconfigurability is another significant feature of LDPC decoders. A tri-mode decoder for the (4095,3367) Euclidean geometry code is designed to work with three compatible binary message passing decoding algorithms. Note that this code contains 262080 edges (21.3 times of the (2048,1723) 10GBASE-T code) in its Tanner graph and is the largest code ever implemented. Besides, an efficient QC-LDPC Shift Network (QSN) is proposed to reduce the interconnect delay and control logic of circular shift network, a core component in the reconfigurable decoder that supports a family of structurally compatible codes. The interconnect delay and control logic area are reduced by a factor of 2.12 and 8, respectively. Non-binary LDPC codes are effective in combating burst errors. Using the power representation of the elements in the Galois field to organize both intrinsic and extrinsic messages, we present an efficient decoder architecture for non-binary QC-LDPC codes. The proposed decoder is reconfigurable and can be used to decode any code of a given field size. The decoder supports both regular and irregular non-binary QC-LDPC codes. Using a practical metric of throughput per unit area, the proposed implementation outperforms the best implementations published in research literature to date.