High Throughput Vlsi Architectures For Iterative Decoders

High Throughput Iterative Decoders: Towards Shannon Bound in VLSI addresses the algorithms and implementations of iterative decoders for error control in communication applications. The iterative codes are based on various concatenated schemes of convolutional codes, also known as turbo codes, and low density parity check (LDPC) codes. The decoding alogirthms are instances of message passing or belief propagation algorithms, which rely on the iterative cooperation between soft-decoding modules known as soft-input-Iterative decoding is a recent advacement in communication theory that is applicable to wireless, wireline, and optical communicatiosn systems. It promises significant advantage in bit-error rate (BER) performance at signal to noise ratios very close to the theoretical capacity bound. However, a direct mapping of the decoding algorithms leads to a multifold increase in the implementation complexity. As deep submicron technology matures, there is a possibility of implementing these applications that were once thought to be too complex to fit onto a single silicon die. We present the architectural and implementation issues related to the VLSI implementation of high throughput iterative decoders. The computational hardware and memory requirements of different competing architectures are discussed. This monograph also introduces reduced complexity modifications of algorithms that provide efficient mapping into architectures and VLSI implementations.

Error-correcting codes are ubiquitous. They are adopted in almost every modern digital communication and storage system, such as wireless communications, optical communications, Flash memories, computer hard drives, sensor networks, and deep-space probing. New-generation and emerging applications demand codes with better error-correcting capability. On the other hand, the design and implementation of those high-gain error-correcting codes pose many challenges. They usually involve complex mathematical computations, and mapping them directly to hardware often leads to very high complexity. VLSI Architectures for Modern Error-Correcting Codes serves as a bridge connecting advancements in coding theory to practical hardware implementations. Instead of focusing on circuit-level design techniques, the book highlights integrated algorithmic and architectural transformations that lead to great improvements on throughput, silicon area requirement, and/or power consumption in the hardware implementation. The goal of this book is to provide a comprehensive and systematic review of available techniques and architectures, so that they can be easily followed by system and hardware designers to develop en/decoder implementations that meet error-correcting performance and cost requirements. This book can be also used as a reference for graduate-level courses on VLSI design and error-correcting coding. Particular emphases are placed on hard- and soft-decision Reed-Solomon (RS) and Bose-Chaudhuri-Hocquenghem (BCH) codes, and binary and non-binary low-density parity-check (LDPC) codes. These codes are among the best candidates for modern and emerging applications due to their good error-correcting performance and lower implementation complexity compared to other codes. To help explain the computations and en/decoder architectures, many examples and case studies are included. More importantly, discussions are provided on the advantages and drawbacks of different implementation approaches and architectures.

For the new millenium, Wai-Kai Chen introduced a monumental reference for the design, analysis, and prediction of VLSI circuits: The VLSI Handbook. Still a valuable tool for dealing with the most dynamic field in engineering, this second edition includes 13 sections comprising nearly 100 chapters focused on the key concepts, models, and equations. Written by a stellar international panel of expert contributors, this handbook is a reliable, comprehensive resource for real answers to practical problems. It emphasizes fundamental theory underlying professional applications and also reflects key areas of industrial and research focus. WHAT'S IN THE SECOND EDITION? Sections on... Low-power electronics and design VLSI signal processing Chapters on... CMOS fabrication Content-addressable memory Compound semiconductor RF circuits High-speed circuit design principles SiGe HBT technology Bipolar junction transistor amplifiers Performance modeling and analysis using SystemC Design languages, expanded from two chapters to twelve Testing of digital systems Structured for convenient navigation and loaded with practical solutions, The VLSI Handbook, Second Edition remains the first choice for answers to the problems and challenges faced daily in engineering practice.

Near-capacity performance and parallelizable decoding algorithms have made Low-Density Parity Check (LDPC) codes a powerful competitor to previous generations of codes, such as Turbo and Reed Solomon codes, for reliable high-speed digital communications. As a result, they have been adopted in several emerging standards. This thesis investigates VLSI architectures for multi-Gbps power and area-efficient LDPC decoders. To reduce the node-to-node communication complexity, a decoding scheme is proposed in which messages are transferred and computed bit-serially. Also, a broadcasting scheme is proposed in which the traditional computations required in the sum-product and min-sum decoding algorithms are repartitioned between the check and variable node units. To increase decoding throughput, a block interlacing scheme is investigated which is particularly advantageous in fully-parallel LDPC decoders. To increase decoder energy efficiency, an efficient early termination scheme is proposed. In addition, an analysis is given of how increased hardware parallelism coupled with a reduced supply voltage is a particularly effective approach to reduce the power consumption of LDPC decoders. These architectures and circuits are demonstrated in two hardware implementations. Specifically, a 610-Mbps bit-serial fully-parallel (480, 355) LDPC decoder on a single Altera Stratix EP1S80 device is presented. To our knowledge, this is the fastest FPGA-based LDPC decoder reported in the literature. A fabricated 0.13-mum CMOS bit-serial (660, 484) LDPC decoder is also presented. The decoder has a 300 MHz maximum clock frequency and a 3.3 Gbps throughput with a nominal 1.2-V supply and performs within 3 dB of the Shannon limit at a BER of 10-5. With more than 60% power saving gained by early termination, the decoder consumes 10.4 pJ/bit/iteration at Eb=N0=4dB. Coupling early termination with supply voltage scaling results in an even lower energy consumption of 2.7 pJ/bit/iteration with 648 Mbps decoding throughput. The proposed techniques demonstrate that the bit-serial fully-parallel architecture is preferred to memory-based partially-parallel architectures, both in terms of throughput and energy efficiency, for applications such as 10GBase-T which use medium-size LDPC code (e.g., 2048 bit) and require multi-Gbps decoding throughput.

This book constitutes the refereed proceedings of the 18th National Conference on Computer Engineering and Technology, NCCET 2014, held in Guiyang, China, during July/August 2014. The 18 papers presented were carefully reviewed and selected from 85 submissions. They are organized in topical sections on processor architecture; computer application and software optimization; technology on the horizon.

Artificial intelligence (AI), the internet of things (IoT), and blockchain provide services to 6G in the form of radio resource management, mobility management, energy management, and network management. Moreover, 6G strengthens AI and blockchain-based applications. Further study on the benefits and potential opportunities of 6G for AI and blockchain is required to utilize the technology successfully. Role of 6G Wireless Networks in AI and Blockchain-Based Applications considers the role of the 6G wireless network deployed on AI and blockchain technology-based applications in fields such as the healthcare industry, agriculture, e-business, and transportation. The book specifically focuses on remote healthcare monitoring, online shopping preference, V2V communication, UAV, holographic application, and augmented and virtual reality as advanced services of 6G networks. Covering topics such as machine learning, smart cities, and virtual reality, this reference work is ideal for computer scientists, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

This book introduces readers to a reconfigurable chip architecture for future wireless communication systems, such as 5G and beyond. The proposed architecture perfectly meets the demands for future mobile communication solutions to support different standards, algorithms, and antenna sizes, and to accommodate the evolution of standards and algorithms. It employs massive MIMO detection algorithms, which combine the advantages of low complexity and high parallelism, and can fully meet the requirements for detection accuracy. Further, the architecture is implemented using ASIC, which offers high energy efficiency, high area efficiency and low detection error. After introducing massive MIMO detection algorithms and circuit architectures, the book describes the ASIC implementation for verifying the massive MIMO detection. In turn, it provides detailed information on the proposed reconfigurable architecture: the data path and configuration path for massive MIMO detection algorithms, including the processing unit, interconnections, storage mechanism, configuration information format, and configuration method.

This book provides a detailed overview of detection algorithms for multiple-input multiple-output (MIMO) communications systems focusing on their hardware realisation. The book begins by analysing the maximum likelihood detector, which provides the optimal bit error rate performance in an uncoded communications system. However, the maximum likelihood detector experiences a high complexity that scales exponentially with the number of antennas, which makes it impractical for real-time communications systems. The authors proceed to discuss lower-complexity detection algorithms such as zero-forcing, sphere decoding, and the K-best algorithm, with the aid of detailed algorithmic analysis and several MATLAB code examples. Furthermore, different design examples of MIMO detection algorithms and their hardware implementation results are presented and discussed. Finally, an ASIC design flow for implementing MIMO detection algorithms in hardware is provided, including the system simulation and modelling steps and register transfer level modelling using hardware description languages. Provides an overview of MIMO detection algorithms and discusses their corresponding hardware implementations in detail; Highlights architectural considerations of MIMO detectors in achieving low power consumption and high throughput; Discusses design tradeoffs that will guide readers’ efforts when implementing MIMO algorithms in hardware; Describes a broad range of implementations of different MIMO detectors, enabling readers to make informed design decisions based on their application requirements.