Efficient FPGA implementation of high-throughput mixed radix multipath delay commutator FFT processor for MIMO-OFDM

This article presents and evaluates pipelined architecture designs for an improved high-frequency Fast Fourier Transform (FFT) processor implemented on Field Programmable Gate Arrays (FPGA) for Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM). The architecture presented is a Mixed-Radix Multipath Delay Commutator. The presented parallel architecture utilizes fewer hardware resources compared to Radix-2 architecture, while maintaining simple control and butterfly structures inherent to Radix-2 implementations. The high-frequency design presented allows enhancing system throughput without requiring additional parallel data paths common in other current approaches, the presented design can process two and four independent data streams in parallel and is suitable for scaling to any power of two FFT size N. FPGA implementation of the architecture demonstrated significant resource efficiency and high-throughput in comparison to relevant current approaches within literature. The proposed architecture designs were realized with Xilinx System Generator (XSG) and evaluated on both Virtex-5 and Virtex-7 FPGA devices. Post place and route results demonstrated maximum frequency values over 400 MHz and 470 MHz for Virtex-5 and Virtex-7 FPGA devices respectively

Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design

In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes

Algorithm Development and VLSI Implementation of Energy Efficient Decoders of Polar Codes

With its low error-floor performance, polar codes attract significant attention as the potential standard error correction code (ECC) for future communication and data storage. However, the VLSI implementation complexity of polar codes decoders is largely influenced by its nature of in-series decoding. This dissertation is dedicated to presenting optimal decoder architectures for polar codes. This dissertation addresses several structural properties of polar codes and key properties of decoding algorithms that are not dealt with in the prior researches. The underlying concept of the proposed architectures is a paradigm that simplifies and schedules the computations such that hardware is simplified, latency is minimized and bandwidth is maximized. In pursuit of the above, throughput centric successive cancellation (TCSC) and overlapping path list successive cancellation (OPLSC) VLSI architectures and express journey BP (XJBP) decoders for the polar codes are presented. An arbitrary polar code can be decomposed by a set of shorter polar codes with special characteristics, those shorter polar codes are referred to as constituent polar codes. By exploiting the homogeneousness between decoding processes of different constituent polar codes, TCSC reduces the decoding latency of the SC decoder by 60% for codes with length n = 1024. The error correction performance of SC decoding is inferior to that of list successive cancellation decoding. The LSC decoding algorithm delivers the most reliable decoding results; however, it consumes most hardware resources and decoding cycles. Instead of using multiple instances of decoding cores in the LSC decoders, a single SC decoder is used in the OPLSC architecture. The computations of each path in the LSC are arranged to occupy the decoder hardware stages serially in a streamlined fashion. This yields a significant reduction of hardware complexity. The OPLSC decoder has achieved about 1.4 times hardware efficiency improvement compared with traditional LSC decoders. The hardware efficient VLSI architectures for TCSC and OPLSC polar codes decoders are also introduced. Decoders based on SC or LSC algorithms suffer from high latency and limited throughput due to their serial decoding natures. An alternative approach to decode the polar codes is belief propagation (BP) based algorithm. In BP algorithm, a graph is set up to guide the beliefs propagated and refined, which is usually referred to as factor graph. BP decoding algorithm allows decoding in parallel to achieve much higher throughput. XJBP decoder facilitates belief propagation by utilizing the specific constituent codes that exist in the conventional factor graph, which results in an express journey (XJ) decoder. Compared with the conventional BP decoding algorithm for polar codes, the proposed decoder reduces the computational complexity by about 40.6%. This enables an energy-efficient hardware implementation. To further explore the hardware consumption of the proposed XJBP decoder, the computations scheduling is modeled and analyzed in this dissertation. With discussions on different hardware scenarios, the optimal scheduling plans are developed. A novel memory-distributed micro-architecture of the XJBP decoder is proposed and analyzed to solve the potential memory access problems of the proposed scheduling strategy. The register-transfer level (RTL) models of the XJBP decoder are set up for comparisons with other state-of-the-art BP decoders. The results show that the power efficiency of BP decoders is improved by about 3 times

Algorithm Development and VLSI Implementation of Energy Efficient Decoders of Polar Codes

With its low error-floor performance, polar codes attract significant attention as the potential standard error correction code (ECC) for future communication and data storage. However, the VLSI implementation complexity of polar codes decoders is largely influenced by its nature of in-series decoding. This dissertation is dedicated to presenting optimal decoder architectures for polar codes. This dissertation addresses several structural properties of polar codes and key properties of decoding algorithms that are not dealt with in the prior researches. The underlying concept of the proposed architectures is a paradigm that simplifies and schedules the computations such that hardware is simplified, latency is minimized and bandwidth is maximized. In pursuit of the above, throughput centric successive cancellation (TCSC) and overlapping path list successive cancellation (OPLSC) VLSI architectures and express journey BP (XJBP) decoders for the polar codes are presented. An arbitrary polar code can be decomposed by a set of shorter polar codes with special characteristics, those shorter polar codes are referred to as constituent polar codes. By exploiting the homogeneousness between decoding processes of different constituent polar codes, TCSC reduces the decoding latency of the SC decoder by 60% for codes with length n = 1024. The error correction performance of SC decoding is inferior to that of list successive cancellation decoding. The LSC decoding algorithm delivers the most reliable decoding results; however, it consumes most hardware resources and decoding cycles. Instead of using multiple instances of decoding cores in the LSC decoders, a single SC decoder is used in the OPLSC architecture. The computations of each path in the LSC are arranged to occupy the decoder hardware stages serially in a streamlined fashion. This yields a significant reduction of hardware complexity. The OPLSC decoder has achieved about 1.4 times hardware efficiency improvement compared with traditional LSC decoders. The hardware efficient VLSI architectures for TCSC and OPLSC polar codes decoders are also introduced. Decoders based on SC or LSC algorithms suffer from high latency and limited throughput due to their serial decoding natures. An alternative approach to decode the polar codes is belief propagation (BP) based algorithm. In BP algorithm, a graph is set up to guide the beliefs propagated and refined, which is usually referred to as factor graph. BP decoding algorithm allows decoding in parallel to achieve much higher throughput. XJBP decoder facilitates belief propagation by utilizing the specific constituent codes that exist in the conventional factor graph, which results in an express journey (XJ) decoder. Compared with the conventional BP decoding algorithm for polar codes, the proposed decoder reduces the computational complexity by about 40.6%. This enables an energy-efficient hardware implementation. To further explore the hardware consumption of the proposed XJBP decoder, the computations scheduling is modeled and analyzed in this dissertation. With discussions on different hardware scenarios, the optimal scheduling plans are developed. A novel memory-distributed micro-architecture of the XJBP decoder is proposed and analyzed to solve the potential memory access problems of the proposed scheduling strategy. The register-transfer level (RTL) models of the XJBP decoder are set up for comparisons with other state-of-the-art BP decoders. The results show that the power efficiency of BP decoders is improved by about 3 times

Selected Papers from IEEE ICASI 2019

The 5th IEEE International Conference on Applied System Innovation 2019 (IEEE ICASI 2019, https://2019.icasi-conf.net/), which was held in Fukuoka, Japan, on 11–15 April, 2019, provided a unified communication platform for a wide range of topics. This Special Issue entitled “Selected Papers from IEEE ICASI 2019” collected nine excellent papers presented on the applied sciences topic during the conference. Mechanical engineering and design innovations are academic and practical engineering fields that involve systematic technological materialization through scientific principles and engineering designs. Technological innovation by mechanical engineering includes information technology (IT)-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology. These new technologies that implant intelligence in machine systems represent an interdisciplinary area that combines conventional mechanical technology and new IT. The main goal of this Special Issue is to provide new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials in nanoscience and nanotechnology

Architectures for Dynamic Data Scaling in 2/4/8K Pipeline FFT Cores

This paper presents architectures for supporting dynamic data scaling in pipeline fast Fourier transforms (FFTs), suitable when implementing large size FFTs in applications such as digital video broadcasting and digital holographic imaging. In a pipeline FFT, data is continuously streaming and must, hence, be scaled without stalling the dataflow. We propose a hybrid floating-point scheme with tailored exponent datapath, and a co-optimized architecture between hybrid floating point and block floating point (BFP) to reduce memory requirements for 2-D signal processing. The presented co-optimization generates a higher signal-to-quantization-noise ratio and requires less memory than for instance convergent BFP. A 2048-point pipeline FFT has been fabricated in a standard-CMOS process from AMI Semiconductor (Lenart and Öwall, 2003), and a field-programmable gate array prototype integrating a 2-D FFT core in a larger design shows that the architecture is suitable for image reconstruction in digital holographic imaging