1.5 Gbit/s FPGA Implementation of a Fully-Parallel Turbo Decoder Designed for Mission-Critical Machine-Type Communication Applications

Liu, An; Hailes, Peter; Maunder, Robert G.; Al-Hashimi, Bashir M.; Hanzo, Lajos

doi:10.1109/access.2016.2599408

Cited by 26 publications

(6 citation statements)

References 47 publications

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“…Parallel hardware implementation is another important measure to reduce signal processing latency. For example, the recently proposed parallel turbo decoder architecture [14] eliminates the serial data dependencies, realizes full parallel processing, realizes full parallel processing, offers an average throughput of 1.53 Gb/s, and finally achieves a 50% hardware resource reduction compared with the original architecture.…”

Section: Ultra-fast Signal Processingmentioning

confidence: 99%

Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches

et al. 2018

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The fifth-generation cellular mobile networks are expected to support mission critical ultra-reliable low latency communication (URLLC) services in addition to the enhanced mobile broadband applications. This article first introduces three emerging mission critical applications of URLLC and identifies their requirements on end-to-end latency and reliability. We then investigate the various sources of end-to-end delay of current wireless networks by taking the 4G Long Term Evolution (LTE) as an example. Subsequently, we propose and evaluate several techniques to reduce the end-to-end latency from the perspectives of error control coding, signal processing, and radio resource management. We also briefly discuss other network design approaches with the potential for further latency reduction.There is a general consensus that the future of many industrial control, traffic safety, medical, and internet services depends on wireless connectivity with guaranteed consistent latencies of 1ms or less and exceedingly stringent reliability of BLERs as low as 10 -9 [3]. While the projected enormous capacity growth is achievable through conventional methods of moving to higher parts of the radio spectrum and network densifications, significant reductions in latency, while guaranteeing an ultra-high reliability, will involve a departure from the underlying theoretical principles of wireless communications. II. Emerging URLLC ApplicationsIn this section, we briefly introduce three emerging mission-critical applications, including telesurgery, intelligent transportation, and industry automation, whose latency and reliability requirements will be identified. Other possible applications of URLLC include Tactile Internet, augmented/virtual reality, fault detection, frequency and voltage control in smart grids, which are not elaborated here due to space limitation. A. Tele-surgeryThe application of URLLC in tele-surgery has two main use cases [4]: (1) remote surgical consultations, and (2) remote surgery. The remote surgical consultations can occur during complex life-saving procedures after serious accidents with patients having health emergency that cannot wait to be transported to a hospital. In such cases, first-responders at an accident venue may need to connect to surgeons in hospital to get advice and guidance to conduct complex medical operations. On the other hand, in a remote surgery scenario, the entire treatment procedure of patients is executed by a surgeon at a remote site, where hands are replaced by robotic arms. In these two use cases, the communication networks should be able to

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Section: Ultra-fast Signal Processingmentioning

confidence: 99%

Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches

et al. 2018

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“…The usage of FPGA-based systems in the telecommunication industry is widespread, with typical applications in the development of general transmission decoders [16,17], video decoders [18,19], security approaches like parity checks [20,21], satellite issues [22,23], and digital TV systems [24]. The modern usage of those devices includes machine learning proposals [25], cloud computing [26], and 5G communication networks [27].…”

Section: Related Workmentioning

confidence: 99%

Hardware Update through Digital TV Signals

et al. 2021

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This paper presents a new hardware reconfiguration approach named hardware reconfiguration through digital television (HARD), which can update FPGA hardware modules based on digital TV (DTV) signals. Such a scheme allows several synthesized hardware cores (bitstreams) signaled and broadcast through open DTV signals via data streaming to be identified, acquired, decoded, and then used for system updates. Reconfiguration data are partitioned, encapsulated into private sections, and then sent in a carrousel fashion in order to be recovered by modified receivers. Service information content, specially designed for identifying and describing the characteristics of multiplexed hardware bitstreams, was added to the transmitted signal and provided all necessary information in the traditional DTV style. The receiver framework, in turn, checked whether those characteristics corresponded to its embedded reconfigurable devices and, if a match was found, it reassembled the related bitstreams and reconfigured the respective internal circuits. Experiments performed with an implementation of the proposed methodology confirmed its feasibility and showed that remounting and reconfiguration times were satisfactory and presented no blocking aspect. Finally, HARD can be used in several designs regarding intelligent reconfigurable devices, minimize device costs in the long term, and provide better hardware reuse.

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“…Several works have adopted the key perspective of pushing promising algorithms from a simple software status to a fully-functional hardware prototype [17]- [19]. More than proving the concept, this perspective significantly reduces time-to-market for new outstanding application-oriented techniques.…”

Section: Fpgamentioning

confidence: 99%

FPGA-SDR Integration and Experimental Validation of a Joint DA ML SNR and Doppler Spread Estimator for 5G Cognitive Transceivers

Haggui¹,

Affes²,

Bellili

2019

IEEE Access

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In a multi-connected, multi-technology, and pervasive mobile infrastructure, such as what is being planned for 5G, artificial intelligence and cognition will play a major role. An important goal of future mobile infrastructures is to self-adapt their characteristics to their operating conditions, at the physical link, as well as at the network and application layers, which gives rise to a new paradigm known as context-aware cognitive radio (CR). CR transceivers (CTRs) mostly incorporate a cognitive engine that relies on various sensorial entities, which attempt to provide sufficient information about the quality of the link through the estimation of various key channel parameters. Two important parameters are required in a wide range of CTR architectures: the signal-to-noise ratio (SNR) and the Doppler spread. Within this context, we tackle the hardware design and integration of a joint data-aided (DA) maximum likelihood (ML) SNR and Doppler spread estimator recently shown to outperform main state-of-the-art solutions both in terms of accuracy and complexity. We propose a deep-pipelined and resource-efficient architecture for the outlined joint DA ML estimator, and we integrate our design on an FPGA-based software-defined radio (SDR) platform. We finally validate and test this prototype in real time under realistic over-the-air propagation conditions reproduced by a highly-scalabile channel emulator. Compared to its MATLAB floating-point version, our hardware prototype suggests negligible losses in performance despite the existence of several hardware impairments, thereby confirming its very strong potential and attractiveness for possible integration in future 5G CTRs.

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1.5 Gbit/s FPGA Implementation of a Fully-Parallel Turbo Decoder Designed for Mission-Critical Machine-Type Communication Applications

Cited by 26 publications

References 47 publications

Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches

Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches

Hardware Update through Digital TV Signals

FPGA-SDR Integration and Experimental Validation of a Joint DA ML SNR and Doppler Spread Estimator for 5G Cognitive Transceivers

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