A joint early detection-early stopping scheme for short-frame turbo decoding

Ma, Zheng; Fan, Pingzhi; Mow, Wai Ho; Chen, Qingchun

doi:10.1016/j.aeue.2010.01.005

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…The candidates coding schemes could be LDPC or polar codes. Except for adopting low complexity decoding algorithms to reduce the power consumption at decoder, decoding control schemes, such as stopping criteria [57], [58], can be employed to reduce the unnecessary computations for decoders.…”

Section: Channel Coding For Hrll Communicationsmentioning

confidence: 99%

High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

Xiao

et al. 2019

IEEE Internet Things J.

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214

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As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and lowlatency (HRLL) wireless IoT networks. We discuss physical, MAC and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grantfree schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

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Section: Channel Coding For Hrll Communicationsmentioning

confidence: 99%

High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

Xiao

et al. 2019

IEEE Internet Things J.

Self Cite

214

View full text Add to dashboard Cite

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“…The CRC of the ith SU is measured by FC, if the error found is greater than threshold, i.e., ξ i > ξ, the sensing measurement of ith SU is not combined for that particular sensing. The CRC is a technique which is believed to be quite good in terms of error detection and easy to implement in hardware [24,25]. It is based on polynomial arithmetic on computing the remainder of dividing one polynomial in GF(2) (Galois filed with two elements) by another [26].…”

Section: System Modelmentioning

confidence: 99%

Efficient error detection in soft data fusion for cooperative spectrum sensing

Bhatti

Ahmed

Saeed

et al. 2018

AEU - International Journal of Electronics and Communications

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The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme.

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