A Distributed Decoding Algorithm for 6G Internet-of-Things Networks

Weijie YUAN; Shuangyang LI; Ruoxi CHONG; Baoming BAI; NG D W K

doi:10.11999/JEIT200343

Volume 43 Issue 1

Jan. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(1): 21-27

Weijie YUAN, Shuangyang LI, Ruoxi CHONG, Baoming BAI, NG D W K. A Distributed Decoding Algorithm for 6G Internet-of-Things Networks[J]. Journal of Electronics & Information Technology, 2021, 43(1): 21-27. doi: 10.11999/JEIT200343

Citation:

Weijie YUAN, Shuangyang LI, Ruoxi CHONG, Baoming BAI, NG D W K. A Distributed Decoding Algorithm for 6G Internet-of-Things Networks[J]. Journal of Electronics & Information Technology, 2021, 43(1): 21-27. doi: 10.11999/JEIT200343

Citation:

PDF( 1067 KB)

A Distributed Decoding Algorithm for 6G Internet-of-Things Networks

doi: 10.11999/JEIT200343

1.
School of Electrical Engineering and Telecommunications, Univ. of New South Wales, Sydney 2032, Australia
2.
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China

Received Date: 2020-04-30
Rev Recd Date: 2020-12-08

Available Online: 2020-12-14

Publish Date: 2021-01-15

Abstract

Abstract

With the standardization and the commercialization of 5G, the research on 6G technology is started. The Internet-of-Things (IoT) draws substantial interests in recent years due to its great potential for several applications in 6G wireless communication systems. As massive access and explosive data transmission are expected, the robustness and scalability are two key aspects for 6G IoT networks. In IoT networks, the said “things” (users) can collect environmental data in real time by adopting various multi-functional wireless sensors. Conventionally, the collected data are feedback to a central unit for further processing. However, the performance of this scheme relies on the normal operation of the central unit, which is not robust to the malfunction of central unit. This paper proposes a distributed decoding algorithm that the decoding is done at local users by enabling the cooperation and information exchange between users. As a result, each user achieves a decoding performance similar to that of the centralized approach which improves the robustness and the scalability of the network. Meanwhile, compared to the conventional distributed decoding approach, the proposed algorithm does not require that each user has the perfect knowledge of the network topology. Therefore, the proposed algorithm lays the foundation of 6G IoT networks.
- 6G,
- Internet-of-Things (IoT),
- Decoding algorithm,
- Message passing

FullText(HTML)

References(15)

References

WONG V W S, SCHOBER R, NG D W K, et al. Key Technologies for 5G Wireless Systems[M]. Cambridge, UK: Cambridge University Press, 2017: 55–83.

尤肖虎, 尹浩, 邬贺铨. 6G与广域物联网[J]. 物联网学报, 2020, 4(1): 3–11. doi: 10.11959/j.issn.2096-3750.2020.00158

YOU Xiaohu, YIN Hao, and WU Hequan. On 6G and wide-area IoT[J]. Chinese Journal on Internet of Things, 2020, 4(1): 3–11. doi: 10.11959/j.issn.2096-3750.2020.00158

DAVID K and BERNDT H. 6G vision and requirements: Is there any need for beyond 5G?[J]. IEEE Vehicular Technology Magazine, 2018, 13(3): 72–80. doi: 10.1109/MVT.2018.2848498

ATZORI L, IERA A, and MORABITO G. The internet of things: A survey[J]. Computer Networks, 2010, 54(15): 2787–2805. doi: 10.1016/j.comnet.2010.05.010

FENG Shuo, SETOODEH P, and HAYKIN S. Smart home: Cognitive interactive people-centric Internet of Things[J]. IEEE Communications Magazine, 2017, 55(2): 34–39. doi: 10.1109/MCOM.2017.1600682CM

FIGUEIREDO L, JESUS I, MACHADO J A T, et al. Towards the development of intelligent transportation systems[C]. 2001 IEEE Intelligent Transportation Systems Conference, Oakland, USA, 2001: 1206–1211.

BOCKELMANN C, PRATAS N, NIKOPOUR H, et al. Massive machine-type communications in 5G: Physical and MAC-layer solutions[J]. IEEE Communications Magazine, 2016, 54(9): 59–65. doi: 10.1109/MCOM.2016.7565189

杨洋, 陈超, 白宝明, 等. LDPC码串行译码策略的收敛速度分析[J]. 西安电子科技大学学报: 自然科学版, 2010, 37(5): 795–800. doi: 10.3969/J.ISSN.1001-2400.2010.05.004

YANG Yang, CHEN Chao, BAI Baoming, et al. Analysis of the convergence rate of serial schedule based decoding for LDPC codes[J]. Journal of Xidian University, 2010, 37(5): 795–800. doi: 10.3969/J.ISSN.1001-2400.2010.05.004

GUNTURU A and CHAVVA A K R. Opportunistic early decoding for NB-IoT devices using link abstraction based on RBIR metric[C]. 2018 IEEE Wireless Communications and Networking Conference, Barcelona, Spain, 2018: 1–6.

LI Shihua, DU Haibo, and LIN Xiangze. Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics[J]. Automatica, 2011, 47(8): 1706–1712. doi: 10.1016/J.AUTOMATICA.2011.02.045

LYNCH N A. Distributed Algorithms[M]. San Francisco: Elsevier, 1996: 122–150.

ZHU Hao, GIANNAKIS G B, and CANO A. Distributed in-network channel decoding[J]. IEEE Transactions on Signal Processing, 2009, 57(10): 3970–3983. doi: 10.1109/TSP.2009.2023936

DHULI S, GAURAV K, and SINGH Y N. Convergence analysis for regular wireless consensus networks[J]. IEEE Sensors Journal, 2015, 15(8): 4522–4531. doi: 10.1109/JSEN.2015.2420952

KSCHISCHANG F R, FREY B J, and LOELIGER H A. Factor graphs and the sum-product algorithm[J]. IEEE Transactions on Information Theory, 2001, 47(2): 498–519. doi: 10.1109/18.910572

吴皓威, 武小飞, 邹润秋, 等. 空间耦合LDPC码的分层译码算法[J]. 电子与信息学报, 2020, 42(8): 1881–1887. doi: 10.11999/JEIT190626

WU Haowei, WU Xiaofei, ZOU Runqiu, et al. A layered decoding algorithm for spatially-coupled LDPC codes[J]. Journal of Electronics &Information Technology, 2020, 42(8): 1881–1887. doi: 10.11999/JEIT190626

Relative Articles

Supplements(0)

Cited By

Proportional views