Low‐complexity multiple‐signal joint decoding for overlapped x domain multiplexing signalling
Authors: 
Peng Lin, Yafeng Wang, Daoben LiAuthors Info & Claims
Abstract
By multiplexing waveform mapped with shifting and weighted symbols, overlapped x domain multiplexing (OVXDM) encoding scheme can obtain high spectral efficiency under a low signal‐to‐noise ratio. Though the maximum likelihood sequence detection decoders can obtain the optimal decoding performance, their computational complexities which increase exponentially with spectral efficiency in OVXDM are unbearable for practical implementations. In this study, a low‐complexity multiple‐signal joint decoding (MSJD) algorithm for OVXDM signalling is proposed over additive white Gaussian noise (AWGN) channel and extended to frequency selective fading (FSF) channel. Based on a novel joint information‐based metric, the proposed algorithm utilises information of each transmitted symbol contained in multiple received signals, and selects the proper number of received signals for decoding to achieve performance maximisation. Moreover, theoretical analysis is provided for the proposed algorithm in both AWGN and FSF channels, which indicates the relationship between performance and number of received signals under different OVXDM encoding parameters including multiplexing waveform and overlapping fold. Simulation results confirm the superior performance of the MSJD algorithm in terms of decoding performance and computational complexity compared with the present approaches.
References
[1]
IMT‐2020 (5G) Promotion Group : ‘White Paper on 5G Network Architecture Design’. Available at http://www.imt‐2020.cn/en/documents/down‐load/27, May 2016
[2]
Yu Y.: ‘Mobile edge computing towards 5G: vision, recent progress, and open challenges’, China Commun., 2016, 13, (2), pp. 89–99
[3]
Li D.: ‘An overlapped time division multiplexing transmission scheme’. PCT application number: PCT/CN2006/001585
[4]
Li D.: ‘A novel high spectral efficiency waveform coding‐OVTDM’, Int. J. Wirel. Commun. Mob. Comput., Special Issue: 5G Wirel. Commun. Syst., 2014, 2, (4‐1), pp. 11–26
[5]
Li D.: ‘Overlapped multiplexing principe and an improved capacity on additive white Gaussian noise channel’, IEEE Access, 2017, 6, (2), pp. 1–10
[6]
Cover T.M., Thomas J.A.: ‘Elements of information theory’ (John Wiley & Sons, New York, USA, [M]. [S. l.], 2006)
[7]
Li D.: ‘A high spectral efficient waveform encoding OVTDM theory and applications’ (China Academic Press, Beijing, China, 2013)
[8]
Fan Z., Xihong Z.: ‘Transmission and encoding research on life status data in WSN combined with OVTDM and CS’. 2015 2nd Int. Symp. on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi‐HealthTech), Bejing, August 2015, pp. 1–4
[9]
Li D.: ‘A novel high spectral efficiency waveform coding‐OVFDM’, China Commun., 2015, 12, (2), pp. 61–73
[10]
Dunham J., Tzoug K.‐H.: ‘Performance bounds for convolutional codes with digital viterbi decoders in Gaussian noise’, IEEE Trans. Commun., 1983, 31, (10), pp. 1124–1132
[11]
Chitre M.A., Potter J.R., Ong S.H.: ‘Viterbi decoding of convolutional codes in symmetric α‐stable noise’, IEEE Trans. Commun., 2007, 55, (12), pp. 2230–2233
[12]
Zhao D., Li D., Jin X.et al.: ‘Sphere‐decoding of OVTDM’. 2007 3rd Int. Workshop on Signal Design and its Applications in Communications, Chengdu, December 2007, pp. 22–25
[13]
Peng A.Y.‐C., Kim I.‐M., Yousefi S.: ‘Low‐complexity sphere decoding algorithm for quasi‐orthogonal space‐time block codes’, IEEE Trans. Commun., 2006, 54, (3), pp. 377–382
[14]
Zhao W., Giannakis G.B.: ‘Sphere decoding algorithms with improved radius search’, IEEE Trans. Commun., 2005, 53, (7), pp. 377–382
[15]
Zhang Q., Wang K., Liu A.et al.: ‘Decoding overlapped time division multiplexing system with Fano algorithm’. 10th Int. Conf. on Wireless Communications, Networking and Mobile Computing, Beijing, 2014, pp. 284–288
[16]
Niu K., Chen K., Lin J.: ‘Low‐complexity sphere decoding of polar codes based on optimum path metric’, IEEE Commun. Lett., 2014, 18, (2), pp. 332–335
[17]
Anderson J.B., Mohan S.: ‘Sequential coding algorithms: a survey and cost analysis’, IEEE Trans. Commun., 1984, 32, (2), pp. 169–176
[18]
Xu R., Kocak T., Woodward G.et al.: ‘High throughput parallel Fano decoding’, IEEE Trans. Commun., 2011, 59, (9), pp. 2394–2405
[19]
Jelinek F.: ‘Fast sequential decoding algorithm using a stack’, IBM J. Res. Dev., 1969, 13, (6), pp. 675–685
[20]
Zhang H., Chen Y., Li D.et al.: ‘Low‐complexity sliding window block decoding using bit‐flipping for OVFDM systems’, IEEE Acess, 2017, PP, (99), pp. 1–10
[21]
Jiang H., Li D., Li W.: ‘Performance analysis of overlapped multiplexing techniques’. Proc. 3rd Int. Workshop on Signal Design and its Applications in Communications, Chengdu, China, September 2007, vol. 3233–237
[22]
Viterbi A.J.: ‘An intuitive justification and a simplified implementation of the MAP decoder for convolutional codes’, IEEE J. Sel. Areas Commun.’, 1998, 16, (2), pp. 260–264
[23]
Nguyen D.‐H., Nguyen H.: ‘An improved Log‐MAP algorithm based on polynomial regression function for LTE Turbo decoding’. 2015 IEEE Int. Conf. Communication Workshop (ICCW), London, February 2015, pp. 2613–2617
[24]
Stamoulis A., Giannakis G.B., Scaglione A.: ‘Block FIR decision‐feedback equalizers for filterbank precoded transmissions with blind channel estimation capabilities’, IEEE Trans. Commun., 2001, 49, (1), pp. 69–83
[25]
Li Y., Wang L., Ding Z.: ‘An integrated linear programming receiver for LDPC coded MIMO‐OFDM signals’, IEEE Trans. Commun., 2013, 61, (7), pp. 2816–2827
[26]
Jacklin N., Ding Z.: ‘On efficient utilization of training pilots for linear equalization of QAM transmissions’, IEEE Trans. Commun., 2013, 61, (12), pp. 4984–4996
[27]
Wang K., Shen H., Wu W.et al.: ‘Joint detection and decoding in LDPC‐based space‐time coded MIMO‐OFDM systems via linear programming’, IEEE Trans. Signal Process., 2015, 63, (13), pp. 3411–3424
[28]
Proakis J.G., Salehi M.: ‘Digital communications’ (McGraw‐Hill Higher Education, New York, USA, 2008, 5th edn.)
[29]
Blahut R.E.: ‘Principles and practice of information theory’ (Addison Wesley Publishing Company, Boston, MA, USA, 1988), (ISBN 0‐201‐10709‐0)
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Information
Published In
© 2021 The Institution of Engineering and Technology.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 22 May 2018
Author Tags
Author Tags
- low‐complexity multiple‐signal
- overlapped x domain
- multiplexing waveform
- shifting symbols
- weighted symbols
- (OVXDM) encoding scheme
- high spectral efficiency
- low signal‐to‐noise ratio
- maximum likelihood sequence detection decoders
- optimal decoding performance
- computational complexity
- multiple‐signal joint decoding algorithm
- OVXDM signalling
- additive white Gaussian noise channel
- frequency selective fading channel
- novel joint information
- algorithm utilises information
- transmitted symbol
- multiple received signals
- performance maximisation
- different OVXDM encoding parameters
- overlapping fold
- MSJD algorithm
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