Deep Learning Based Multi-user Interference Cancellation Technology

Changyun Zhang

doi:doi:10.11648/j.sd.20190706.11

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Deep Learning Based Multi-user Interference Cancellation Technology

Changyun Zhang

Published in Science Discovery (Volume 7, Issue 6)

Received: 4 November 2019 Published: 9 December 2019

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Abstract

In the paper, I proposed a neural network-based solution to multiple access interference under the Multi-antenna Input and Multi-antenna Output (MIMO) communication system. In a model of the uplink and downlink of the multiuser MIMO system. In cases of multiple access interference, each transmitter were designed with neural networks, after the transmitted signal passes through the channel, detecting received signals at receivers designed by neural network. The model could eliminate the interference between different users. The neural network-designed model adopted Rician fading channel (including Rayleigh fading channel) and simulated the Symbol Error Rate (SER) performance of multiple users under different signal-noise ratios. With respect to SER, the solution improved system performance compared with the current multiple access interference cancellation technology. Therefore, communication systems designed with neural networks face a promising future in multiple access interference cancellation.

Published in	Science Discovery (Volume 7, Issue 6)
DOI	10.11648/j.sd.20190706.11
Page(s)	379-384
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

MIMO, Neural Network, Multi-user Interference Cancellation Technology, Symbol Error Rate

References

[1]	A. Kawagoe, N. Honma and N. Takemura, "Imp-act of user terminal antenna spacing on inter-terminal interference cancellation in multi-user full duplex MIMO transmission," 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, 2017, pp. 1-4.
[2]	W. Gao, S. He and G. Chuai, "Clusteringfor coordinated zero-forcing beamformingin multi-user interference networks," 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), Beijing, 2016, pp. 322-326.
[3]	王皎,强永全,李道本. 迭代迫零多用户检测算法研究与应用[J]. 无线电工程(11):11-13,19.
[4]	P. P. Vaidyanathan, "On the degree of MIMO systems," 2007 IEEE International Symposium on Circuits and Systems, New Orleans, LA, 2007, pp. 661-664.
[5]	X. Xie, H. Yang and A. V. Vasilakos, "Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks With Channel Uncertainty," in IEEE Access, vol. 5, pp. 5121-5134, 2017.
[6]	A. Balatsoukas-Stimming, O. Castañeda, S. Jacobsson, G. Durisi and C. Studer, "Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO," 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. 1-5.
[7]	I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
[8]	王珏, 石纯一. 机器学习研究[J]. 广西师范大学学报(自然科学版), 2003(02):4-18.
[9]	C. Zhang, P. Patras and H. Haddadi, "Deep Learning in Mobile and Wireless Networking: A Survey," in IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, thirdquarter 2019.
[10]	H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu and N. D. Sidiropoulos, "Learning to Optimize: Training Deep Neural Networks for Interference Management," in IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, 15 Oct.15, 2018.
[11]	T. O’Shea and J. Hoydis, "An Introduction to Deep Learning for the Physical Layer," in IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017.
[12]	T. Erpek, T. J. O'Shea and T. C. Clancy, "Learning a Physical Layer Scheme for the MIMO Interfere-nce Channel," 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, 2018, pp. 1-5.
[13]	M. A. Wijaya, K. Fukawa and H. Suzuki, "Intercell-Interference Cancellation and Neural Network Transmit Power Optimization for MIMO Channels," 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, 2015, pp. 1-5.
[14]	N. Samuel, T. Diskin and A. Wiesel, "Learning to Detect," in IEEE Transactions on Signal Processing, vol. 67, no. 10, pp. 2554-2564, 15 May15, 2019.
[15]	J. Yang and J. Song, "Multi-user detection under wireless channel," 2009 IEEE International Conference on Communications Technology and Applications, Beijing, 2009, pp. 581-585.

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Changyun Zhang. (2019). Deep Learning Based Multi-user Interference Cancellation Technology. Science Discovery, 7(6), 379-384. https://doi.org/10.11648/j.sd.20190706.11

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Changyun Zhang. Deep Learning Based Multi-user Interference Cancellation Technology. Sci. Discov. 2019, 7(6), 379-384. doi: 10.11648/j.sd.20190706.11

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AMA Style

Changyun Zhang. Deep Learning Based Multi-user Interference Cancellation Technology. Sci Discov. 2019;7(6):379-384. doi: 10.11648/j.sd.20190706.11

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@article{10.11648/j.sd.20190706.11,
  author = {Changyun Zhang},
  title = {Deep Learning Based Multi-user Interference Cancellation Technology},
  journal = {Science Discovery},
  volume = {7},
  number = {6},
  pages = {379-384},
  doi = {10.11648/j.sd.20190706.11},
  url = {https://doi.org/10.11648/j.sd.20190706.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20190706.11},
  abstract = {In the paper, I proposed a neural network-based solution to multiple access interference under the Multi-antenna Input and Multi-antenna Output (MIMO) communication system. In a model of the uplink and downlink of the multiuser MIMO system. In cases of multiple access interference, each transmitter were designed with neural networks, after the transmitted signal passes through the channel, detecting received signals at receivers designed by neural network. The model could eliminate the interference between different users. The neural network-designed model adopted Rician fading channel (including Rayleigh fading channel) and simulated the Symbol Error Rate (SER) performance of multiple users under different signal-noise ratios. With respect to SER, the solution improved system performance compared with the current multiple access interference cancellation technology. Therefore, communication systems designed with neural networks face a promising future in multiple access interference cancellation.},
 year = {2019}
}

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TY  - JOUR
T1  - Deep Learning Based Multi-user Interference Cancellation Technology
AU  - Changyun Zhang
Y1  - 2019/12/09
PY  - 2019
N1  - https://doi.org/10.11648/j.sd.20190706.11
DO  - 10.11648/j.sd.20190706.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 379
EP  - 384
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20190706.11
AB  - In the paper, I proposed a neural network-based solution to multiple access interference under the Multi-antenna Input and Multi-antenna Output (MIMO) communication system. In a model of the uplink and downlink of the multiuser MIMO system. In cases of multiple access interference, each transmitter were designed with neural networks, after the transmitted signal passes through the channel, detecting received signals at receivers designed by neural network. The model could eliminate the interference between different users. The neural network-designed model adopted Rician fading channel (including Rayleigh fading channel) and simulated the Symbol Error Rate (SER) performance of multiple users under different signal-noise ratios. With respect to SER, the solution improved system performance compared with the current multiple access interference cancellation technology. Therefore, communication systems designed with neural networks face a promising future in multiple access interference cancellation.
VL  - 7
IS  - 6
ER  -

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Author Information

Changyun Zhang

Department of Electronics and Information Engineering, Shenzhen University, Shenzhen, China

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Changyun Zhang. (2019). Deep Learning Based Multi-user Interference Cancellation Technology. Science Discovery, 7(6), 379-384. https://doi.org/10.11648/j.sd.20190706.11

Copy | Download

ACS Style

Changyun Zhang. Deep Learning Based Multi-user Interference Cancellation Technology. Sci. Discov. 2019, 7(6), 379-384. doi: 10.11648/j.sd.20190706.11

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AMA Style

Changyun Zhang. Deep Learning Based Multi-user Interference Cancellation Technology. Sci Discov. 2019;7(6):379-384. doi: 10.11648/j.sd.20190706.11

Copy | Download

@article{10.11648/j.sd.20190706.11,
  author = {Changyun Zhang},
  title = {Deep Learning Based Multi-user Interference Cancellation Technology},
  journal = {Science Discovery},
  volume = {7},
  number = {6},
  pages = {379-384},
  doi = {10.11648/j.sd.20190706.11},
  url = {https://doi.org/10.11648/j.sd.20190706.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20190706.11},
  abstract = {In the paper, I proposed a neural network-based solution to multiple access interference under the Multi-antenna Input and Multi-antenna Output (MIMO) communication system. In a model of the uplink and downlink of the multiuser MIMO system. In cases of multiple access interference, each transmitter were designed with neural networks, after the transmitted signal passes through the channel, detecting received signals at receivers designed by neural network. The model could eliminate the interference between different users. The neural network-designed model adopted Rician fading channel (including Rayleigh fading channel) and simulated the Symbol Error Rate (SER) performance of multiple users under different signal-noise ratios. With respect to SER, the solution improved system performance compared with the current multiple access interference cancellation technology. Therefore, communication systems designed with neural networks face a promising future in multiple access interference cancellation.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Deep Learning Based Multi-user Interference Cancellation Technology
AU  - Changyun Zhang
Y1  - 2019/12/09
PY  - 2019
N1  - https://doi.org/10.11648/j.sd.20190706.11
DO  - 10.11648/j.sd.20190706.11
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 379
EP  - 384
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20190706.11
AB  - In the paper, I proposed a neural network-based solution to multiple access interference under the Multi-antenna Input and Multi-antenna Output (MIMO) communication system. In a model of the uplink and downlink of the multiuser MIMO system. In cases of multiple access interference, each transmitter were designed with neural networks, after the transmitted signal passes through the channel, detecting received signals at receivers designed by neural network. The model could eliminate the interference between different users. The neural network-designed model adopted Rician fading channel (including Rayleigh fading channel) and simulated the Symbol Error Rate (SER) performance of multiple users under different signal-noise ratios. With respect to SER, the solution improved system performance compared with the current multiple access interference cancellation technology. Therefore, communication systems designed with neural networks face a promising future in multiple access interference cancellation.
VL  - 7
IS  - 6
ER  -

Copy | Download