The Effectiveness of LDPC Decoding Algorithms in 5G Channel Modelling of MIMO-OFDM System under The Influence of Spatial Correlation
Main Article Content
Abstract
This paper investigates the spatial cross-correlation properties of 5G channel modeling in non-line of sight (NLOS) of Urban Micro Cell (UMi), Rural Macro Cell (RMa) and Indoor cell (InH) at 6 GHz frequency band as Third Generation Partnership Project (3GPP) specification. In our spatial correlation Multiple-input multiple-output (MIMO) channel, if increasing the distances of BS antenna elements, the correlation characteristics of the channel dramatically vary, that leads to alter the observed system behavior. By analyzing the 5G LDPC code and using different decoding algorithms and code rates, we evaluate the system’s performance by Bit Error Rate (BER) in wideband correlated Multiple-input multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. In our spatial correlation, the more rising of the distances of antenna elements in the BS side, the better of the effectiveness of the LDPC decoding algorithms achieved. Of the Belief Propagation based (BP-based) Algorithm, Offset Minimum-Sum (OMS) Algorithm and Linear Approximation Minimum-Sum (LAMS) Algorithm, we propose to use the LAMS decoding algorithm with high code rate 5/6 in our spatial correlated wideband channel because of lower complexity and more efficiency in term of the system’s performance
Keywords
MIMO-OFDM, 5G channel modeling, spatial cross-correlation, LDPC, LDPC decoding algorithm
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
1. Thu Nga Nguyen, Van Duc Nguyen, A performance
comparison of the SCM and the onering channel
modelling method for MIMO‐OFDMA systems, in
Wireless Communications Mobile Computing,
Volume16, Issue17, pp. 3123–3138, 10/2016.
https://doi.org/10.1002/wcm.2730.
2. ETSI TR 138 901 V14.0.0 5G, Study on channel model
for frequencies from 0.5 to 100 GHz (3GPP TR 38.901
version 14.0.0 Release 14), ETSI, Tech. Rep, 5/2017.
3. C. -X. Wang, J. Bian, J. Sun, W. Zhang, M. Zhang, A
survey of 5G channel measurements and models, in
IEEE Communications Surveys & Tutorials, vol. 20, no.
4, pp. 3142-3168, Fourth quarter 2018.
https://doi.org/10.1109/COMST.2018.2862141.
4. Eldowek, B.M., Abd El-atty, S.M., El-Rabaie, ES.M. et
al., 3D modeling and analysis of the space–time
correlation for 5g millimeter wave MIMO channels.
Wireless Pers Commun 104, pp. 783–799 (2019).
https://doi.org/10.1007/s11277-018-6051-4.
5. M. K. Samimi, S. Sun, T. S. Rappaport, MIMO channel
modeling and capacity analysis for 5G millimeter-wave
wireless systems, 2016 10th European Conference on
Antennas and Propagation (EuCAP), 2016, pp. 1-5.
https://doi.org/10.1109/EuCAP.2016.7481507.
6. Y. Yu, P. J. Smith, P. A. Dmochowski, J. Zhang and M.
Shafi, 3D vs. 2D channel models: Spatial correlation and
channel capacity comparison and analysis, 2017 IEEE
International Conference on Communications (ICC),
2017, pp. 1-7.
https://doi.org/10.1109/ICC.2017.7996405.
7. K. Haneda et al., Indoor 5G 3GPP-like channel models
for office and shopping mall environments, 2016 IEEE
International Conference on Communications
Workshops (ICC), 2016, pp. 694-699.
https://doi.org/ 10.1109/ICCW.2016.7503868.
8. S. Sun et al., Investigation of prediction accuracy,
sensitivity, and parameter stability of large-scale
propagation path loss models for 5g wireless
communications, in IEEE Transactions on Vehicular
Technology, vol. 65, no. 5, pp. 2843-2860, May 2016.
https://doi.org/ 10.1109/TVT.2016.2543139.
9. 3GPP- TR 25.996 V10.0.0. Technical specification
group radio access network spatial channel model for
MIMO simulation, 3/2011.
10. T. Richardson, S. Kudekar, Design of low-density parity
check codes for 5G new radio, in IEEE Communications
Magazine, vol. 56, no. 3, pp. 28-34, March 2018.
https://doi.org/ 10.1109/MCOM.2018.1700839.
11. X. Wu, M. Jiang, C. Zhao, Decoding optimization for 5G
LDPC codes by machine learning, in IEEE Access,
vol. 6, pp. 50179-50186, 2018.
https://doi.org/ 10.1109/ACCESS.2018.2869374.
12. Jinghu Chen, M. P. C. Fossorier, Near optimum
universal belief propagation based decoding of lowdensity parity check codes, IEEE Transactions on
Communications, vol. 50, no. 3, pp. 406-414, Mar 2002.
https://doi.org/ 10.1109/26.990903.
13. S. S. Eldin, M. Nasr, S. Khamees, E. Sourour,
M. Elbanna, LDPC coded MIMO OFDM-based IEEE
802.11n wireless LAN, 2009 IFIP International
Conference on Wireless and Optical Communications
Networks, 2009, pp. 1-5.
https://doi.org/ 10.1109/WOCN.2009.5010560.
14. Y. Xin, S. A. Mujtaba, T. Zhang and J. Jiang, Bypass
decoding: a reduced-complexity decoding technique for
LDPC-Coded MIMO-OFDM systems, in IEEE
Transactions on Vehicular Technology, vol. 57, no. 4,
pp. 2319-2333, July 2008.
https://doi.org/ 10.1109/TVT.2007.9121639.
comparison of the SCM and the onering channel
modelling method for MIMO‐OFDMA systems, in
Wireless Communications Mobile Computing,
Volume16, Issue17, pp. 3123–3138, 10/2016.
https://doi.org/10.1002/wcm.2730.
2. ETSI TR 138 901 V14.0.0 5G, Study on channel model
for frequencies from 0.5 to 100 GHz (3GPP TR 38.901
version 14.0.0 Release 14), ETSI, Tech. Rep, 5/2017.
3. C. -X. Wang, J. Bian, J. Sun, W. Zhang, M. Zhang, A
survey of 5G channel measurements and models, in
IEEE Communications Surveys & Tutorials, vol. 20, no.
4, pp. 3142-3168, Fourth quarter 2018.
https://doi.org/10.1109/COMST.2018.2862141.
4. Eldowek, B.M., Abd El-atty, S.M., El-Rabaie, ES.M. et
al., 3D modeling and analysis of the space–time
correlation for 5g millimeter wave MIMO channels.
Wireless Pers Commun 104, pp. 783–799 (2019).
https://doi.org/10.1007/s11277-018-6051-4.
5. M. K. Samimi, S. Sun, T. S. Rappaport, MIMO channel
modeling and capacity analysis for 5G millimeter-wave
wireless systems, 2016 10th European Conference on
Antennas and Propagation (EuCAP), 2016, pp. 1-5.
https://doi.org/10.1109/EuCAP.2016.7481507.
6. Y. Yu, P. J. Smith, P. A. Dmochowski, J. Zhang and M.
Shafi, 3D vs. 2D channel models: Spatial correlation and
channel capacity comparison and analysis, 2017 IEEE
International Conference on Communications (ICC),
2017, pp. 1-7.
https://doi.org/10.1109/ICC.2017.7996405.
7. K. Haneda et al., Indoor 5G 3GPP-like channel models
for office and shopping mall environments, 2016 IEEE
International Conference on Communications
Workshops (ICC), 2016, pp. 694-699.
https://doi.org/ 10.1109/ICCW.2016.7503868.
8. S. Sun et al., Investigation of prediction accuracy,
sensitivity, and parameter stability of large-scale
propagation path loss models for 5g wireless
communications, in IEEE Transactions on Vehicular
Technology, vol. 65, no. 5, pp. 2843-2860, May 2016.
https://doi.org/ 10.1109/TVT.2016.2543139.
9. 3GPP- TR 25.996 V10.0.0. Technical specification
group radio access network spatial channel model for
MIMO simulation, 3/2011.
10. T. Richardson, S. Kudekar, Design of low-density parity
check codes for 5G new radio, in IEEE Communications
Magazine, vol. 56, no. 3, pp. 28-34, March 2018.
https://doi.org/ 10.1109/MCOM.2018.1700839.
11. X. Wu, M. Jiang, C. Zhao, Decoding optimization for 5G
LDPC codes by machine learning, in IEEE Access,
vol. 6, pp. 50179-50186, 2018.
https://doi.org/ 10.1109/ACCESS.2018.2869374.
12. Jinghu Chen, M. P. C. Fossorier, Near optimum
universal belief propagation based decoding of lowdensity parity check codes, IEEE Transactions on
Communications, vol. 50, no. 3, pp. 406-414, Mar 2002.
https://doi.org/ 10.1109/26.990903.
13. S. S. Eldin, M. Nasr, S. Khamees, E. Sourour,
M. Elbanna, LDPC coded MIMO OFDM-based IEEE
802.11n wireless LAN, 2009 IFIP International
Conference on Wireless and Optical Communications
Networks, 2009, pp. 1-5.
https://doi.org/ 10.1109/WOCN.2009.5010560.
14. Y. Xin, S. A. Mujtaba, T. Zhang and J. Jiang, Bypass
decoding: a reduced-complexity decoding technique for
LDPC-Coded MIMO-OFDM systems, in IEEE
Transactions on Vehicular Technology, vol. 57, no. 4,
pp. 2319-2333, July 2008.
https://doi.org/ 10.1109/TVT.2007.9121639.