Preview

Journal of the Russian Universities. Radioelectronics

Advanced search

Optimization of Iterative Channel State Estimation Algorithms in SCMA System

https://doi.org/10.32603/1993-8985-2018-21-3-23-34

Abstract

Optimization of iterative algorithms for channel state estimation in a sparse coding multiple-access system (SCMA) is performed to reduce computational costs of the receiver. It is shown that when a signal-to-noise ratio (SNR) does not exceed 10 dB, one iteration of the algorithm is sufficient, and an increase in the number of iterations does not lead to an increase in spectral efficiency. Simulation demonstrates a possibility of a reasonable choice of the total number of decoder iterations and their distribution between different stages of the channel estimation. For an uncoded system, iterative re-estimation of the channel is proposed, as well as ways to reduce computational costs during its calculation. In the coded system, at a low SNR the achieved spectral efficiency values are approximately similar to those with pilot-only channel estimation. The article provides recommendations for the placement of data symbols and pilot signals in re-source blocks to increase the system spectral efficiency.

About the Authors

V. P. Klimentyev
Saint Petersburg Electrotechnical University "LETI".
Russian Federation

Vyacheslav P. Klimentyev – Master’s Degree in radio engineering (2013), postgraduate student of the Department of Theoretical Fundamentals of Radio Engineering of Saint Petersburg Electrotechnical University "LETI". The author of 18 scientific publications. Area of expertise: signal processing; digital communications. 

5, Professor Popov Str., 197376, St. Petersburg.



A. B. Sergienko
Saint Petersburg Electrotechnical University "LETI".
Russian Federation

Alexander B. – PhD in Engineering (1995), Associate Professor (1998), Professor (2018) of the Department of Theoretical Fundamentals of Radio Engineering of Saint Petersburg Electrotechnical University "LETI", IEEE member (1998). The author of 102 scientific publications. Area of expertise: signal processing; digital communications.

5, Professor Popov Str., 197376, St. Petersburg



References

1. 5G; NR; Physical channels and modulation (3GPP TS 38.211 version 15.0.0 Release 15). ETSI. Sophia Antipolis, France, 2017, 219 p.

2. Andrews J. G., Buzzi S., Choi W., Hanly S. V., Lozano A., Soong A. C. K., Zhang J. C. What Will 5G Be? IEEE J. on Selected Areas in Communications. 2014, vol. 32, no. 6, pp. 1065–1082.

3. Dai L., Wang B., Yuan Y., Han S., Wang C. I, Z. Nonorthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends. IEEE Commun. Mag. 2015, vol. 53, no. 9, pp. 74–81.

4. Nikopour H., Baligh H. Sparse Code Multiple Access. Proc. IEEE 24th Intern. Symp. On Personal, Indoor and Mobile Radio Comm. (PIMRC 2013), London, 8–9 Sept. 2013. Piscataway, IEEE, 2013, pp. 332–336.

5. Hoshyar R., Wathan F. P., Tafazolli R. Novel LowDensity Signature for Synchronous CDMA Systems over AWGN Channel. IEEE Trans. Signal Proc. 2008, vol. 56, no. 4, pp. 1616–1626.

6. Dai X., Chen S., Sun S., Kang S., Wang Y., Shen Z., Xu J. Successive interference cancelation amenable multiple access (SAMA) for future wireless communications. Proc. of the 2014 IEEE Intern. Conf. Communication Systems (ICCS), Macau, 19–21 Nov. 2014. Piscataway, IEEE, 2014, pp. 222–226.

7. Pratschner S., Zöchmann E., Rupp M. Low Complexity Estimation of Frequency Selective Channels for the LTE-A Uplink. IEEE Wireless Communications Letters. 2015, vol. 4, no. 4, pp. 673–676.

8. Wang Y., Zhou S., Xiao L, Zhang X. Sparse Bayesian learning based user detection and channel estimation for SCMA uplink systems. Intern. Conf. on Wireless Comm. and Signal Proc. (WCSP'15). Nanjing, 15–17 Oct. 2015. Piscataway, IEEE, 2015, pp. 1–5.

9. Wu Y., Zhang S., Chen Y. Iterative multiuser receiver in sparse code multiple access systems. IEEE Intern. Conf. on Comm., London, 8–12 June 2015. Piscataway, IEEE, 2015, pp. 2918–2923.

10. Chu D. Polyphase codes with good periodic correlation properties. IEEE Trans. on Information Theory. 1972, vol. 18, no. 4, pp. 531–532.

11. Results and Remaining Issues of LLS evaluation on Multiple Access, 3GPP TSG RAN WG1 Meeting #86 R1167105, Gothenburg, Sweden, 22–26 Aug. 2016. Available at: http://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_86/Docs /R1-167105.zip (accessed: 09.03.2018).

12. Purkovic A., Johnson B. F., Jovanovic S., Tretter S. A. US Pat. 2009/0300463 A1. Int. Cl. H03M 13/29, G06F 11/10, H03M 13/05. System and method for determining parity bit soft information at a turbo decoder output. Publ. 27.05.2009.

13. Liu J, Wu G., Li S., Tirkkonen O. On Fixed-point Implementation of Log-MPA for SCMA Signals. IEEE Wireless Communications Letters. 2016, vol. 99, pp. 1–4.

14. Apolinario J. A. QRD-RLS Adaptive Filtering. New York, Springer US, 2009, 356 p.

15. Altera Innovate Asia. Presentation "1st 5G Algorithm Innovation Competition-ENV1.0-SCMA". Available at: http://www.innovateasia.com/5g/images/pdf/1st%205G%20Algorithm%20Innovation%20Competition-ENV1.0%20-%20SCMA.pdf (accessed: 09.03.2018).

16. LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding (3GPP TS 36.212 version 10.0.0 Release 10). ETSI. Sophia Antipolis, France, 2011, 73 p.

17. Vameghestahbanati M., Bedeer E., Marsland I., Gohary R. H., Yanikomeroglu H. Enabling Sphere Decoding for SCMA. IEEE Communications Letters. 2017, vol. 21, no. 12, pp. 2750–2753.


Review

For citations:


Klimentyev V.P., Sergienko A.B. Optimization of Iterative Channel State Estimation Algorithms in SCMA System. Journal of the Russian Universities. Radioelectronics. 2018;(3):23-34. (In Russ.) https://doi.org/10.32603/1993-8985-2018-21-3-23-34

Views: 595


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1993-8985 (Print)
ISSN 2658-4794 (Online)