Abstract
This paper considers the blind detection of spatial modulation systems multiple-input multiple-output systems. In this system, we propose a clustering detection framework with graph theory that conducts signal detection without the training of channel state information. In detail, firstly, by dynamically controlling the size of each cluster, we transform the original optimization problem of the traditional K-means clustering detector into a new optimization problem. In addition, the cluster assignment subproblem of the iterative clustering algorithm for solving the new optimization problem makes it equivalent to a minimum cost flow linear network optimization problem of graph theory, which can be addressed by the breadth-first algorithm. Moreover, a novel clustering detector with the breadth-first algorithm is presented correspondingly. Numerical results show that the proposed detector is efficient in avoiding the undesired local optima and can closely approach the performance of the optimal detector.
Similar content being viewed by others
References
Mesleh, R. Y., et al. (2008). Spatial modulation. IEEE Transactions on Vehicular Technology, 57(4), 2228–2241.
Di Renzo, M., et al. (2014). Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation. Proceedings of IEEE, 102(1), 56–103.
Yang, P., et al. (2015). Design guidelines for spatial modulation. IEEE Communications Surveys and Tutorials, 17(1), 6–26. 1st Quart.
Liang, H. W., et al. (2016). Coding-aided k-means clustering blind transceiver for space shift keying mimo systems. IEEE Transactions on Wireless Communications, 15(1), 103–115.
Rajashekar, R., et al. (2014). Reduced-complexity ML detection and capacity optimized training for spatial modulation systems. IEEE Transactions on Communications, 62(1), 112–125.
Wang, J., et al. (2012). Signal vector based detection scheme for spatial modulation. IEEE Commun. Lett., 16(1), 19–21.
Arain, Q. A., Uqaili, M. A., Deng, Z., et al. (2017). Clustering Based Energy Efficient and Communication Protocol for Multiple Mix-Zones Over Road Networks. Wireless Personal Communications, 95(2), 411–428.
Memon, I. (2018). Distance and clustering-based energy-efficient pseudonyms changing strategy over road network. International Journal of Communication Systems, 31(11), e3704.
You, L., et al. (2017). Blind detection for spatial modulation systems based on clustering. IEEE Communications Letters, 21(11), 2392–2395.
Jain, A. K., et al. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
Bertsekas, D. P. (1991). Linear Network Optimization. Cambridge, MA: MIT Press.
Bradley, P. S., Bennett, A., & Demiritz, A. (2000). Constrained K-means Clustering. : Microsoft Research. MSR-TR-2000-65.
Moore, E. F. (1959). The shortest path through a maze. In: Proceedings of the international symposium on the theory of switching (pp. 285–292).
Acknowledgements
This research was supported by the MSIT(Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2014-1-00729) supervised by the IITP (Institute of Information & communications Technology Planning & Evaluation)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, L., Jin, M. & Yoo, SJ. A clustering detector with graph theory for blind detection of spatial modulation systems. Wireless Netw 27, 1193–1201 (2021). https://doi.org/10.1007/s11276-020-02508-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-020-02508-8