A clustering detector with graph theory for blind detection of spatial modulation systems
- PDF / 803,972 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 8 Downloads / 215 Views
(0123456789().,-volV)(0123456789(). ,- volV)
A clustering detector with graph theory for blind detection of spatial modulation systems Lijuan Zhang1 • Minglu Jin1 • Sang-Jo Yoo2 Accepted: 18 November 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Blind detection Spatial modulation (SM) Minimum cost flow (MCF) linear network Graph theory K-means clustering (KMC)
1 Introduction Spatial modulation (SM) has been recently proposed as a novel modulation scheme for multiple-input multiple-output (MIMO) systems to reduce the complexity and cost of conventional MIMO schemes while still ensuring excellent performance [1–3]. As a further advance, SM has become an attractive option to meet the skyrocketing demand on high spectral efficiency and high energy efficiency in both the continuous growth of the internet of things (IoT) and Wireless Sensor Network (WSN) [4]. & Minglu Jin [email protected] Lijuan Zhang [email protected] Sang-Jo Yoo [email protected] 1
School of Information and Communication Engineering, Dalian University of Technology, Dalian City 116024, China
2
School of Information and Communication Engineering, Inha University, 253 Yonghyun-dong, Nam-gu, Incheon 402-751, Korea
However, despite the above advantages, there exist many challenges for the application of SM in future wireless networks, especially for the detection of SM systems. On the one hand, compared with conventional MIMO systems, the above key features make SM detection more complicated, which needs to demodulate transmit antenna indices except for transmitted symbols. On the other hand, most existing detectors have concentrated on investigating the detection performance under the condition of perfect channel state information (CSI), such as maximum likelihood (ML) detector [5], signal vector based detection (SVD) [6] and so on. Therefore, the traditional method brings great challenges for the SM detection especially CSI is unavailable. To perform the blind detection of SM systems more
Data Loading...
