.png){kind=icon}
Return
A Constrained Clustering-Based Blind Detector for Spatial Modulation
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2019.2915304.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter, we propose a novel clustering-based detector for spatial modulation multiple-input multiple-output (MIMO) system. Specifically, we first convert unconstrained optimization problem of conventional K-means algorithm to the constrained optimization by controlling the number of received symbols in each cluster. Then, a low-complexity greedy algorithm is designed for solving the constrained optimization problem to determine the cluster centroids of the proposed detector and a novel detector based on the greedy algorithm is proposed accordingly. Simulation results show that the proposed detector efficiently avoids the occurrence of error floor effects of conventional K-means detector and can achieve near-maximum likelihood (ML) performance even if the number of clusters is large while effectively reducing the complexity compared to existing blind detectors.
Keywords:
Blind detection
greedy algorithm
K-means clustering (KMC)
spatial modulation (SM)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveSpatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAIShareSave no more
no more