.png){kind=icon}
返回
A Constrained Clustering-Based Blind Detector for Spatial Modulation
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2019.2915304.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Blind detection
greedy algorithm
K-means clustering (KMC)
spatial modulation (SM)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
分享收藏Reduced-Complexity ML Detection and Capacity-Optimized Training for Spatial Modulation Systems空间调制系统的低复杂度ML检测和容量优化训练
OAAI
OAAI分享收藏分享收藏Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏 没有更多内容
没有更多内容