A New Low-Complexity Near-ML Detection Algorithm for Spatial Modulation
A New Low-Complexity Near-ML Detection Algorithm for Spatial Modulation
复制标题
一种新的低复杂度近机器学习空间调制检测算法
DOI：
10.1109/wcl.2012.120312.120601
复制
发表时间：
2013-02-01
影响因子：
6.3
通讯作者：
Li, Shaoqian
中科院分区：
文献类型：
--
作者：
Tang, Qian;Xiao, Yue;Li, Shaoqian
在这封信中，我们提出了一个基于距离的有序检测（DBD）算法的空间调制（SM），以降低接收机的复杂度，并实现了近最大似然（ML）的性能。该算法首先对信道衰减进行补偿，获得具有发射天线索引的估计符号，然后根据这些符号与其解调星座之间的距离对索引进行排序。通过性能和复杂度之间的权衡，提出了一种搜索方法来获得最终的决策。理论分析证明了DBD算法与ML算法的等价性。此外，一个新的低复杂度的软输出DBD（SODBD）算法被开发的编码SM系统。仿真结果表明，DBD算法在性能上接近ML算法，同时有效地降低了传统近似ML算法的复杂度。
In this letter, we propose a distance-based ordered detection (DBD) algorithm for spatial modulation (SM) to reduce the receiver complexity and achieve a near maximum likelihood (ML) performance. The proposed algorithm firstly compensates the channel attenuation and obtains the estimated symbols with transmit antenna indices, then orders the indices based on the distances between these symbols and their demodulation constellations. A searching method is developed to obtain the final decision through a trade-off between performance and complexity. The equivalence between DBD and ML algorithms is also proved by theoretical analysis. Furthermore, a new low-complexity soft output DBD (SODBD) algorithm is developed for coded SM systems. The simulation results show that DBD algorithm has a close performance to ML algorithm while effectively reducing the complexity compared to conventional near-ML algorithms.