Low Complexity Likelihood-based Search Tree Detection Algorithm

LI Xiao-wen; FAN Yi-fang; HOU Ning-ning

doi:10.13756/j.gtxyj.2020.03.014

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Low Complexity Likelihood-based Search Tree Detection Algorithm

- Low Complexity Likelihood-based Search Tree Detection Algorithm
- Study on Optical communication Issue 3, Pages: 69-72(2020)
- 作者机构：
  
  重庆邮电大学通信与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2020.03.014
  CLC： TN929.5
- Published：2020，
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[1]李小文,范艺芳,侯宁宁.低复杂度的似然搜索树检测算法[J].光通信研究,2020(03):69-72.

LI Xiao-wen, FAN Yi-fang, HOU Ning-ning. Low Complexity Likelihood-based Search Tree Detection Algorithm. [J]. (3):69-72(2020)
[1]李小文,范艺芳,侯宁宁.低复杂度的似然搜索树检测算法[J].光通信研究,2020(03):69-72. DOI： 10.13756/j.gtxyj.2020.03.014.

LI Xiao-wen, FAN Yi-fang, HOU Ning-ning. Low Complexity Likelihood-based Search Tree Detection Algorithm. [J]. (3):69-72(2020) DOI： 10.13756/j.gtxyj.2020.03.014.

摘要

大规模多输入多输出（MIMO）系统中

随着天线数目的增加

传统的信号检测算法的检测性能大幅度下降

复杂度呈指数增长。为了解决天线过多带来的复杂度问题

文章基于邻近搜索思想提出一种似然判定准则

作为搜索树分支策略。首先

构造二次规划模型

将其作为搜索树根节点

然后

寻找目标函数值最小的节点作为分支节点

最后将判定准则运用于分支节点降低分支复杂度。在调制阶数为4的正交幅度调制

误比特率达到10

-4

时

文章提出的似然搜索树检测算法与传统搜索树算法相比

性能增益提升了约1.5 dB

复杂度降低了69.84%。仿真结果表明

在大规模MIMO场景下

文章提出的算法表现出了良好的性能及较低的复杂度。

Abstract

In massive Multiple-Input-Multiple-Output（MIMO） system

traditional detection algorithms suffer from more performance loss and higher complexity with increase in the number of antennas. In order to solve the complexity problem

a likelihood criterion based on neighborhood search as a branch strategy is proposed. Firstly

the quadratic programing model is constructed

and the model is used to search tree as the root. Then

the node with the smallest value of the objective function is found as the branching node. Finally

the likelihood criterion is applied to the branching nodes for reducing the complexity of the branch. Specially

when the bit error rate achieves 10

-4

the performance gain of the proposed algorithm is increased by 1.5 dB and the complexity of the proposed algorithm is reduced by about 69.84 percentage points compared with the traditional search tree algorithm in 16 quadrature amplitude modulation. The simulation results show that the proposed algorithm has better error performance and lower complexity.

关键词

大规模多输入多输出二次规划分支界定似然准则

Keywords

massive MIMOquadratic programingbranch and boundlikelihood criterion

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