基于迟滞噪声混沌神经网络的导频分配

邵凯; 李述栋; 王光宇; 付天飞

doi:10.11999/JEIT190748

基于迟滞噪声混沌神经网络的导频分配

doi: 10.11999/JEIT190748

邵凯^{1, 2, 3, ,},
李述栋^{1, 2},
王光宇^{1, 2, 3},
付天飞^{1, 2}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
移动通信技术重庆市重点实验室重庆 400065
3.
移动通信教育部工程研究中心重庆 400065

详细信息

作者简介:
邵凯：男，1977年生，副教授，研究方向为新型多载波调制技术、新型多址接入技术

李述栋：男，1994年生，硕士，研究方向为AI在无线通信中的应用

王光宇：男，1964年生，教授，研究方向为新型多载波调制技术、新型多址接入技术

付天飞：男，1992年生，硕士，研究方向为大规模MIMO导频污染抑制技术

通讯作者:
邵凯　shaokai@cqupt.edu.cn

中图分类号: TN911
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出版历程
- 收稿日期: 2019-09-27
- 修回日期: 2020-05-29
- 网络出版日期: 2020-06-24
- 刊出日期: 2020-10-13

Hysteretic Noisy Chaotic Neural Networks Based Pilot Assignment

Kai SHAO^{1, 2, 3
, ,},
Shudong LI^{1, 2},
Guangyu WANG^{1, 2, 3},
Tianfei FU^{1, 2}

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China
3.
Engineering Research Center of Mobile Communications, Ministry of Education, Chongqing 400065, China

摘要

摘要: 在多小区大规模多输入多输出(MIMO)系统中，导频污染已经成为制约整个系统的瓶颈。合理地使用导频资源能减轻导频污染的影响，为了寻找使边缘用户和容量最大的导频分配方式，该文首次提出了基于迟滞噪声混沌神经网络(HNCNN)的导频分配方案。迟滞噪声混沌神经网络作为良好的优化工具，其优化能力与所设计的能量函数相关。该方案结合导频资源使用的特点以及最大化边缘用户和容量的计算方式，设计了新的能量函数。仿真结果表明，网络能在一定迭代次数后收敛到较优的导频分配方式。与其它文献方案相比，采用以HNCNN为框架求取导频分配方式，可以更有效减轻导频污染的影响，使系统性能得到改善。
- 大规模多输入多输出 /
- 导频污染 /
- 导频分配 /
- 迟滞噪声混沌神经网络
Abstract: In multi-cell massive Multiple Input Multiple Output (MIMO) systems, pilot contamination has become the bottleneck which restricts the performance of the whole system, so the reasonable usage of pilot resources can mitigate the pilot contamination of the system. In order to find the pilot allocation method that maximizes the total transmission capacity of edge users, a pilot allocation scheme based on Hysteretic Noise Chaotic Neural Network (HNCNN) is proposed for the first time. HNCNN is a famous optimization tool, and its optimization ability is related to the designed energy function. This scheme combines the characteristics of pilot resource usage and the calculation method of maximizing the total transmission capacity of edge users to design a new energy function. The simulation results show that the proposed network can converge to a better pilot allocation mode after a certain number of iterations. Compared with other literature pilot allocation solution, the pilot allocation method based on HNCNN can further reduce the influence of pilot contamination and improve the system performance.
- Massive Multiple Input Multiple Output(MIMO) /
- Pilot contamination /
- Pilot assignment /
- Hysteretic Noise Chaotic Neural Network (HNCNN)

HTML全文

图 1 导频分配示意图($L$=3，$K$=4)

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图 2 用户分组门限示意图

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图 3 导频分配矩阵示例

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图 4 基于HNCNN的导频分配流程框图

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图 5 多小区多用户场景

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图 6 不同算法的边缘用户上行传输容量对比

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图 7 不同算法的用户上行传输容量对比

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表 1 大规模MIMO参数

参数	取值
小区数$L$	7
每小区天线数	256
小区半径	1.6 km
小区服务数	12
上下行发射功率	15 dBm, 43 dBm
路径衰落指数$\alpha $	3.8
用户分组门限F	0.33
对数阴影衰落	8 dB

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表 2 HNCNN仿真分析

场景	随机平均$\Sigma $	算法	有效次数	平均$\varSigma$	提升比例(%)
1	58.38	HNCNN-C	981/1000	78.73	34.86
1	58.38	HNCNN-A	1000/1000	79.90	36.86
2	65.43	HNCNN-C	982/1000	84.98	29.88
2	65.43	HNCNN-A	990/1000	85.86	31.22
3	78.26	HNCNN-C	964/1000	97.07	24.04
3	78.26	HNCNN-A	982/1000	97.49	24.57
4	54.47	HNCNN-C	982/1000	65.65	20.53
4	54.47	HNCNN-A	1000/1000	67.13	23.24
5	59.04	HNCNN-C	946/1000	82.20	39.23
5	59.04	HNCNN-A	958/1000	83.66	41.70
6	62.81	HNCNN-C	984/1000	73.14	16.45
6	62.81	HNCNN-A	993/1000	74.80	19.09

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参考文献(15)

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