D2D通信中一种基于非数据辅助误差适量幅度的同信道干扰控制方法及其性能分析

曾孝平; 李诗琪; 杨凡; 简鑫; 吴继森

doi:10.11999/JEIT200473

D2D通信中一种基于非数据辅助误差适量幅度的同信道干扰控制方法及其性能分析

doi: 10.11999/JEIT200473

1.
重庆大学通信工程学院重庆 400044
2.
重庆理工大学电气与电子工程学院重庆 400054
3.
重庆通用航空集团有限公司重庆 401135

基金项目: 国家自然科学基金(61501065, 61571069, 61601067, 61701054)；重庆市基础科学与前沿技术研究专项一般基金资助项目(cstc2016jcyjA0021)；中央高校基本科研业务费(106112016CDJXY160001)；重庆市重大主题专项(cstc2019jscx-zdztzxX0045, cstc2019jscx-zdztzxX0029, cstc2019jscx-zdztzxX0051)

详细信息

作者简介:
曾孝平：男，1965年生，教授，研究方向为下一代移动通信、无线通信、空间信息网

李诗琪：女，1996年生，硕士生，研究方向为下一代移动通信、干扰检测

杨凡：男，1983年生，教授，研究方向为无线宽带自适应传输、无线通信网络

简鑫：男，1987年生，副教授，研究方向为下一代移动通信、多入多出网络

吴继森：男，1966年生，高级工程师，研究方向为飞行器设计、航空通信导航设计

通讯作者:
曾孝平　zxp@cqu.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2020-06-11
- 修回日期: 2020-12-31
- 网络出版日期: 2021-02-26
- 刊出日期: 2021-09-16

NDA-EVM Based Co-channel Interference Control Method and Performance Analysis in D2D Communication

1.
College of Communication Engineering, Chongqing University, Chongqing 400044, China
2.
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
3.
Chongqing General Aviation Co., Ltd (CQGA), Chongqing 401135, China

Funds: The National Natural Science Foundation of China (61501065, 61571069, 61601067, 61701054), The Chongqing Research Program of Basic Research and Frontier Technology (cstc2016jcyjA0021), The Fundamental Research Funds for the Central Universities (106112016CDJXY160001), The Chongqing Technology Innovation and Application Development Key Project (cstc2019jscx-zdztzxX0045, cstc2019jscx-zdztzxX0029, cstc2019jscx-zdztzxX0051)

摘要

摘要: 针对D2D通信系统中广泛存在的同信道干扰问题，该文提出一种基于非数据辅助误差矢量幅度(NDA-EVM)进行同信道干扰分析的方法。以NDA-EVM作为信道质量评估参量，推导信号在M-QAM调制下的NDA-EVM统一计算模型，利用信道增益建立NDA-EVM同信道干扰分布模型，并进一步求解该模型的性能上限，从而量化同信道干扰。理论分析和仿真实验表明，相对于传统算法，该文所提上限的计算时间复杂度由$O({M^2})$降为$O({M})$，提高了信道评估时效性；推导性能上限与理论值吻合度高，特别是在低SNR时为紧上界，两者最小均方根误差RMSE低至0.2615。
- 同信道干扰 /
- D2D通信 /
- 非数据辅助 /
- 误差矢量幅度
Abstract: In view of the widely exist co-channel interference in D2D communication system, a novel method to quantify the co-channel interference based on NonDate Aided Error Vector Magnitude (NDA-EVM) is proposed. NDA-EVM is considered as a new metric to evaluate the change of the channels. The NDA-EVM of M-QAM modulated signal is analytically derived. Moreover, the model of NDA-EVM under co-channel interference is established. Specifically, the upper bound of NDA-EVM is calculated when co-channel interferences exist, so co-channel interferences can be quantified. Theoretical analysis and simulation experiments indicate that, when compared with traditional NDA-EVM algorithm, the proposed upper bound of NDA-EVM reduces the time complexity of algorithm from O(M²) to O(M), the effectiveness of channel estimation is improved. Besides, the derived upper bound closely matches with the theoretical value, especially at low SNR, the RMSE is as low as 0.2615.
- Co-channel interference /
- D2D communication /
- NonDate-Aided (NDA) /
- Error Vector Magnitude (EVM)

HTML全文

图 1 D2D通信系统模型

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图 2 $\alpha {\rm{ = 1}}$时NDA-EVM理论值与上限、下限值对比(16QAM)

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图 3 L=2时的NDA-EVM上限(4QAM)

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图 4 L=2时的NDA-EVM上限(256QAM)

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图 5 L=2时不同${m_{\rm{I}}}$的NDA-EVM上限

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图 6 L=2时NDA-EVM与DA-EVM对比

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图 7 不同干扰信道数量时的NDA-EVM上限

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表 1 算法时间复杂度

计算式	循环次数	时间复杂度
$\begin{aligned} \frac{2}{{k + 1}}&\bigg\{ \sum\limits_{j = 0}^k {\bigg[\sigma ( - b + {\lambda _{jk,{\rm{R}}}})\varphi \bigg(\frac{{ - b - {\lambda _{jk,{\rm{R}}}}}}{\sigma }\bigg) + ({\lambda _{jk,R}}^2 + {\sigma ^2})Q\bigg(\frac{{ - b - {\lambda _{jk,{\rm{R}}}}}}{\sigma }\bigg)} \bigg] \\ & + \sum\limits_{j = 0}^k {\bigg[\sigma (b + {\lambda _{jk,{\rm{R}}}})\varphi \bigg(\frac{{b - {\lambda _{jk,{\rm{R}}}}}}{\sigma }\bigg) + \bigg({\lambda _{jk,{\rm{R}}}}^2 + {\sigma ^2}\bigg)Q\bigg(\frac{{b - {\lambda _{jk,{\rm{R}}}}}}{\sigma }\bigg)} \bigg] \\ & + \sum\limits_{i = 1}^{k - 1} {\sum\limits_{j = 0}^k {\bigg[\sigma ( - b + {\lambda _{ji,{\rm{R}}}})\varphi \bigg(\frac{{ - b - {\lambda _{ji,{\rm{R}}}}}}{\sigma }\bigg) + ({\lambda _{ji,{\rm{R}}}}^2 + {\sigma ^2})Q\bigg(\frac{{ - b - {\lambda _{ji,{\rm{R}}}}}}{\sigma }\bigg)} \bigg]} {\rm{ }} \\ & - \sum\limits_{i = 1}^{k - 1} \sum\limits_{j = 0}^k \bigg[\sigma (b + {\lambda _{ji,{\rm{R}}}})\varphi \bigg(\frac{{b - {\lambda _{ji,{\rm{R}}}}}}{\sigma }\bigg) + ({\lambda _{ji,{\rm{R}}}}^2 + {\sigma ^2})Q\bigg(\frac{{b - {\lambda _{ji,{\rm{R}}}}}}{\sigma }\bigg) \bigg] \bigg\} \end{aligned} $	$2{M_m} + ({M_m} - 2) \times {M_m}$	$O(M_m^2)$
$\dfrac{{\rm{4}}}{{k + 1}}\left\{ C + 4kb\sigma \displaystyle\sum\limits_{j = 0}^{\dfrac{{k - 1}}{2}} {\left[\alpha {\beta _j} - \sqrt {\dfrac{1}{{2\pi }}} \exp \left(\sqrt {\dfrac{\pi }{2}} \alpha {\beta _j} - \dfrac{{{\alpha ^2}{\beta _j}^2}}{2}\right) \right]}\right\} $	$\dfrac{{{M_m}}}{2}$	$O({M_m})$

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表 2 仿真参数

仿真参数	数值
载频${f_c}$(GHz)	2.2
发送功率${P_{\rm{0}}}$(W)	1
符号周期${T_{{\rm{sym}}}}$(μs)	66.7
循环前缀(CP)类型	${\rm{Normal}}$
${\rm{MQAM}}$星座类型	${\rm{QAM/16QAM/64QAM/256QAM}}$

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