κ−μ阴影衰落信道下非数据辅助的误差矢量幅度性能分析

doi:10.11959/j.issn.1000-436x.2018088

通信学报 ›› 2018, Vol. 39 ›› Issue (5): 177-188.doi: 10.11959/j.issn.1000-436x.2018088

κ−μ阴影衰落信道下非数据辅助的误差矢量幅度性能分析

杨凡^1,²,曾孝平¹,毛海伟¹,简鑫¹,杜得荣¹,谭晓衡¹,高乙文³

¹ 重庆大学通信工程学院，重庆 400044
² 重庆金美通信有限责任公司，重庆 400030
³ 中国移动通信集团重庆有限公司，重庆 400044

修回日期:2018-03-28 出版日期:2018-05-01 发布日期:2018-06-01
作者简介:杨凡（1983-），男，湖北广水人，重庆大学博士生，重庆金美通信有限责任公司高级工程师，主要研究方向为无线宽带自适应传输、无线通信网络、下一代移动通信技术、无线通信中的编码技术等。|曾孝平（1956-），男，四川广安人，博士，重庆大学教授、博士生导师，主要研究方向为航空移动通信、下一代移动通信、生物信号处理等。|毛海伟（1994-），男，湖南邵阳人，重庆大学硕士生，主要研究方向为无线宽带自适应传输。|简鑫（1987-），男，四川自贡人，博士，重庆大学副教授，主要研究方向为统计数学、无线通信理论与技术、流量分析理论等。|杜得荣（1983-），男，山东菏泽人，重庆大学博士生，主要研究方向为无线信道建模。|谭晓衡（1976-），男，重庆人，博士，重庆大学教授、博士生导师，主要研究方向为下一代移动通信、通信信号处理等。|高乙文（1987-），女，重庆人，中国移动通信集团重庆有限公司校园中心经理，主要研究方向为移动通信网络、下一代移动通信系统等。
基金资助:
国家自然科学基金资助项目(91438104);国家自然科学基金资助项目(61501065);国家自然科学基金资助项目(61601067);国家自然科学基金资助项目(61571069);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基金资助项目(106112016CDJXY160001)

Nondata-aided error vector magnitude performance analysis over κ−μ shadowed fading channel

Fan YANG^1,²,Xiaoping ZENG¹,Haiwei MAO¹,Xin JIAN¹,Derong DU¹,Xiaoheng TAN¹,Yiwen GAO³

¹ The College of Communication Engineering,Chongqing University,Chongqing 400044,China
² Chongqing Jinmei Communication Co.,Ltd.,Chongqing 400030,China
³ China Mobile Group Chongqing Co.,Ltd.,Chongqing 400044,China

Revised:2018-03-28 Online:2018-05-01 Published:2018-06-01
Supported by:
The National Natural Science Foundation of China(91438104);The National Natural Science Foundation of China(61501065);The National Natural Science Foundation of China(61601067);The National Natural Science Foundation of China(61571069);The Chongqing Research Program of Basic Research and Fourier Technology(CSTC2016JCYJA0021)

摘要/Abstract

摘要：

κ?μ阴影衰落信道下系统性能预测是无线通信中一个具有挑战性的问题，严重影响着传输机制设计。针对此问题，提出一种采用非数据辅助的误差矢量幅度（NDA-EVM,nondata-aided error vector magnitude）对κ?μ阴影衰落信道进行性能分析的理论方法。选取NDA-EVM作为反映信道变化的评估参量，采用最大似然准则推导阴影衰落信道下不同调制阶数NDA-EVM统一的计算模型，并以衰落因子为中间变量，建立NDA-EVM与κ?μ分布的联系，据此推导NDA-EVM 在κ?μ阴影衰落信道的理论下限，并化简给出几种典型信道的性能界限。对所得结论进行理论分析和数值仿真模拟，结果表明：相较传统的DA-SNR（data-aided signal to noise ratio）和DA-EVM（data- aided error vector magnitude），NDA-EVM对κ?μ阴影衰落信道的评估具有最小均方根误差；推导的下限与理论值有很高的吻合度，特别是在低SNR时为紧下界；下限与κ?μ阴影衰落信道参数均呈负相关特性，对信道变化非常敏感。

关键词: κ?μ阴影衰落信道, 性能下限, 非数据辅助, 误差矢量幅度

Abstract:

The performance prediction of wireless system over κ?μ shadowed fading channels was a challenging problem of wireless communications,which affects transmission scheme design seriously.To solve this problem,a novel method of quantifying the κ?μ shadowed fading channels performance based on nondata-aided error vector magnitude (NDA-EVM) was proposed.NDA-EVM was considered as a new metric to evaluate the change of the channels.The unified model to calculate different modulation order of NDA-EVM was analytically derived by maximum likelihood criterion.Moreover,the relationship between the κ?μ distribution and the NDA-EVM was built by using the attenuation factor of the channel as intermediate variable.Thereafter,the lower bounds of the NDA-EVM over the κ?μ shadowed fading channels were formulated,which was also simplified for various typical channels.The theoretical analysis was taken,moreover,numerical results were also conducted to verify the effectiveness of the derived formulation.It shows that NDA-EVM estimation has the lest root mean square error than data-aided signal to noise ratio (DA-SNR) estimation and error vector magnitude (DA-EVM) estimation over the κ?μ shadowed fading channels.The derived lower bounds closely match the theoretical values,especially at low SNR.In addition,the lower bounds are negatively related to all of the parameters of the κ?μ shadowed fading channels,which make it sensitive to the change of the fading channels.

Key words: κ?μ shadowed fading channels, performance lower bounds, nondata-aided, error vector magnitude

中图分类号:

TN929.5

杨凡,曾孝平,毛海伟,简鑫,杜得荣,谭晓衡,高乙文. κ−μ阴影衰落信道下非数据辅助的误差矢量幅度性能分析[J]. 通信学报, 2018, 39(5): 177-188.

Fan YANG,Xiaoping ZENG,Haiwei MAO,Xin JIAN,Derong DU,Xiaoheng TAN,Yiwen GAO. Nondata-aided error vector magnitude performance analysis over κ−μ shadowed fading channel[J]. Journal on Communications, 2018, 39(5): 177-188.

图/表 11

表1

表2

表3

NDA-EVM下限的参数灵敏度"

参数灵敏度	计算式
调制参数k对NDAEVM的灵敏度S_k	$\begin{array}{l} S_{k} = [- \frac{b^{2} (2 + 3 k)}{2 σ_{n}^{2}} + \frac{b}{σ_{n} + b k} + \frac{- 2 μ}{k}] N D A - E V M_{κ - μ s h} + \\ \frac{τ (k)}{k^{2 p}} \frac{h {(k)}^{μ - p} Γ (μ - p)}{β^{μ - m} δ^{m} Γ (μ)} \frac{\partial}{\partial k} F_{D}^{(2)} [μ - p, μ - m, m, μ; \frac{- h (k)}{β}, \frac{- h (k)}{δ}] \end{array}$
参数μ对NDA-EVM的灵敏度S_μ	$\begin{array}{l} S_{μ} = [(μ - p) h (k)^{- 1} + ψ (μ - p) + (1 - l n β) (1 - \frac{m}{μ}) + \frac{1 - (κ + 1)^{2} \ln δ}{(2 κ + 1)^{2} {(δ)}^{m}} - ψ (μ)] N D A - E V M_{κ - μ s h} + \\ \frac{τ (k)}{k^{2 p}} \frac{h (k)^{μ - p} Γ (μ - p)}{β^{μ - m} δ^{m} Γ (μ)} \frac{\partial}{\partial μ} F_{D}^{(2)} [μ - p, μ - m, m, μ; \frac{- h (k)}{β}, \frac{- h (k)}{δ}] \end{array}$
参数κ对NDA-EVM的灵敏度S_κ	$\begin{array}{l} S_{κ} = [\ln (β) (2 μ m β - \frac{4 m}{1 + 2 κ}) + β μ (μ - m) + \frac{2 m \ln δ}{(1 + κ) (1 + 2 κ)} + \frac{m (1 + 2 κ) (m - μ + 2 κ m - 4 κ μ)}{δ [μ^{2} (1 + κ)^{4}]}] N D A - E V M_{κ - μ s h} + \\ \frac{τ (k)}{k^{2 p}} \frac{h (k)^{μ - p} Γ (μ - p)}{β^{μ - m} δ^{m} Γ (μ)} \frac{\partial}{\partial κ} {F_{D}^{(2)} [μ - p, μ - m, m, μ; \frac{- h (k)}{β}, \frac{- h (k)}{δ}]} \end{array}$
备注	$β = \frac{1}{μ (1 + κ)}, δ = \frac{μ κ + m}{μ (1 + κ) m}, m = \frac{μ (1 + κ)^{2}}{(1 + 2 κ)^{2}}, ψ (x) = \frac{d}{d x} \ln Γ (x) = \frac{Γ^{'} (x)}{Γ (x)} (D i g a m m a 函数)$

表3

图1

图2

图3

图4

图5

图6

图7

参考文献 18

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调制阶数	LTE-A标准下EVM典型值	本文算法NDA-EVM	本文算法NDA-EVM下限值
QAM	72.5%	74.1%	68.4%
16QAM	47.8%	49.5%	44.1%
64QAM	35.9%	38.1%	34.3%

调制阶数	LTE-A标准下EVM典型值	本文算法NDA-EVM	本文算法NDA-EVM下限值
QAM	75.5%	77.9%	70.2%
16QAM	51.1%	54.3%	49.5%
64QAM	36.5%	39.8%	35.1%

κ−μ阴影衰落信道下非数据辅助的误差矢量幅度性能分析

Nondata-aided error vector magnitude performance analysis over κ−μ shadowed fading channel

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