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

Abstract

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

CLC Number:

TN929.5

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.

Figures/Tables 11

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	$β = \frac{1}{μ (1 + κ)}, δ = \frac{μ κ + m}{μ (1 + κ) m}, m = \frac{μ (1 + κ)^{2}}{(1 + 2 κ)^{2}}, ψ (x) = \frac{d}{d x} \ln Γ (x) = \frac{Γ^{'} (x)}{Γ (x)} (D i g a m m a 函数)$

References 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%

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

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