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arxiv: 1704.03219 · v1 · pith:GXRFBJ54new · submitted 2017-04-11 · 💻 cs.IT · math.IT

Error Vector Magnitude Analysis in Generalized Fading with Co-Channel Interference

classification 💻 cs.IT math.IT
keywords fadinginterferersdesiredexperienceinterferencesignalwhenderived
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In this paper, we derive the data-aided Error Vector Magnitude (EVM) in an interference limited system when both the desired signal and interferers experience independent and non identically distributed $\kappa$-$\mu$ shadowed fading. Then it is analytically shown that the EVM is equal to the square root of number of interferers when the desired signal and interferers do not experience fading. Further, EVM is derived in the presence of interference and noise, when the desired signal experiences $\kappa$-$\mu$ shadowed fading and the interferers experience independent and identical Nakagami fading. Moreover, using the properties of the special functions, the derived EVM expressions are also simplified for various special cases.

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