2019
[1]
W. Boukley Hassan, A. Doufexi, G. Oikonomou, M. Beach, "EVM Prediction for Massive MIMO", in Proc. IEEE PIMRC, 2019 (accepted, to appear)
@inproceedings{Boukley-2019-PIMRC, title = {EVM Prediction for Massive MIMO}, author = {Boukley Hassan, Wael and Angela Doufexi and George Oikonomou and Mark Beach}, year = {2019}, month = sep, booktitle = {Proc. IEEE PIMRC}, note = {accepted, to appear}, abstract = {Signal to interference plus noise ratio (SINR) is a widely common performance metric used in the majority of massive multiple-input, multiple-output (Ma-MIMO) research. This metric requires prior knowledge of the user channel vectors and the interference caused by inaccurate channel state information (CSI). However, the interference caused by inaccurate CSI can't be calculated for realworld scenarios. On the other hand, a comprehensive performance indicator can be achieved by the Error Vector Magnitude (EVM) metric in real-world scenarios. This considers all impairments upon the transmitted symbol as seen at the receiver. However, measuring the EVM values for a subset of users requires each user to retransmit data symbols. This paper presents an estimation method with high accuracy by associating EVM to SINR values for Ma-MIMO with zero-forcing (ZF) and Minimum Mean Square Error (MMSE). Also introduced is a novel EVM prediction method for subset of users taken from the original set of simultaneous users in a single cell Ma-MIMO. This method jointly relies on the channel correlation between users and the EVM performance to predict the EVM values for a subset of the available users without the need to retransmit data symbols. This method considers the user channel vector and the interference caused by inaccurate CSI, which make it suitable for Ma-MIMO algorithms, such as user grouping and power control. Real-world experimental data-sets with real-time results are carried out to validate the EVM prediction method using software-defined radio Ma-MIMO testbed.}, }
Signal to interference plus noise ratio (SINR) is a widely common performance metric used in the majority of massive multiple-input, multiple-output (Ma-MIMO) research. This metric requires prior knowledge of the user channel vectors and the interference caused by inaccurate channel state information (CSI). However, the interference caused by inaccurate CSI can't be calculated for realworld scenarios. On the other hand, a comprehensive performance indicator can be achieved by the Error Vector Magnitude (EVM) metric in real-world scenarios. This considers all impairments upon the transmitted symbol as seen at the receiver. However, measuring the EVM values for a subset of users requires each user to retransmit data symbols. This paper presents an estimation method with high accuracy by associating EVM to SINR values for Ma-MIMO with zero-forcing (ZF) and Minimum Mean Square Error (MMSE). Also introduced is a novel EVM prediction method for subset of users taken from the original set of simultaneous users in a single cell Ma-MIMO. This method jointly relies on the channel correlation between users and the EVM performance to predict the EVM values for a subset of the available users without the need to retransmit data symbols. This method considers the user channel vector and the interference caused by inaccurate CSI, which make it suitable for Ma-MIMO algorithms, such as user grouping and power control. Real-world experimental data-sets with real-time results are carried out to validate the EVM prediction method using software-defined radio Ma-MIMO testbed.
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