Asymptotic Performance Analysis of the Regularized Least Squares Precoding with Restricted Transmit Power in Multi-User Massive MIMO

This paper characterizes the regularized least squares (RLS) precoding scheme in multi-user massive multiple-input multiple-output (MU-mMIMO) communication systems. To allow for the use of cheap power amplifiers (PAs) with very limited dynamic ranges, the studied precoder is formulated as a non-clos...

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Veröffentlicht in:2023 31st European Signal Processing Conference (EUSIPCO) S. 1450 - 1454
Hauptverfasser: Ma, Xiuxiu, Kammoun, Abla, Alrashdi, Ayed M., Ballal, Tarig, Alouini, Mohamed-Slim, Al-Naffouri, Tareq Y.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: EURASIP 04.09.2023
Schlagworte:
asymptotic analysis
Bit error rate
Communication systems
convex Gaussian min-max theorem (CGMT)
convex optimization
Non-linear precoder
Nonlinear distortion
Power control
Precoding
regularized least squares
Symbols
Transmitting antennas
ISSN:2076-1465
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Zusammenfassung:This paper characterizes the regularized least squares (RLS) precoding scheme in multi-user massive multiple-input multiple-output (MU-mMIMO) communication systems. To allow for the use of cheap power amplifiers (PAs) with very limited dynamic ranges, the studied precoder is formulated as a non-closed form solution of a convex problem in which the power at each antenna is constrained below a predefined maximum power. By leveraging the convex Gaussian min-max theorem (CGMT), we characterize the statistics of the precoded symbols and the distortion error at each user under the assumption of Gaussian channels. Based on this, the bit error rate (BER) and a tight lower bound of the signal-to-noise and distortion ratio (SINAD lb ) are asymptotically approximated. As a major outcome of our analysis, we establish that there is an average transmit power that asymptotically optimizes the SINAD lb and the BER performance. Such a value can be achieved by properly tuning the power control parameter. Numerical simulations are provided to support the accuracy of our theoretical predictions.
ISSN:2076-1465
DOI:10.23919/EUSIPCO58844.2023.10290123