Power Normalization Perspective for massive MIMO Network using MMSE Precoding Techniques

Eze Gerald Chukwudi, Mamilus A. Ahaneku and Vincent C. Chijindu
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Eze Gerald Chukwudi: Department of Electronic/Electrical Engineering, Federal Polytechnic, Oko, Aguata, Anambra State, Nigeria.
Mamilus A. Ahaneku: Department of Electronic/Electrical Engineering, Federal Polytechnic, Oko, Aguata, Anambra State, Nigeria.
Vincent C. Chijindu: Department of Electronic Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria.

International Journal of Latest Technology in Engineering, Management & Applied Science, 2024, vol. 13, issue 5, 172-185

Abstract: This paper seeks ways to improve spectral efficiency (or throughput) while mitigating multi-user interferences for large-scale antenna arrays, massive multiple input multiple output (mMIMO) systems via the use of the minimum mean squared error (MMSE) precoding schemes. The impact of the power at the user equipment (UEs) being adjusted to meet the transmission power constraint of the BS otherwise known as power normalization on the performance of the single and multi-cell MMSE precoders (S-MMSE and M-MMSE) was studied. The choice of power normalization (matrix normalization or vector normalization) and how they can impact worse or better performances on S-MMSE and M-MMSE under three different channel estimates with respect to varying pilot reuse factors were simulated and analyzed. We considered a downlink mMIMO network model that accounts for the number of antennas and single-antenna UEs. Numerical results obtained after simulations depict that M-MMSE with vector normalization (VN) out-performs S-MMSE with vector/matrix normalization and M-MMSE with matrix normalization (MN) by having the highest average sum SE, throughput, and signal-to-interference plus noise ratio (SINR/SNR) for any number of antennas and UEs in the three-channel estimators. LS channel estimator performs the least when compared to EW-MMSE and MMSE channel estimators.

Date: 2024
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