Uplink MIMO Precoding Under Random Phase Imperfections

Due to the fast deployment and commercial use of fifth generation (5G) communication systems, there is an increasing demand for higher uplink rates, and thus the deployment of more transmit antennas at user equipment (UE) becomes even more urgent. Nowadays, to better balance the uplink user experien...

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Vydané v:IEEE Vehicular Technology Conference s. 1 - 5
Hlavní autori: Xie, Hongxiang, Wang, Hao, Kapetanovic, Dzevdan
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2022
Predmet:
block-diagonal precoder
Channel capacity
Correlation
Costs
eigen-precoder
hardware impairment
Radiofrequency integrated circuits
random phase imperfection
Transmitting antennas
Uplink precoding
User experience
Vehicular and wireless technologies
ISSN:2577-2465
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Popis
Shrnutí:Due to the fast deployment and commercial use of fifth generation (5G) communication systems, there is an increasing demand for higher uplink rates, and thus the deployment of more transmit antennas at user equipment (UE) becomes even more urgent. Nowadays, to better balance the uplink user experience and terminal cost, a feasible way to deploy larger number of transmit antennas at UE is to patch together multiple smaller radio frequency integrated circuits (RFICs), e.g., two RFICs of 2 transmit antennas (2T) will be used to implement 4T. However, this setup will induce a random phase difference between the two RFICs that is unknown at the transmitter. In this paper, we investigate the optimal uplink digital precoder design under such random phase imperfections. In terms of maximizing the average channel capacity, we find that the optimal precoder will be the eigen-vectors of an adjusted transmit correlation matrix. Block-diagonal precoders are also shown to be robust to random phase errors at the expense of some loss in degrees of freedom and channel capacity. Numerical simulations are provided to verify the effectiveness of the proposed uplink precoders under random phase impacts.
ISSN:2577-2465
DOI:10.1109/VTC2022-Fall57202.2022.10012955