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Dual-Polarized IRS-Assisted MIMO Network
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This study considers a dual-polarized intelligent reflecting surface
(DP-IRS)-assisted multiple-input multiple-output (MIMO) single-user
wireless communication system. The transmitter and receiver are equipped
with DP antennas, and each antenna features a separate phase shifter for
each polarization. We attempt to maximize the system’s spectral
efficiency (SE) by optimizing the operations of the reflecting elements
at the DP-IRS, precoder/combiner at the transmitter/receiver, and
vertical/horizontal phase shifters at the DP antennas. To address this
problem, we propose a three-step alternating optimization (AO) algorithm
based on the semi-definite relaxation method. Next, we consider
asymptotically low/high signal-to-noise ratio (SNR) regimes and propose
low-complexity algorithms. In particular, for the lowSNR regime, we
derive computationally low-cost closed-form solutions. According to the
obtained numerical results, the proposed algorithm outperforms the
various benchmark schemes. Specifically, our main algorithm exhibits a
65.6 % increase in the SE performance compared to random operations. In
addition, we compare the SE performance of DP-IRS with that of simple
IRS (S-IRS). For N = 50, DP-IRS achieves 24.8 %, 28.2 %, and 30.3 %
improvements in SE for 4×4, 8×8, and 16×16 MIMO, respectively, compared
to S-IRS.
Title: Dual-Polarized IRS-Assisted MIMO Network
Description:
This study considers a dual-polarized intelligent reflecting surface
(DP-IRS)-assisted multiple-input multiple-output (MIMO) single-user
wireless communication system.
The transmitter and receiver are equipped
with DP antennas, and each antenna features a separate phase shifter for
each polarization.
We attempt to maximize the system’s spectral
efficiency (SE) by optimizing the operations of the reflecting elements
at the DP-IRS, precoder/combiner at the transmitter/receiver, and
vertical/horizontal phase shifters at the DP antennas.
To address this
problem, we propose a three-step alternating optimization (AO) algorithm
based on the semi-definite relaxation method.
Next, we consider
asymptotically low/high signal-to-noise ratio (SNR) regimes and propose
low-complexity algorithms.
In particular, for the lowSNR regime, we
derive computationally low-cost closed-form solutions.
According to the
obtained numerical results, the proposed algorithm outperforms the
various benchmark schemes.
Specifically, our main algorithm exhibits a
65.
6 % increase in the SE performance compared to random operations.
In
addition, we compare the SE performance of DP-IRS with that of simple
IRS (S-IRS).
For N = 50, DP-IRS achieves 24.
8 %, 28.
2 %, and 30.
3 %
improvements in SE for 4×4, 8×8, and 16×16 MIMO, respectively, compared
to S-IRS.
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