Revisiting Near-Far Field Boundary in Dual-Polarized XL-MIMO Systems

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm's effectiveness.

Figures (7)

• Figure 1: System model of a downlink dual-polarized XL-MIMO network.
• Figure 2: Comparison of non-uniform XPD distances $r_U^{th}$ with direction-dependent Rayleigh distances Lu_XL_MIMO_ICC. Further, non-uniform power distances are also considered, characterizing the maximum BS-user distance within which the difference in pathloss (and thus received power) across antenna elements cannot be neglected Lu_XL_MIMO_ICC. Here, the decay exponent of XPD with distance is set to $\eta=1$, and a linear antenna array with $70$ antennas is considered.
• Figure 3: Non-uniform XPD aperture $A^{th}$ defined in (\ref{['def_non_unifrom_XPD_aperture']}) vs. the distance $r_U$ between the UE and the center of the BS antenna array, with elevation and azimuth angles of the user $(\theta_U,\varphi_U)=(\frac{\pi}{6},\frac{\pi}{2})$. The BS antenna array is square, i.e., $k=1$. The thresholds for XPD components $\chi_1$ and $\chi_2$ are given by $\gamma_1^{th}=\gamma_2^{th}=1.1$.
• Figure 4: Ergodic capacity vs. overall transmit power $P$ of the BS, with the coordinate of the UE given by $(30,0,0)$ m, number of BS antennas $M=80$. A linear antenna array is deployed at the BS. Here, the "Conventional scheme" refers to the one designed for conventional dual-polarized massive MIMO, which does not consider XPD and pathloss differences across array elements.
• Figure 5: Improvement in ergodic capacity achieved by utilizing the non-uniform XPD model compared to the conventional model. Here, the decay exponent of XPD with distance is set to $\eta=1$. The thresholds for XPD components $\chi_1$ and $\chi_2$ are given by $\gamma_1^{th}=\gamma_2^{th}=1.05$. A linear antenna array is considered, and the user is set to be aligned with the antenna array.

Theorems & Definitions (4)

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