Cramér-Rao bounds and a ConvNeXt-based scheme are proposed for near-field user localization in wideband XL-MIMO, reporting simulated distance RMSE near 5 cm.
Uplink Performance of Cell-Free Extremely Large-Scale MIMO Systems
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abstract
In this paper, we investigate the uplink performance of cell-free (CF) extremely large-scale multiple-input-multipleoutput (XL-MIMO) systems, which is a promising technique for future wireless communications. More specifically, we consider the practical scenario with multiple base stations (BSs) and multiple user equipments (UEs). To this end, we derive exact achievable spectral efficiency (SE) expressions for any combining scheme. It is worth noting that we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio (MR) combining. Numerical results show that the SE performance of the CF XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is interesting that a smaller antenna spacing leads to a higher correlation level among patch antennas. Finally, we prove that increasing the number of UE antennas may decrease the SE performance with MR combining.
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Deep Learning Based Near-Field User Localization with Beam Squint in Wideband XL-MIMO Systems
Cramér-Rao bounds and a ConvNeXt-based scheme are proposed for near-field user localization in wideband XL-MIMO, reporting simulated distance RMSE near 5 cm.