TY - JOUR
T1 - Location-Based Visible Region Recognition in Extra-Large Massive MIMO Systems
AU - Liu, Daohua
AU - Wang, Jue
AU - Li, Ye
AU - Han, Yu
AU - Ding, Rui
AU - Zhang, Jun
AU - Jin, Shi
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Extra-large massive multiple-input multiple-output (XL-MIMO) is envisioned to be a promising technology for 6G wireless, where the number of antennas is greatly increased to a new extent, resulting in extra large aperture arrays. In this case, different users may see different array parts (termed as visible region, VR) due to the spatial non-stationarity. Exploiting the VR information can facilitate low-complexity transmission design, but how to acquire this information for a large amount of users is still challenging. To this end, we first establish a VR model for XL-MIMO systems and show that a user's VR is location-dependent. Assuming that the VRs of some beacon users can be known a priori, we propose three location-based VR recognition schemes, including Voronoi cell partition, weighted Voronoi cell partition, and a neural network (NN) approach termed VR-Net, which takes the location of a user as input and returns its VR index as output. Simulation results show that all schemes can achieve high VR recognition accuracy if the number of beacon users is sufficiently large. Notably, in the more practical scenario that the available location-VR dataset is limited, the proposed VR-Net is able to achieve much better recognition performance.
AB - Extra-large massive multiple-input multiple-output (XL-MIMO) is envisioned to be a promising technology for 6G wireless, where the number of antennas is greatly increased to a new extent, resulting in extra large aperture arrays. In this case, different users may see different array parts (termed as visible region, VR) due to the spatial non-stationarity. Exploiting the VR information can facilitate low-complexity transmission design, but how to acquire this information for a large amount of users is still challenging. To this end, we first establish a VR model for XL-MIMO systems and show that a user's VR is location-dependent. Assuming that the VRs of some beacon users can be known a priori, we propose three location-based VR recognition schemes, including Voronoi cell partition, weighted Voronoi cell partition, and a neural network (NN) approach termed VR-Net, which takes the location of a user as input and returns its VR index as output. Simulation results show that all schemes can achieve high VR recognition accuracy if the number of beacon users is sufficiently large. Notably, in the more practical scenario that the available location-VR dataset is limited, the proposed VR-Net is able to achieve much better recognition performance.
UR - http://www.scopus.com/inward/record.url?scp=85148464725&partnerID=8YFLogxK
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U2 - 10.1109/TVT.2023.3242615
DO - 10.1109/TVT.2023.3242615
M3 - Article
AN - SCOPUS:85148464725
SN - 0018-9545
VL - 72
SP - 8186
EP - 8191
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 6
ER -