TY - JOUR
T1 - Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes
AU - Nam, Junyoung
AU - Caire, Giuseppe
AU - Debbah, Merouane
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received December 20, 2018; revised September 30, 2019; accepted December 2, 2019. Date of publication December 20, 2019; date of current version April 21, 2020. This work was partially supported by the U.S. National Science Foundation under Grant CCF-0939370 and Grant CCF-1513915. This work was presented in part at the IEEE International Conference on Communications (ICC).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the 'random sparse angular support' model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We also note that the achievable rate lower bound based on massive MIMO 'channel hardening', widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.
AB - This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the 'random sparse angular support' model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We also note that the achievable rate lower bound based on massive MIMO 'channel hardening', widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.
KW - Large-scale MIMO
KW - asymptotic capacity scaling
KW - correlated fading channels
KW - multiplexing gain
KW - random matrix theory
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U2 - 10.1109/TIT.2019.2961323
DO - 10.1109/TIT.2019.2961323
M3 - Article
AN - SCOPUS:85084149532
SN - 0018-9448
VL - 66
SP - 3040
EP - 3064
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 5
M1 - 8937832
ER -