TY - JOUR
T1 - Learning to Beamform in Heterogeneous Massive MIMO Networks
AU - Zhu, Minghe
AU - Chang, Tsung Hui
AU - Hong, Mingyi
N1 - Publisher Copyright:
IEEE
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs. Recently, deep learning based methods have been proposed because of their computational efficiency, but they typically can not generalize well when deployed in heterogeneous scenarios where the base stations (BSs) are equipped with different numbers of antennas and have different inter-BS distances. This paper proposes a novel deep learning based beamforming algorithm to address above challenges. Specifically, we consider the weighted sum rate (WSR) maximization problem in multi-input and single-output (MISO) interference channels, and propose a beamforming learning architecture by unfolding a parallel gradient projection algorithm. By leveraging the low-dimensional structures of the optimal beamforming solution, our constructed learning network can be made independent of the numbers of transmit antennas and BSs. Moreover, such a design can be further extended to a cooperative multicell network where users are jointly served by multiple BSs. Numerical results based on both synthetic and ray-tracing channel models show that the proposed neural network can achieve high WSRs with significantly reduced runtime, while exhibiting favorable generalization capability with respect to the antenna number, BS number and the inter-BS distance.
AB - Finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs. Recently, deep learning based methods have been proposed because of their computational efficiency, but they typically can not generalize well when deployed in heterogeneous scenarios where the base stations (BSs) are equipped with different numbers of antennas and have different inter-BS distances. This paper proposes a novel deep learning based beamforming algorithm to address above challenges. Specifically, we consider the weighted sum rate (WSR) maximization problem in multi-input and single-output (MISO) interference channels, and propose a beamforming learning architecture by unfolding a parallel gradient projection algorithm. By leveraging the low-dimensional structures of the optimal beamforming solution, our constructed learning network can be made independent of the numbers of transmit antennas and BSs. Moreover, such a design can be further extended to a cooperative multicell network where users are jointly served by multiple BSs. Numerical results based on both synthetic and ray-tracing channel models show that the proposed neural network can achieve high WSRs with significantly reduced runtime, while exhibiting favorable generalization capability with respect to the antenna number, BS number and the inter-BS distance.
KW - Beamforming
KW - MISO interfering channel
KW - cooperative multicell beamforming
KW - deep neural network
UR - http://www.scopus.com/inward/record.url?scp=85146250768&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146250768&partnerID=8YFLogxK
U2 - 10.1109/TWC.2022.3230662
DO - 10.1109/TWC.2022.3230662
M3 - Article
AN - SCOPUS:85146250768
SN - 1536-1276
VL - 22
SP - 4901
EP - 4915
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 7
ER -