Learning to Beamform in Heterogeneous Massive MIMO Networks
- URL: http://arxiv.org/abs/2011.03971v1
- Date: Sun, 8 Nov 2020 12:48:06 GMT
- Title: Learning to Beamform in Heterogeneous Massive MIMO Networks
- Authors: Minghe Zhu, Tsung-Hui Chang and Mingyi Hong
- Abstract summary: It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks.
We propose a novel deep learning based paper algorithm to address this problem.
- Score: 48.62625893368218
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is well-known that the problem of 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. While computationally efficient deep learning
based methods have been proposed, their complexity heavily relies upon system
parameters such as the number of transmit antennas, and therefore these methods
typically do not generalize well when deployed in heterogeneous scenarios where
the base stations (BSs) are equipped with different numbers of transmit
antennas and have different inter-BS distances. This paper proposes a novel
deep learning based beamforming algorithm to address the above challenges.
Specifically, we consider the weighted sum rate (WSR) maximization problem in
multi-input and single-output (MISO) interference channels, and propose a deep
neural network architecture by unfolding a parallel gradient projection
algorithm. Somewhat surprisingly, by leveraging the low-dimensional structures
of the optimal beamforming solution, our constructed neural 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. 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.
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