# Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold

@article{Xue2021BlindDD,
title={Blind Data Detection in Massive MIMO via ℓ₃-Norm Maximization Over the Stiefel Manifold},
author={Ye Xue and Yifei Shen and Vincent K. N. Lau and Jun Zhang and Khaled Ben Letaief},
journal={IEEE Transactions on Wireless Communications},
year={2021},
volume={20},
pages={1411-1424}
}
• Published 26 April 2020
• Computer Science
• IEEE Transactions on Wireless Communications
Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel <inline-formula> <tex-math notation="LaTeX">$\ell _{3… 7 Citations ## Figures and Tables from this paper Complete Dictionary Learning via 𝓁4-Norm Maximization over the Orthogonal Group • Computer Science J. Mach. Learn. Res. • 2020 This work proposes a new formulation that maximizes the$\ell^4$-norm over the orthogonal group, to learn the entire dictionary, and gives a conceptually simple and yet effective algorithm based on matching, stretching, and projection' (MSP). AI Empowered Resource Management for Future Wireless Networks • Computer Science 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom) • 2021 For K-user interference management problem, it is theoretically show that graph neural networks (GNNs) are superior to multi-layer perceptrons (MLPs), and the performance gap between these two methods grows with$\sqrt K \$.
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