Learning to Beamform in Joint Multicast and Unicast Transmission With Imperfect CSI

With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to...

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Published in:	IEEE transactions on communications Vol. 71; no. 5; pp. 2711 - 2723
Main Authors:	Zhang, Zhe, Tao, Meixia, Liu, Ya-Feng
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Array signal processing Beamforming Channel estimation Graph neural networks imperfect CSI Interference Joint multicast and unicast transmission Learning Multicast algorithms Multicasting Neural networks Optimization Permutations UMGNN Unicast Video transmission
ISSN:	0090-6778, 1558-0857
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Abstract	With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to be jointly designed, which generally requires perfect channel state information (CSI). However, perfect CSI is usually unavailable due to the channel estimation error. In this paper, we propose a learning based approach to jointly design the multicast and unicast beamformers with imperfect CSI. To learn the beamforming strategy, a new graph neural network (GNN) based architecture named unicast-multicast GNN (UMGNN) is proposed, which only requires the estimated channel. UMGNN can guarantee the permutation invariance/equivalence and model the special property in the multicast transmission, i.e., the multicast rate is determined by the worst user. Moreover, by sharing the parameters across different users, UMGNN exhibits a pretty good scalability to different number of users. Numerical results show that UMGNN outperforms a fully connected neural network and a widely used sampling-based algorithm. To highlight its performance in the multicast transmission, we also show that UMGNN can find the correct worst user that determines the multicast rate.
AbstractList	With the rapid development of mobile Internet, the demand for multicast is growing rapidly, such as content pushing and video streaming. The multicast service is usually offered to users without interrupting their on-going unicast transmission, and thus the multicast and unicast beamformers needs to be jointly designed, which generally requires perfect channel state information (CSI). However, perfect CSI is usually unavailable due to the channel estimation error. In this paper, we propose a learning based approach to jointly design the multicast and unicast beamformers with imperfect CSI. To learn the beamforming strategy, a new graph neural network (GNN) based architecture named unicast-multicast GNN (UMGNN) is proposed, which only requires the estimated channel. UMGNN can guarantee the permutation invariance/equivalence and model the special property in the multicast transmission, i.e., the multicast rate is determined by the worst user. Moreover, by sharing the parameters across different users, UMGNN exhibits a pretty good scalability to different number of users. Numerical results show that UMGNN outperforms a fully connected neural network and a widely used sampling-based algorithm. To highlight its performance in the multicast transmission, we also show that UMGNN can find the correct worst user that determines the multicast rate.
Author	Zhang, Zhe Tao, Meixia Liu, Ya-Feng
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SubjectTerms	Algorithms Array signal processing Beamforming Channel estimation Graph neural networks imperfect CSI Interference Joint multicast and unicast transmission Learning Multicast algorithms Multicasting Neural networks Optimization Permutations UMGNN Unicast Video transmission
Title	Learning to Beamform in Joint Multicast and Unicast Transmission With Imperfect CSI
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