Hybrid Beamforming in Massive-MIMO mmWave Systems Using LU Decomposition

Aljumaily, Mustafa S.

doi:10.1109/vtcfall.2019.8891274

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In order to further improve the performance of massive MIMO systems, a full-space spectrum-sharing strategy [16], a unified transmission strategy for TDD/FDD [17], and the hybrid beamforming as a combination of digital baseband and analog RF phase shifters [18,19] were proposed. The hybrid beamforming method increases an efficiency of beamforming, and hence, the performance of MU systems is improved.…”

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Combining precoding and equalization for interference cancellation in MU-MIMO systems with high density users

Phương

Son

Hiep

2022

J Wireless Com Network

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In multiple users (MU) multiple input multiple output (MIMO) systems, the non-orthogonal multiple access (NOMA) method can provide multiple access. However, a spectrum efficiency of NOMA method is restricted because of remaining an interference signal of another user. A beamforming method can also be applied into MU-MIMO systems for implementation of multiple access without the interference with the other users; however, it is unavailable for a high density user environment in which multiple users locate close to each other. In order to further improve the spectrum efficiency, we propose a novel signal processing method which joints a precoding and an equalization. First, a precoding matrix is prepared for every user at base station, and we design an equalization which is orthogonal to the precoding matrix. Second, a set of linear weights is proposed to calculate at users for cancelling any interference signal. Therefore, signals for other users are eliminated according to the orthogonal feature and linear signal processing, and then, every user can receive its own signal without the interference signal. The proposed method is compared with the beamforming and NOMA methods in several scenarios, and the calculation result shows that our method outperforms other methods. Especially, our novel method works properly in the case of high density users.

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confidence: 99%