MU-MIMO and CSI Feedback Performance of NR/LTE

Mondal, Bishwarup; Сергеев, В. А.; Sengupta, Avik; Ermolaev, Gregory; Davydov, Alexei; Kwon, Eddy; Han, Seunghee; Papathanassiou, Apostolos

doi:10.1109/ciss.2019.8692922

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…Nowadays MU-MIMO with very large antenna arrays is an integral part of the 5G NR cellular standard published by the 3GPP [14]- [16]. MU-MIMO pilots, channel estimation and feedback procedures in the standard are concisely described in [17]. Signal processing techniques for mmWave systems are reviewed in [18].…”

Section: B State Of the Artmentioning

confidence: 99%

Hybrid Beamforming in 5G mmWave Networks: a Full-stack Perspective

Gómez-Cuba¹,

Zugno²,

Kim³

et al. 2020

Preprint

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This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on full stack simulation of mmWave cellular network has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance in the physical layer. Particularly, throughput can be increased in 5G networks by means of Spatial Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution.

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Section: B State Of the Artmentioning

confidence: 99%

Hybrid Beamforming in 5G mmWave Networks: a Full-stack Perspective

Gómez-Cuba¹,

Zugno²,

Kim³

et al. 2020

Preprint

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“…Another promising technique to accommodate the growth of wireless devices is multi-user MIMO (MU-MIMO) [ 17 , 18 ]. Wireless systems that use MU-MIMO precoding can transmit independent data streams to multiple receivers stably in the same time-frequency resource block.…”

Section: Introductionmentioning

confidence: 99%

Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems

Lee

Song

2021

Sensors

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This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should communicate with multiple devices at the same time. To communicate with multiple devices in the same time and frequency slot, high-dimensional channel information should be used to control interferences between the receivers. Therefore, the feedback overhead for the channels of the devices is impractically high. To reduce the overhead, this paper uses common sparsity of channel and nonlinear quantization. To find a common sparse part of a wide frequency band, the proposed system uses minimum mean squared error orthogonal matching pursuit (MMSE-OMP). After the search of the common sparse basis, sparse vectors of subcarriers are searched by using the basis. The sparse vectors are quantized by a nonlinear codebook that is generated by conditional random vector quantization (RVQ). For the conditional RVQ, the Linde–Buzo–Gray (LBG) algorithm is used in conditional vector space. Typically, elements of sparse vectors are sorted according to magnitude by the OMP algorithm. The proposed quantization scheme considers the property for the conditional RVQ. For feedback, indices of the common sparse basis and the quantized sparse vectors are delivered and the channel is recovered at a transmitter for precoding of MU-MIMO. The simulation results show that the proposed scheme achieves lower MMSE for the recovered channel than that of the linear quantization scheme. Furthermore, the transmitter can adopt analog and digital precoding matrix freely by the recovered channel and achieve higher sum rate than that of conventional codebook-based MU-MIMO precoding schemes.

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“…In multiuser MIMO communication, plural streams for respective user terminals are multiplexed at the same time and the same frequency. According to the 5G specification, up to 12 layers for multiuser MIMO are to be supported [7]. In a cellular-based uplink multiuser MIMO system, BS estimate channel state information (CSI) from the pilot signal transmitted by the desired user in the target cell.…”

Section: Introductionmentioning

confidence: 99%