Low Complexity Channel Estimation for Hybrid Beamforming Systems

Tang, Yanru; Cheng, Hongbing; Song, Kee-Bong

doi:10.1109/vtc2020-spring48590.2020.9128986

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…

of the receivers that do not support Rx beam sweeping is just a measurement vector by only Tx beam sweeping. For

and

, various matrices can be adopted [ 19 , 20 , 21 , 22 ]. In this paper, discrete Fourier transform (DFT) matrix is used for Tx and Rx beam sweeping.…”

Section: Quantized Channel Feedback Based On Non-linear Quantization Of Sparse Vectorsmentioning

confidence: 99%

“…To realize beamforming and MU-MIMO systems sufficiently, the improvement of channel estimation and feedback techniques is important. For channel estimation of hybrid beamforming systems, many schemes have been proposed in frequency-division duplex (FDD) and time-division duplex (TDD) environments [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. In FDD systems, uplink and downlink channels use different bands.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of MIMO channel, the sparsity is also observed since the received signals of multiple antennas propagate through similar paths [ 3 ]. By exploiting the sparsity, compressed sensing (CS) can provide reasonable performance for channel estimation although the rate of antenna numbers is high [ 19 , 20 , 21 , 22 , 26 , 27 , 28 ]. The schemes can be classified into three groups.…”

Section: Introductionmentioning

confidence: 99%

“…The schemes can be classified into three groups. The schemes of the first group have focused on the design of beam sweeping vectors to acquire qualified measurements for CS [ 19 , 26 ], and the schemes of the second group provided methods to reduce the computational load of the conventional OMP operation [ 21 , 22 , 26 , 28 ]. The schemes of the last group utilized structure of channel basis to improve the accuracy of channel estimation [ 20 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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.

show abstract

“…

of the receivers that do not support Rx beam sweeping is just a measurement vector by only Tx beam sweeping. For

and

, various matrices can be adopted [ 19 , 20 , 21 , 22 ]. In this paper, discrete Fourier transform (DFT) matrix is used for Tx and Rx beam sweeping.…”

Section: Quantized Channel Feedback Based On Non-linear Quantization Of Sparse Vectorsmentioning

confidence: 99%