Limited Feedback Designs for Machine-Type Communications Exploiting User Cooperation

Song, Jiho; Lee, Byungju; Noh, Song; Lee, Jong-Ho

doi:10.1109/access.2019.2928633

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Thus, if we use an antenna-combining method associated with the quantization process, such as quantization-based combining (QBC) [ 10 ], conventional SCVQ will not provide a close approximation. QBC is an effective antenna-combining method that reduces the quantization error caused by limited feedback using multiple receive antennas, and it has been widely employed to enhance feedback performance [ 24 , 25 , 26 , 27 ]. Thus, if we have an appropriate analytical model, such as SCVQ appropriate for QBC, it can be effectively used to evaluate the performance of various complicated MIMO systems utilizing QBC.…”

Section: Introductionmentioning

confidence: 99%

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback

Min

Kim

2022

Sensors

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The spherical-cap approximation of vector quantization (SCVQ) is an analytical model used for the mathematical analysis of multiple-input multiple-output (MIMO) systems with limited feedback. SCVQ closely emulates the distribution of the quantization error induced by the finite-rate quantization of a channel using a simple and analytically tractable approach. However, the conventional SCVQ model is not applicable when antenna-combining schemes such as quantization-based combining (QBC) are considered. Because QBC is an effective antenna-combining method that minimizes channel quantization errors, it can be adopted for various practical MIMO broadcast systems. Nevertheless, evaluating the performance of QBC-based MIMO systems with an explicit codebook can be extremely difficult, depending on the system complexity. To resolve this, this study generalizes the conventional SCVQ to be compatible with the QBC. The proposed generalized version of the SCVQ effectively emulates the quantization error obtained using QBC, while enabling a simple simulation independent of the number of feedback bits and mathematically tractable analysis. We validate the effectiveness of the proposed model by presenting a wireless communication application based on a dense cellular network.

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Section: Introductionmentioning

confidence: 99%

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback

Min

Kim

2022

Sensors

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“…Enabled by device-to-device (D2D) communications, the conventional cooperation mechanism is developed in [40], [41]. CSI magnitude and phase are separately fed back, rather than together as complex channel gains.…”

Section: Introductionmentioning

confidence: 99%

DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

Guo¹,

Yang²,

Wen³

et al. 2020

Preprint

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In this paper, we exploit the correlation between nearby user equipment (UE) and develop a deep learning-based channel state information (CSI) feedback and cooperative recovery framework, CoCsiNet, to reduce the feedback overhead. The CSI can be divided into two parts: shared by nearby UE and owned by individual UE. The key idea of exploiting the correlation is to reduce the overhead used to repeatedly feedback shared information. Unlike in the general autoencoder framework, an extra decoder is added at the base station to recover shared information from the feedback CSI of two nearby UE, but no modification is performed at the UE. For a UE with multiple antennas, we also introduce a baseline neural network architecture with long short-term memory modules to extract the correlation of nearby antennas. We propose two representative methods for converting CSI into bitstreams at the UE through quantization and binarization. Given that the CSI phase is not sparse, we propose a magnitudedependent phase feedback strategy that introduces CSI magnitude information to the loss function of the phase feedback. Simulation results show the effectiveness of the proposed CoCsiNet and explain the mechanism of the CoCsiNet via parameter visualization.

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“…The effective single antenna channel provides analytic flexibility in limited feedback systems. By using its flexibility, QBC has been widely investigated in various wireless applications [ 19 , 20 , 21 , 22 , 23 ]. Compared to ZFBF, the number of feedback bits is significantly reduced to combine with the block diagonalization beamforming in [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…In [ 22 ], the QBC scheme is applied to orthogonal frequency division multiplexing systems by using subcarrier grouping. In a recent study, and with user cooperation, the QBC scheme was applied to a machine type communication networks [ 23 ]. This demonstrated that the QBC scheme is of benefit in various networks; however, its potential gain has not been investigated in actual cellular networks.…”

Section: Introductionmentioning

confidence: 99%

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Antenna Combining for Interference Limited MIMO Cellular Networks

Kim

2020

Sensors

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This paper considers a downlink cellular network where multi-antenna base stations (BSs) simultaneously serve their associated multi-antenna users. Each BS is distributed according to a homogeneous Poisson point process and uses zero-forcing beamforming for spatial division multiplexing with partial channel state information (CSI). During downlink transmission, each user combines the multiple antenna outputs and quantizes the CSI to feed back to its associated BS. Specifically, this paper focuses on antenna combining at the receiver. Conventional quantization-based combining (QBC) effectively reduces the quantization error; however, inter-cell interference in the cellular networks degrades the QBC gain. This degradation is analyzed using a spherical-cap approximation of vector quantization (SCVQ). From the SCVQ, the ergodic spectral efficiency and the optimal number of feedback bits are investigated, and it is shown that the QBC degrades the gain of the effective channel. To address this problem, an optimization solution is proposed that selects the antenna combining to maximize the spectral efficiency. The solution is also derived by considering the expected beamforming vectors of other cells. It is demonstrated by simulation that the proposed solution outperforms the conventional methods.

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Limited Feedback Designs for Machine-Type Communications Exploiting User Cooperation

Cited by 5 publications

References 29 publications

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback

Spherical-Cap Approximation of Vector Quantization for Quantization-Based Combining in MIMO Broadcast Channels with Limited Feedback

DL-based CSI Feedback and Cooperative Recovery in Massive MIMO

Antenna Combining for Interference Limited MIMO Cellular Networks

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