5G-NR (New Radio) CSI Computation Algorithm and Performance

Mondal, Bishwarup; Сергеев, В. А.; Sengupta, Avik; Davydov, Alexei

doi:10.1109/acssc.2018.8645339

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…The users feed back a partial CSI for each subband, namely, the dominant eigenvector of the covariance matrix of its downlink channel to the BS. However, since feeding back the actual dominant eigenvector is costly, each user first quantizes it using a discrete Fourier transform (DFT) Type-2 codebook in 5G-NR [6], and then feeds it back to the BS. Finally, having the quantized eigenvectors of the covariance matrices of the downlink channels, the BS sends data symbols to the users by utilizing some precoding techniques.…”

Section: System Model and Preliminaries A System Modelmentioning

confidence: 99%

“…In both of them, DFT beams, which are constructed from an oversampled DFT matrix, are used, but only one beam per stream is employed in Type-1 to represent the channel. In order to obtain a more accurate representation, Type-2 codebook is utilized for MU-MIMO operation [6].…”

Section: B Dft Type-2 Structure In 5g-nrmentioning

confidence: 99%

“…In order to quantize the dominant eigenvector, we utilize the search method proposed in [6]. The method is based on first finding the best DFT beams and then obtaining the respective amplitude and phase combining coefficients.…”

Section: Quantization Of the Dominant Eigenvector Of The Channelmentioning

confidence: 99%

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Channel Reconstruction Based Multiuser Precoding with Limited Feedback

Ozates

Kazemi

Goken

et al. 2021

2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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We consider the downlink of a multiuser multipleinput multiple-output (MU-MIMO) system, where each user feeds back a partial channel state information (CSI), namely, the quantized version of the dominant eigenvector of its channel covariance matrix, to the base station (BS) for precoding. Specifically, we propose a downlink multiuser precoding scheme by first reconstructing the equivalent channel matrix of each user via a limited feedback, and then by employing a precoder to suppress the multiuser interference at the receivers. For the single stream case, a signal-to-leakage-and-noise ratio (SLNR) based precoding is employed, while for the full stream case with limited feedback, we employ a lattice reduction aided block diagonalization type precoding with suitable modifications at the receiver side. Extensive numerical examples which are provided using the 5G new radio (5G-NR) channel models demonstrate that the proposed schemes outperform the existing eigenvector based algorithms, and they are more robust against the downlink channel estimation errors.

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Section: System Model and Preliminaries A System Modelmentioning

confidence: 99%

Section: B Dft Type-2 Structure In 5g-nrmentioning

confidence: 99%

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Channel Reconstruction Based Multiuser Precoding with Limited Feedback

Ozates

Kazemi

Goken

et al. 2021

2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

View full text Add to dashboard Cite

show abstract

“…Firstly, since clustering is an aspect of user scheduling, executing clustering algorithms based on instantaneous CSI is problematic, as the CSI acquired might be stale by the time it is used for user clustering. Secondly, for mmWave-NOMA systems, CSI acquisition and user tracking, which are required to establish and support highly directional transmission links, can create a tremendous amount of overhead and latency [6]. Thirdly, in practice, the availability and the reliability of CSI at base stations cannot be always guaranteed due to the following reasons: 1) Nonideal hardware behavior.…”

Section: Introductionmentioning

confidence: 99%

Vision-Assisted User Clustering for Robust mmWave-NOMA Systems

Rajasekaran¹,

Sokun²,

Maraqa³

et al. 2022

Preprint

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Non-orthogonal multiple access (NOMA) is an emerging paradigm for beyond 5G (B5G) systems to support a large number and variety of connected users, simultaneously. When operated in the mmWave band and higher bands, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of "correlated" users together and serve them in one beam in the same time slot. Identifying the set of users to cluster together greatly affects the viability of NOMA systems. Typically, only channel state information (CSI) is used to make these clustering decisions. When any problem arises in accessing up-to-date and accurate CSI, user clustering will not properly function due to its harddependency on CSI, and obviously, this will negatively affect the robustness of these NOMA systems. To improve the robustness of the NOMA systems, in this paper, we propose to utilize emerging trends such as location-aware and camera-equipped base stations (CBSs) which do not require any extra radio frequency resource consumption. Specifically, we explore three different dimensions of feedback that a CBS can benefit from to solve the user clustering problem, namely CSI-based feedback and non-CSI-based feedback, comprised of user equipment (UE) location and the CBS camera feed. We first investigate how the vision assistance of a CBS can be used in conjunction with other dimensions of feedback to make clustering decisions in various scenarios. Later, we provide a simple user case study to illustrate how to implement vision-assisted user clustering in mmWave-NOMA systems to improve robustness, in which a deep learning (DL) beam selection algorithm is trained on the images captured by the CBS to perform NOMA clustering. We demonstrate that the user clustering without CSI can achieve comparable performance to accurate CSI-based user clustering solutions, and user clustering can continue to function without much performance loss even in the scenarios where CSI is severely outdated or not available at all.

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“…For example, a searchfree algorithm is introduced in [10] exploiting the discretetime Fourier transform (DFT) codebook structure, which is based on an iterative linear phase estimation (ILPE). In [11], a low complexity precoder search algorithm is presented for both low and high resolution CSI taking the advantage of dual-stage codebook structure in 5G-NR. The work in [12] proposes computationally efficient precoder matrix selection adapting longer intervals for PMI feedback according to a calculated metric, such as the mutual information.…”

Section: Introductionmentioning

confidence: 99%

Search-Free Precoder Selection for 5G New Radio using Neural Networks

Akyildiz

Duman

2020

2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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We propose a search-free precoder selection method with neural networks motivated by the fact that large codebook sizes are adopted in 5G New Radio (5G-NR). The proposed method does not require an explicit codebook search unlike the traditional selection algorithms. Instead, it aims at finding the precoder matrix index that maximizes the corresponding channel capacity using a neural network directly. The network is trained off-line using extensive simulated data with the underlying channel statistics; however, the actual selection algorithm is based on simple calculations with the neural network, hence it is feasible for real time implementation. We demonstrate that the proposed search-free selection algorithm is highly efficient, i.e., it results in a performance very close to optimal precoder in the codebook while its complexity is significantly lower. Simulations with realistic channel models of 5G-NR corroborate these observations as well. We also show that pruning of the trained neural network gives a way to achieve further complexity reduction with a very small reduction in the system performance.

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5G-NR (New Radio) CSI Computation Algorithm and Performance

Cited by 12 publications

References 8 publications

Channel Reconstruction Based Multiuser Precoding with Limited Feedback

Channel Reconstruction Based Multiuser Precoding with Limited Feedback

Vision-Assisted User Clustering for Robust mmWave-NOMA Systems

Search-Free Precoder Selection for 5G New Radio using Neural Networks

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