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Correction

Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504

by
Andrei Stefan Nedelcu
1,
Fabian Steiner
2 and
Gerhard Kramer
2,*
1
Optical and Quantum Laboratory, Huawei Munich Research Center, 80992 Munich, Germany
2
Institute for Communications Engineering, Technical University of Munich (TUM), 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Entropy 2023, 25(3), 445; https://doi.org/10.3390/e25030445
Submission received: 13 February 2023 / Accepted: 15 February 2023 / Published: 3 March 2023
(This article belongs to the Special Issue Information Theoretic Methods for Future Communication Systems)

1. Error in Figure

In the original publication [1], there was a mistake in Figure 1 as published. The labels of the output signals x1[t]…xN[t] should appear at the output of the power amplifier as transmit waveforms. The corrected version of Figure 1 appears below.

2. Text Correction

There was an error in the original publication. “Blind detector” is incorrect and should be replaced throughout with “data aided detector”.
1. A correction has been made to Abstract:
“The information rates are computed for pilot-aided channel estimation and data-aided channel estimation.”
2. A correction has been made to 1. Introduction, 1.2. Discrete Signaling and OFDM, Paragraph Number 1:
“For this purpose, we consider two types of channel estimation at the receivers: pilot-aided channel estimation via pilot-aided transmission (PAT) and data-aided channel estimation.“
3. A correction has been made to 1. Introduction, 1.3. Contributions and Organization, Bullet Point Number 4:
“We develop an auxiliary channel model to compute achievable rates for pilot-aided and data-aided channel estimation. The models let one compare modulations, precoders, channels, and receivers;”
4. A correction has been made to 4. Performance Metrics, 4.1. Achievable Rates, Paragraph Number 2:
“We study the GMI of two non-coherent systems: classic PAT and data-aided channel estimation. For both systems, we apply memoryless signaling with the product distribution”
5. A correction has been made to 4. Performance Metrics, 4.1. Achievable Rates, Paragraph Number 5:
“For the data-aided detector we replace 𝑆𝑝 with 𝑆 in (20).”
6. A correction has been made to 4. Performance Metrics, 4.1. Achievable Rates, Paragraph Number 6, Bullet Point 3:
“For the data-aided detector, in (21) we replace S𝑝 with the set of all index pairs (ℓ,𝑚), and we replace 𝑆𝑝 with 𝑆;”
7. A correction has been made to 4. Performance Metrics, 4.1. Achievable Rates, Paragraph Number 5, Bullet Point 4:
“For the data-aided detector we set S𝑝 = ∅ in (22);”
8. A correction has been made to 4. Performance Metrics, 4.2. Discussion, Paragraph Number 1:
“Third, as 𝑆 grows, the channel estimate of the data-aided detector becomes more accurate and the performance approaches that of a coherent receiver. Related theory for PAT and large 𝑆 is developed in [49]. However, the PAT rate is generally smaller than for a data-aided detector because the PAT channel estimate is less accurate and because PAT does not use all symbols for data.”
9. A correction has been made to 4. Performance Metrics, 4.2. Discussion, Paragraph Number 2:
“We remark that blind channel estimation can approach the performance of data-aided receivers for large 𝑆. Blind channel estimation algorithms can, e.g., be based on high-order statistics and iterative channel estimation and decoding.”
10. A correction has been made to 5. Numerical Results, Paragraph Number 2:
“The average GMIs for Systems A–C were computed using 𝑆 = 256, 𝐵 = 200, and a data-aided detector. The coded results of System D instead have 𝑆 = 1584 symbols to fit the block structure determined by the LDPC encoder. For System D we considered both PAT and a data-aided detector.”
11. A correction has been made to 5. Numerical Results, Paragraph Number 7:
“The solid curves are for data-aided channel estimation and the dotted curves show the performance of PAT when the fraction of pilots is 𝑆𝑝/𝑆 = 10%.”
12. A correction has been made to 6. Conclusions, Paragraph Number 1:
“The performance was analyzed by computing the GMI for two auxiliary channel models: one model for pilot-aided channel estimation and a second model for a data-aided channel estimation.”
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Nedelcu, A.S.; Steiner, F.; Kramer, G. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Multi-user MIMO downlink with a low resolution digitally controlled analog architecture.
Figure 1. Multi-user MIMO downlink with a low resolution digitally controlled analog architecture.
Entropy 25 00445 g001
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MDPI and ACS Style

Nedelcu, A.S.; Steiner, F.; Kramer, G. Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504. Entropy 2023, 25, 445. https://doi.org/10.3390/e25030445

AMA Style

Nedelcu AS, Steiner F, Kramer G. Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504. Entropy. 2023; 25(3):445. https://doi.org/10.3390/e25030445

Chicago/Turabian Style

Nedelcu, Andrei Stefan, Fabian Steiner, and Gerhard Kramer. 2023. "Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504" Entropy 25, no. 3: 445. https://doi.org/10.3390/e25030445

APA Style

Nedelcu, A. S., Steiner, F., & Kramer, G. (2023). Correction: Nedelcu et al. Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM. Entropy 2022, 24, 504. Entropy, 25(3), 445. https://doi.org/10.3390/e25030445

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