Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel

Li, Xiang; Wu, Jianhui; Wei, Heng; Huang, Yang

doi:10.1109/access.2020.3037807

Cited by 5 publications

(3 citation statements)

References 30 publications

(37 reference statements)

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“…This equalizer is ideal, especially for reducing the symbol error probability. To calculate the LLR a posteriori L(u i |y ), it was used the trellis representation associated with the transmission on the selective channel in frequency [38]. By applying the Bayes relation, the relation for LLR can be written in the sense of Equation (1).…”

Section: Log-map Equalizermentioning

confidence: 99%

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

Cuc,

Morgoș,

Grava

et al. 2023

Applied Sciences

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In this article we present an iterative system of equalization and decoding to manage the intersymbol interference over an additive white Gaussian noise (AWGN) channel. Following the classic turbo equalization scheme, the proposed system consists of low-density parity-check (LDPC) coding at the transmitter side; we applied a Log maximum a posteriori probability (Log-MAP) equalizer and min-sum LDPC decoding at the receiver side. The equalizer and decoder, linked through interleaving and deinterleaving, iteratively update each other’s information. We performed the performance analysis of the proposed system, bit error rate (BER) vs. signal-to-noise ratio (SNR), considering three different impulse responses of the channel (h). Our experimental results indicated that increasing the number of iterations performed by the LDPC decoder from 10 to 20 during the iterative process of equalization and decoding leads to better outcomes. The proposed system was compared with turbo equalization and separate equalization, performed before the decoding process with minimum mean-square error (MMSE) and LDPC decoding, in terms of BER vs. SNR, considering the three different h. Based on the analyzed results, it can be concluded that the equalization performance depends on both the impulse responses of the channel and the chosen decoding and equalization method; therefore, the equalization method does not always offer good results for any h.

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Section: Log-map Equalizermentioning

confidence: 99%

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

Cuc,

Morgoș,

Grava

et al. 2023

Applied Sciences

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“…Hence, numerical simulations need to be carried out to find the optimal ν such as in [7], [8], [30]. Nevertheless, the principle of the PCS is to reduce H such that the achievable rate falls below the true capacity of the channel, which then yields significant BLER improvements [29]. However, with increasing ν, the effective modulation-order is decreased and the BLER can be made arbitrarily small for any channel.…”

Section: When Can Pcs Provide Gains In Throughput?mentioning

confidence: 99%

Supporting Probabilistic Constellation Shaping in 5G-NR

Hu¹,

Wang²,

Semnov³

2022

Preprint

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<p>In this paper, we consider applying probabilistic constellation shaping (PCS) to the fifth-generation new-radio (5G-NR) system. We propose a practical PCS transceiver design which is fully backward compatible to the existing 5G-NR system, and can bring significant improvements in block-error rate (BLER) and throughput. Further, we derive the properties of average-power, entropy-loss, and peak-to-average power-ratio (PAPR) increment in connection to the proposed PCS design. We show that an effective PCS can set the variance of a zero-mean Gaussian distribution for shaping to be around 0.5,making both the PAPR-increment and the entropy-loss to be at acceptable levels. Furthermore, we derive a necessary condition for PCS to attain a higher throughput than a uniform constellation. It shows that the normalized entropy-loss with PCS must be smaller than the BLER attained from transmissions with a uniform constellation. This is a critical observation and provides an important insight for the PCS design. Moreover, we also illustrate that the proposed PCS scheme can provide additional flexibilities in adjusting the effective code-rate without affecting the encoder. As a result, transitions among different MCSs can be smoother even with an adaptive modulation and coding scheme (MCS), and a better throughput-envelope can be obtained as signal-to-noise ratio (SNR) changes.</p>

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“…Conventionally, the analysis of PCS is mostly focused on theoretical shaping-gains, which assumes a system that operates close to the capacity with optimal coding and decoding in Shannon sense. In practice, the system can operate far away from the capacity-bound [28], especially with a multi-input multi-output (MIMO) transmission under a fading channel, and further with a finite code-length [38], [39], [49]. To be able to make fair comparisons between PCS-QAM and Uniform-QAM with practical systems, we generalized the shaping-gain to be: Either the SNR-gain with a PCS-QAM when it attains the same throughput as a Uniform-QAM, or the rate-increment with a PCS-QAM compared to a Uniform-QAM at the same SNR.…”

Section: Introductionmentioning

confidence: 99%

Supporting Probabilistic Constellation Shaping in 5G-NR Evolution

Hu,

Wang,

Semenov

2024

IEEE Trans. Wireless Commun.

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It is well-known that probabilistic constellation shaping (PCS) provides a shaping-gain of 1.53dB asymptotically as signal-to-noie (SNR) increases. This is however, under ideal assumptions that the considered system operates in optimal sense and can achieve the Shannon capacity. While in practice the system can operate below the capacity, and in particular when block-error rate (BLER) and throughput are considered which are the most relevant merits, the benefits of PCS are unclear. In this paper, we propose a PCS-transceiver for the fifth-generation new-radio (5G-NR) that supports quadratureamplitude-modulation (QAM) constellations shaped with PCS, namely, PCS-QAM. We put a special interest in the throughput achieved under a stringent BLER constraint, and validate the effectiveness of PCS with practical detection and decoding algorithms, in comparison to conventional uniform QAM constellations, namely, Uniform-QAM. We also analyze the properties of power-gain, entropy-loss, and peak-to-average power-ratio (PAPR), in connections to the PCS design. Further, we derive a necessary and sufficient condition for a PCS-QAM to outperform a Uniform-QAM in throughput, and prove that the normalized entropy-loss with the PCS-QAM must be less than the BLER obtained with the Uniform-QAM. Furthermore, we demonstrate that the PCS-transceiver is flexible in rate-adaption without affecting the encoder, and yields a better throughput-envelope by mitigating the SNR-gaps between two adjacent modulation and coding scheme (MCS) indexes in 5G-NR.

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Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel

Cited by 5 publications

References 30 publications

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

Supporting Probabilistic Constellation Shaping in 5G-NR

Supporting Probabilistic Constellation Shaping in 5G-NR Evolution

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