A Fronthaul Signal Compression Method Based on Trellis Coded Quantization

Brito, Flavio; Berg, Miguel; Lu, Chenguang; Ramalho, Leonardo; Sousa, Ilan; Klautau, Aldebaro

doi:10.1109/latincom48065.2019.8937963

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The authors in [3] use the same structure from [1] and [2] but adopt Trellis Coded Quantization (TCQ) [4] which gives a lower EVM than SQ with a lower computational burden than VQ. In contrast with the works [1], [2] and [3], the authors in [5] use Linear Predictive Coding (LPC) to predict the n th sample of an OFDM symbol and quantize the error prediction with SQ, compressing it with Huffman Coding. The LPC-based technique was also improved in [6] with the implementation of high rate adaptation.…”

Section: A Compression Methods For C-ran Scenariosmentioning

confidence: 99%

Signal Compression for Efficient Partitioning of Deep Neural Networks

Brito¹,

Nascimento²,

Gonçalves³

et al. 2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Fifth generation (5G) mobile networks are adopting several techniques to provide higher data rates while meeting strict latency requirements. Advanced compression techniques and distributed data processing are among them supporting cost efficient network deployments. Also, the usage of machine learning techniques to optimize telecommunication systems is gaining momentum, specially after promising results with deep learning models. Following this trend, there are investigations towards splitting the processing of such models between different network nodes, making these applications more suitable and adaptable to scenarios with low processing capacity nodes. Therefore, in this work we investigate and propose different compression techniques for efficient partitioning of deep neural networks. We combine several splits with quantization and Huffman coding compression algorithms, providing insights on the configurations with the best performance. We compress the output score of the model to reduce the overhead of transmitting such scores through the network and evaluate how the accuracy is affected by this compression, and which compression technique provides the best performance.

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Section: A Compression Methods For C-ran Scenariosmentioning

confidence: 99%

Signal Compression for Efficient Partitioning of Deep Neural Networks

Brito¹,

Nascimento²,

Gonçalves³

et al. 2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…In [13], the correlation between samples is explored through the well-known linear predictive coding to solve the complexity problem posed by vector quantization. In [14], trellis coded quantization has been implemented, which provides better compression performance than scalar quantization and lower computational cost than vector quantization. In [15], a discrete cosine transform (DCT) based compression scheme has been proposed.…”

mentioning

confidence: 99%

Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network

Shehata

Mary

Crussière

2022

IEEE Trans. on Broadcast.

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A common problem of the virtualized cloud radio access network architecture (C-RAN) is the compression of the time-domain IQ samples before transmission over the fronthaul link. Considering a multicarrier waveform such as OFDM, whose IQ samples follow a quasi-Gaussian distribution, the conventional Gaussian quantizer may be used as the optimal solution to the compression problem. However, since the high peak-to-average power ratio (PAPR) of OFDM signals remains a serious problem, various techniques may be employed to reduce the time-domain fluctuations of the IQ samples in the OFDM, resulting in a change in its distribution. The latter fact makes the Gaussian quantizer suboptimal. The literature lacks a performance analysis of the conventional OFDM-based compression techniques when the PAPR of the OFDM signal is reduced. Therefore, in this paper, we study for the first time the impact of reducing the PAPR of the OFDM signal before compression in the C-RAN architecture through rate-distortion analysis. We consider clipping and tone reservation PAPR reduction algorithms. The former is the simplest PAPR reduction approach, while the latter is one of the most effective algorithms used in broadcasting standards such as DVB-T2 and ATSC 3.0. We first derive the distribution of the PAPR-reduced OFDM IQ samples. This is used to optimize the thresholds and codebook levels of a non-uniform scalar quantizer and the number of quantization bits allocated for each quantized level in the entropy coding stage, along with the MER performance analysis. The simulation results show that the conventional Gaussian-based compression techniques applied to a PAPR-reduced signal is not very robust to the statistical changes in the signal unless the signal distribution at the input of the Gaussian quantizer is not significantly affected. However, a significant gain is obtained when the quantizer is optimized with respect to the true distribution of the PAPR-reduced IQ samples.

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A Fronthaul Signal Compression Method Based on Trellis Coded Quantization

Cited by 2 publications

References 13 publications

Signal Compression for Efficient Partitioning of Deep Neural Networks

Signal Compression for Efficient Partitioning of Deep Neural Networks

Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network

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