Joint Source-Channel Coding of Gaussian sources over AWGN channels via Manifold Variational Autoencoders

Saidutta, Yashas Malur; Abdi, Afshin; Fekri, Faramarz

doi:10.1109/allerton.2019.8919888

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…In [17], used neural networks, in particular, Variational Autoencoders (VAEs) to design neural network based Joint Source Channel Coding and extended the system design to where k ≠1. However, their performance was reasonable but a lot was required to improve upon their performance in order to meet up with the benchmark set by [18] . The authors of, [18] developed a new scheme for JSCC of Gaussian sources over AWGN channels.…”

Section: Related Workmentioning

confidence: 99%

“…However, their performance was reasonable but a lot was required to improve upon their performance in order to meet up with the benchmark set by [18] . The authors of, [18] developed a new scheme for JSCC of Gaussian sources over AWGN channels. VAEs was implemented in their design but with a novel encoder architecture for the VAE specifically developed for zerodelay Gaussian JSCC over AWGN channels, a situation where the source dimension (m) is greater than the channel dimension (k).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

Chikezie¹,

Femi²,

Ozioma³

et al. 2022

Adv. sci. technol. eng. syst. j.

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In the time past, virtually all the contemporary communication systems depend on distinct source and channel encoding schemes for data transmission. Irrespective of the recorded success of the distinct schemes, the new developed scheme known as joint source channel coding technique has proven to have technically outperformed the conventional schemes. The aim of the study is centered in developing an enhanced joint source-channel coding scheme that could mitigate some of the limitations observed in the contemporary joint source channel coding schemes. The study tends to leverage on recent developments in machine learning known as deep learning techniques for robust and enhanced scheme, devoid of explicit code dependence for the signal compression and as well in error correction but learn automatically on end-to-end mapping structure for the source signals.It primarily aimed at providing an improved channel performance approach for wireless communication network. A deep learning algorithm was implemented in the study, the scheme focused on improving the Bit Error Rate (BER) performance while reducing latency and the processing complexity in Joint Source Channel Coding systems. The deep learning autoencoder model was deployed to compare with the hamming code, convolution code, and uncoded systems. JSCC using neural networks were simulated based on BER performance over a range of energy per symbol to noise ratio (Eb/No). Training and test error for the fully connected neural network autoencoder models on channels with 0.0dB and 8.0dB were carried out. The results obtained showed that the autoencoder model had a better BER performance when compared with the convolution code and uncoded systems, it also outperformed the uncoded BFSK with an approximately equal BER performance when compared with the hamming code (soft decision) decoding system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

Chikezie¹,

Femi²,

Ozioma³

et al. 2022

Adv. sci. technol. eng. syst. j.

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“…The authors established that the self-concatenated convolutional codes give the best possible results. The study [ 28 ] puts forward a scheme for coding source and channel jointly to be transmitted through AWGN using neural network algorithms. The proposed system is trained with around 3000 samples.…”

Section: Related Contemporary Workmentioning

confidence: 99%

Performance Analysis of Sphere Packed Aided Differential Space-Time Spreading with Iterative Source-Channel Detection

Khan

Minallah

Ahmad

et al. 2021

Sensors

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The introduction of 5G with excessively high speeds and ever-advancing cellular device capabilities has increased the demand for high data rate wireless multimedia communication. Data compression, transmission robustness and error resilience are introduced to meet the increased demands of high data rates of today. An innovative approach is to come up with a unique setup of source bit codes (SBCs) that ensure the convergence and joint source-channel coding (JSCC) correspondingly results in lower bit error ratio (BER). The soft-bit assisted source and channel codes are optimized jointly for optimum convergence. Source bit codes assisted by iterative detection are used with a rate-1 precoder for performance evaluation of the above mentioned scheme of transmitting sata-partitioned (DP) H.264/AVC frames from source through a narrowband correlated Rayleigh fading channel. A novel approach of using sphere packing (SP) modulation aided differential space time spreading (DSTS) in combination with SBC is designed for the video transmission to cope with channel fading. Furthermore, the effects of SBC with different hamming distances d(H,min) but similar coding rates is explored on objective video quality such as peak signal to noise ratio (PSNR) and also the overall bit error ratio (BER). EXtrinsic Information Transfer Charts (EXIT) are used for analysis of the convergence behavior of SBC and its iterative scheme. Specifically, the experiments exhibit that the proposed scheme of error protection of SBC d(H,min) = 6 outperforms the SBCs having same code rate, but with d(H,min) = 3 by 3 dB with PSNR degradation of 1 dB. Furthermore, simulation results show that a gain of 27 dB Eb/N0 is achieved with SBC having code rate 1/3 compared to the benchmark Rate-1 SBC codes.

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AEVBComm: an intelligent communication system based on $$\beta$$-VAE

Hemadri,

Rayaluru,

Pandya

et al. 2024

CSIT

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Joint Source-Channel Coding of Gaussian sources over AWGN channels via Manifold Variational Autoencoders

Cited by 11 publications

References 14 publications

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks

Performance Analysis of Sphere Packed Aided Differential Space-Time Spreading with Iterative Source-Channel Detection

AEVBComm: an intelligent communication system based on $$\beta$$-VAE

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