SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity
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Abstract (arXiv)
A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality while reducing the bandwidth requirement by exploiting frequency domain sparsity through compressed sensing. The proposed algorithm produces a linear relationship between the channel signal-to-noise ratio (CSNR) and peak signal-to-noise ratio (PSNR), without requiring the channel state knowledge at the transmitter. This is particularly attractive when transmitting to multiple receivers or over unknown time-varying channels, as the receiver PSNR depends on the experienced channel quality, and is not bottlenecked by the worst channel. SparseCast is benchmarked against two alternative algorithms: SoftCast and BCS-SPL. Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.5 dB and BCS-SPL by 15.2 dB.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2018.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 1.
4 papers.
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021DeepWiVe: Deep-Learning-Aided Wireless Video Transmissioncited by 45
- 2024Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designscited by 32
- 2021Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channelscited by 0
- 2024Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs26 Sep 2024
- 2021DeepWiVe: Deep-Learning-Aided Wireless Video Transmission25 Nov 2021
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challenges5 Apr 2021
- 2021Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels2 Jan 2021