E2E-WAVE: End-to-End Learned Waveform Generation for Underwater Video Multicasting
We present E2E-WAVE, the first end-to-end learned waveform generation system for underwater video multicasting. Acoustic channels exhibit 20--46% bit error rates where forward error correction becomes counterproductive -- LDPC increases rather than decreases errors beyond its decoding threshold. E2E-WAVE addresses this by embedding semantic similarity directly into physical layer waveforms: when decoding errors are unavoidable, the system preferentially selects semantically similar tokens rather than arbitrary corruption. Combining VideoGPT tokenization (1024x compression) with a trainable waveform bank and fully differentiable OFDM transmission, E2E-WAVE achieves +5 dB (19.26%) PSNR and ...
| Authors | Khizar Anjum, Tingcong Jiang, Dario Pompili |
|---|---|
| Primary category | eess.SP |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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