G-Wave: Bit-to-Waveform Generation for Adaptive Wireless Communications

Wireless waveforms are traditionally hand-designed for general propagation conditions, rather than adapted to each specific environment. Existing neural transceivers seek such adaptation, but often rely on relatively simple architectures with limited long-range modeling capability. More expressive generative models such as Transformers are difficult to apply directly because wireless communication demands extremely accurate bit recovery, often with BER below $0.1\%$, while providing no ground-truth waveform supervision. To address these limitations, we introduce , a first generative transceiver that directly synthesizes environment-adaptive physical time-domain waveforms from bits. consists of a new Transformer-based transmitter and receiver models equipped with a multiscale architecture that combines long-range sequence modeling with fine-grained signal synthesis to meet the stringent precision required for communication. The two models are jointly optimized end-to-end using only bit-reconstruction error, allowing the waveform itself to be discovered without waveform labels. Across diverse 3GPP wireless environments, achieves lower BER than the SOTA neural transceiver (DeepOFDM) and a strong conventional baseline (OFDM) under estimated channel information, reducing mean BER by $42.4\%$ and $72.5\%$, respectively.