Low-Rank Adaptation of Conditional Diffusion Transformers for Few-Shot Radio-Frequency Signal Generation
TL;DR
Downstream classifier experiments confirm that the synthetic signals help few-shot modulation classification, and they also bound the claim: conventional label-preserving transforms remain stronger at very small sample sizes, downstream utility does not track spectral fidelity, and the generated signals under-represent the noise floor in the low-SNR regime.
Abstract
Synthesizing realistic radio-frequency signals is important for data augmentation in wireless systems, yet labeled in-phase/quadrature (I/Q) measurements are expensive to collect, and diffusion models typically demand large per-class training sets. This paper studies whether a diffusion model trained on one modulation scheme can be transferred to unseen schemes with minimal data and a minimal parameter budget. We formulate the adaptation as a minimal-cardinality parameter-increment problem and adapt a conditional diffusion transformer, pretrained on QPSK signals from the RML2018.01a corpus, to three target modulations (OQPSK, 16PSK, and 32QAM) through low-rank adaptation (LoRA) of its attention projections under a composite objective that couples waveform reconstruction with spectral and autocorrelation constraints, updating only 0.496% of the 9.9-million-parameter backbone. With only 50 target signals, the adapted models attain a periodogram cosine similarity of 0.985 against 0.705 for from-scratch training and match from-scratch models given 1000 signals: a twenty-fold gain in sample efficiency. The transferred models also match or exceed reference target experts trained on up to 333 times more target data on every spectral and autocorrelation metric. Ablations on two target schemes show that sources with dense phase manifolds transfer best irrespective of family labels and that the generation quality is insensitive to the LoRA rank between 1 and 16, pointing to a low-dimensional adaptation subspace. Downstream classifier experiments confirm that the synthetic signals help few-shot modulation classification, and they also bound the claim: conventional label-preserving transforms remain stronger at very small sample sizes, downstream utility does not track spectral fidelity, and the generated signals under-represent the noise floor in the low-SNR regime. The main study uses RML2018.01a; a cross-corpus check on the independently generated Sig53 corpus reproduces the transfer advantage with compressed margins, and validation on over-the-air recordings remains future work.