Benedikt Geiger

In OFDM-ISAC, communications favor waterfilling power with Gaussian-like inputs while sensing favors uniform power and constant-modulus constellations, creating a trade-off in frequency-selective channels. This work treats per-subcarrier power and kurtosis as joint variables and uses an autoencoder to train offline a family of finite constellations conditioned on kurtosis, generated online for each allocation. Simulations show joint optimization clearly beats optimizing power or constellation alone at intermediate SNR, and the pretrained family nearly realizes the predicted allocation gains.

In OFDM-ISAC, communications favor waterfilling power with Gaussian-like inputs while sensing favors uniform power and constant-modulus constellations, creating a trade-off in frequency-selective channels. This work treats per-subcarrier power and kurtosis as joint variables and uses an autoencoder to train offline a family of finite constellations conditioned on kurtosis, generated online for each allocation. Simulations show joint optimization clearly beats optimizing power or constellation alone at intermediate SNR, and the pretrained family nearly realizes the predicted allocation gains.