# Orthogonal Frequency Division Multiplexing

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.

The work introduces GREQ, a two-phase hybrid model-learning framework: phase one uses a channel-statistics-based greedy algorithm to deterministically satisfy user QoS with minimal subchannels, and phase two exploits the OFDM property that inter-cell interference on a subchannel comes only from the same subchannel to decompose the large-scale MARL problem into independent per-subchannel QMIX subproblems, avoiding exponential action-space growth; in simulations with 50MHz bandwidth and different numerologies, GREQ improves throughput by up to 40% over optimization and learning baselines, achieves 100% constraint satisfaction, and activates only 20–80% of available subchannels.

The work introduces GREQ, a two-phase hybrid model-learning framework: phase one uses a channel-statistics-based greedy algorithm to deterministically satisfy user QoS with minimal subchannels, and phase two exploits the OFDM property that inter-cell interference on a subchannel comes only from the same subchannel to decompose the large-scale MARL problem into independent per-subchannel QMIX subproblems, avoiding exponential action-space growth; in simulations with 50MHz bandwidth and different numerologies, GREQ improves throughput by up to 40% over optimization and learning baselines, achieves 100% constraint satisfaction, and activates only 20–80% of available subchannels.