Thang X. Vu

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.