# Imperfect Successive Interference Cancellation

In downlink NOMA, power allocation has relied on fixed, equal, or centralized learned rules that struggle to serve weak-channel users and throughput at once. A Kampala International University team models each user as an independent learning agent, coordinates them with a shared reward over local observations, and builds imperfect-SIC residual interference into the learning environment. In a three-user simulation the scheme reached about 18 bps/Hz and a fairness index of 0.96 at 30 dB SNR, with outage below 10% at 10 dB.

In downlink NOMA, power allocation has relied on fixed, equal, or centralized learned rules that struggle to serve weak-channel users and throughput at once. A Kampala International University team models each user as an independent learning agent, coordinates them with a shared reward over local observations, and builds imperfect-SIC residual interference into the learning environment. In a three-user simulation the scheme reached about 18 bps/Hz and a fairness index of 0.96 at 30 dB SNR, with outage below 10% at 10 dB.