  Managing inter-cell interference is among the major challenges in a wireless
network, more so when strict quality of service needs to be guaranteed such as
in ultra-reliable low latency communications (URLLC) applications. This study
introduces a novel intelligent interference management framework for a local 6G
network that allocates resources based on interference prediction. The proposed
algorithm involves an advanced signal pre-processing technique known as
empirical mode decomposition followed by prediction of each decomposed
component using the sequence-to-one transformer algorithm. The predicted
interference power is then used to estimate future signal-to-interference plus
noise ratio, and subsequently allocate resources to guarantee the high
reliability required by URLLC applications. Finally, an interference
cancellation scheme is explored based on the predicted interference signal with
the transformer model. The proposed sequence-to-one transformer model exhibits
its robustness for interference prediction. The proposed scheme is numerically
evaluated against two baseline algorithms, and is found that the root mean
squared error is reduced by up to 55% over a baseline scheme.
