Interference Mitigation and Resource Allocation in Integrated TN-NTN Systems via Deep Q-Learning

As the demand for spectrum increases, the coexistence of terrestrial networks (TNs) and non-terrestrial networks (NTNs) is becoming a critical aspect of 6th generation (6G) communication scenarios. Integrated TN-NTN systems share the same frequency bands, leading to significant performance degradation due to co-channel interference. This paper proposes a deep Q-network (DQN) based deep reinforcement learning (DRL) algorithm to efficiently allocate resource blocks within shared frequency bands to each user equipment (UE) in integrated TN-NTN systems. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies. The proposed algorithm continuously interacts with the network environment to learn optimal resource allocation policies, thereby minimizing interference and maximizing system data throughput, promoting efficient use of spectrum resources.