Sub-Band Full Duplex Resource Allocation: A Predictive Deep Reinforcement Learning Approach
For 6G network operators, this work addresses the need for autonomous resource management in SBFD systems under dynamic traffic conditions.
This paper proposes a predictive deep reinforcement learning framework for sub-band allocation in Sub-Band Full Duplex systems, integrating Bi-LSTM traffic forecasting with DDQN to dynamically balance uplink and downlink performance. The approach improves spectrum utilization and reduces queue buildup compared to static configurations.
This paper presents a predictive deep learning framework for dynamic sub-band allocation in Sub-Band Full Duplex (SBFD) systems, addressing the challenge of balancing uplink (UL) and downlink (DL) performance under highly dynamic traffic conditions. The key contribution lies in integrating a hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) model for traffic forecasting with a Double Deep Q-Network (DDQN) for real-time resource allocation. Using both predicted traffic and current queue states, the proposed system enables proactive scheduling based on traffic demand. Evaluation results show that the prediction model achieves high accuracy in capturing bursty traffic patterns, while the DDQN agent effectively adapts UL/DL split ratios according to traffic variations. The framework improves spectrum utilization, reduces queue buildup, and avoids inefficient static configurations. The proposed approach demonstrates that combining predictive intelligence with reinforcement learning significantly enhances the efficiency and adaptability of SBFD systems, making it a strong candidate for autonomous resource management in future 6G networks.