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.

翻译：本文提出了一种面向子带全双工系统的动态子带分配预测性深度学习框架，旨在解决高度动态流量条件下上行与下行性能平衡的挑战。核心创新在于将用于流量预测的混合双向长短期记忆模型与用于实时资源分配的双深度Q网络相结合。通过利用预测流量与当前队列状态，所提系统能够依据流量需求实现主动式调度。评估结果表明，该预测模型在捕获突发流量模式方面具有较高精度，同时DDQN智能体能够根据流量变化有效调整上下行时隙配比。该框架提升了频谱利用率，减少了队列积压，并避免了低效的静态配置。研究表明，将预测智能与强化学习相结合可显著提升子带全双工系统的效率与适应性，使其成为未来6G网络自主资源管理的理想候选方案。