s (called stay-and-adjust). (3) Switch to another channel that meets the QoS requirement (called spectrum handoff). In [207], a learning-based and QoE-driven spectrum handoff scheme was proposed to maximize the multimedia users' satisfaction. A mixed preemptive and non-preemptive resume priority (PRP/NPRP) M/G/1 queueing model was designed for the spectrum usage behaviors of prioritized multimedia applications. The RL-assisted QoE-driven spectrum handoff scheme was developed to maximize the quality of video transmissions in the long term. Their proposed learning scheme could adaptively perform spectrum handoff based on the variation of channel conditions and traffic loads. To address limitations of PRP/NPRP queuing models, the authors in [210] employed a hybrid queuing model with discretion rules to characterize the SUs' spectrum access pri- orities. The channel waiting time during spectrum handoff was then calculated according to this hybrid queuing model. The multi-teacher knowledge transfer method was further proposed to accelerate the algorithm, wherein the multiple SUs that already had mature spectrum adaptation strategies could share their knowledge with an inexperienced SU. 10.2.2 Database-Assisted SS The sensing-driven OSA system aims to explore the spectrum holes in the unlicensed band, which can be inefficient and unreliable, especially when the number of wireless communi- cation devices increases rapidly. To better serve the secondary system, a specific spectrum band like 3.5 GHz is opened to the public. Database-assisted Dynamic SS (DSS) systems such as LSA and SAS have been proposed to better coordinate the users in different systems with various spectrum access priorities. LSA has two types of users: incumbents and SUs, where incumbents send their spec- trum usage information to a database center called LSA Repository [136]. The system then decides whether the SUs can access the spectrum resource with this information or not and no sensing ability is required for those users. The SAS system also mai