Computation Offloading Scheduling Using Game Abstraction in Ultra-dense Networks

Ultra-dense networks are a key technology for 5G, characterized by having more cells than active users. However, as the density of small cell base stations (SBS) increases, ultra-dense networks introduce challenges in the exploration of resource allocation methods, particularly with respect to computational complexity and network scalability. To address these issues, this paper proposes a computation offloading and scheduling model for each SBS in ultra-dense networks. Specifically, in the proposed model, each SBS receives computing tasks from mobile devices and stores them in a queue buffer. Subsequently, each SBS first determines how many tasks need to be processed locally and whether offloading them is necessary. To tackle the complexity of this decision-making process, we employ game theory abstraction to reduce the large-scale state space and design a fast, adaptive computation offloading algorithm (FACOA). This algorithm enables each SBS to learn efficiently and quickly adapt to strategy changes made by other SBSs. Finally, simulation and experiments show that the proposed algorithm shorten the convergence time by 6 times.