Spectrum Based Resource Allocation Using Auction Based Game Theoretic Approach

Efficient spectrum allocation is a crucial challenge in edge computing, where limited wireless resources must support diverse applications with strict latency constraints. As edge computing relies on real-time data processing close to users, optimizing spectrum usage is essential for maintaining performance. Auction-based models have emerged as a promising solution, enabling fair, efficient, and incentive-compatible spectrum allocation by allowing participants to bid dynamically based on demand. In this paper, we propose a novel auction-based spectrum allocation model tailored to the unique characteristics of edge networks, considering factors such as low latency requirements, dynamic resource demands, and heterogeneous edge devices. Our model incorporates advanced auction mechanisms, including combinatorial auctions, which allow bidding on multiple spectrum blocks simultaneously for improved efficiency, and double auctions, which match spectrum buyers and sellers while maximizing utilization and fairness. Through simulations, we demonstrate that our approach significantly enhances spectrum efficiency, fairness, and computational scalability compared to traditional allocation methods. The findings highlight the potential of auction-based spectrum allocation models as a key enabler for future edge computing systems, ensuring optimal resource utilization and improved network performance.