Cost Optimization for Reconfigurable Intelligent Surface Resource Leasing in 6G Networks

Reconfigurable Intelligent Surfaces (RIS) have been used for aiding transmission in densely populated areas where the direct communication between the tower and user is blocked by obstacles. This work introduces a system in which an RIS Service Provider (RSP) strategically deploys RIS on buildings to establish alternative communication path between the sender and the receiver. Competing 6G Network Operators (6GNOs) lease the available RIS resources and as a result we assume that RSP implements a dynamic cost model for its RIS infrastructure. Within this 6G ecosystem, we develop a decision support framework (DSF) for a cost-savvy network operator (CSNO) that aims to lease RIS infrastructure for delay-tolerant (DT) applications. We develop a queue-based modeling approach, the CSNO maintains a data queue where packets arrive randomly. In non Line-of-Sight (LOS) scenario, DSF assists the CSNO in making optimal RIS leasing decisions. DSF adopts a threshold-based policy derived from the Lyapunov optimization technique, enabling a tradeoff between quality of service (QoS) and overall leasing cost for the CSNO. Simulation results demonstrate the effectiveness of the proposed DSF, showing significant cost savings and improved performance over baseline strategy.