Hybrid Deployment Optimization Algorithm for Reconfigurable Intelligent Surface

As a key 6G candidate technology, reconfigurable intelligent surface (RIS) integrates into sensor-communication systems, supporting positioning and sensing as environmental sensor nodes or anchors. To address efficient RIS deployment under constraints and mitigate wireless communication blind spots, this paper proposes a hybrid optimization algorithm. It decomposes the NP-hard combinatorial optimization problem into two stages: (1) a greedy strategy ensures coverage completeness by allocating one locally optimal RIS to each independent shadow area; (2) a Branch-and-Bound (BnB) algorithm optimizes global deployment to maximize overall signal gain in shadow areas. This decoupling reduces computational complexity for large-scale problems. Simulation results show the algorithm's superiority: the greedy phase guarantees fair coverage, and the BnB-based global optimization achieves up to 56.85% higher average Signal-to-Interference-plus-Noise Ratio (SINR) gain in shadow areas than random deployment, improving both shadow-area user communication quality and overall network performance.