Robust Deployment and Resource Allocation for Robotic Aerial Base Station Enabled OFDM Integrated Sensing and Communication

TL;DR

This paper proposes a robust optimization framework for deploying robotic aerial base stations in integrated sensing and communication systems, significantly improving service satisfaction rates through resource allocation strategies.

Contribution

It introduces a novel robust optimization model for RABS deployment in ISAC, formulated as a MILP, and demonstrates its effectiveness through numerical analysis.

Findings

Minimum SR improved by 28.61% on average

Formulated as a mixed-integer linear programming problem

Uses iterative linear programming rounding algorithm

Abstract

The envisioned robotic aerial base station (RABS) concept is expected to bring further flexibility to integrated sensing and communication (ISAC) systems. In this letter, characterizing the spatial traffic distribution on a grid-based model, the RABS-assisted ISAC system is formulated as a robust optimization problem to maximize the minimum satisfaction rate (SR) under a cardinality constrained uncertainty set. The problem is reformulated as a mixed-integer linear programming (MILP) and solved approximately by the iterative linear programming rounding algorithm. Numerical investigations show that the minimum SR can be improved by 28.61% on average compared to fixed small cells.