A Bistatic Sensing System in Space-Air-Ground Integrated Networks

TL;DR

This paper proposes a joint optimization approach for a bistatic sensing system in space-air-ground networks, enhancing target detection by optimizing satellite transmit and ground receive filters, with insights into network design.

Contribution

It introduces a novel joint optimization framework for bistatic sensing in SAGIN, utilizing fractional programming and alternating optimization for improved signal-to-interference ratio.

Findings

The proposed algorithm converges well in simulations.

Receive filtering optimization is crucial, especially at higher satellite altitudes.

The method provides valuable insights for network design in SAGIN.

Abstract

Sensing is anticipated to have wider extensions in communication systems with the boom of non-terrestrial networks (NTNs) during the past years. In this paper, we study a bistatic sensing system by maximizing the signal-to-interference-plus-noise ration (SINR) from the target aircraft in the space-air-ground integrated network (SAGIN). We formulate a joint optimization problem for the transmit beamforming of low-earth orbit (LEO) satellite and the receive filtering of ground base station. To tackle this problem, we decompose the original problem into two sub-problems and use the alternating optimization to solve them iteratively. Using techniques of fractional programming and generalized Rayleigh quotient, the closed-form solution for each sub-problem is returned. Simulation results show that the proposed algorithm has good convergence performance.Moreover, the optimization of receive filtering dominates the optimality, especially when the satellite altitude becomes higher, which provides valuable network design insights.