IMNet: Interference-Aware Channel Knowledge Map Construction and Localization

TL;DR

This paper introduces IMNet, a two-stage deep learning approach for constructing interference-aware channel knowledge maps and localizing interfering nodes in aerial-to-ground communication channels, improving accuracy amidst interference.

Contribution

The paper proposes a novel IMNet architecture with a negative correction module for precise ISS map reconstruction in A2G channels with interference.

Findings

IMNet achieves lower construction error than baseline methods.

The method effectively localizes interfering nodes.

Simulation results validate the approach's robustness in interference scenarios.

Abstract

This paper presents a novel two-stage method for constructing channel knowledge maps (CKMs) specifically for A2G (Aerial-to-Ground) channels in the presence of non-cooperative interfering nodes (INs). We first estimate the interfering signal strength (ISS) at sampling locations based on total received signal strength measurements and the desired communication signal strength (DSS) map constructed with environmental topology. Next, an ISS map construction network (IMNet) is proposed, where a negative value correction module is included to enable precise reconstruction. Subsequently, we further execute signal-to-interference-plus-noise ratio map construction and IN localization. Simulation results demonstrate lower construction error of the proposed IMNet compared to baselines in the presence of interference.