Edge AI Inference in ISCC Networks: Sensing Accuracy Analysis and Precoding Design

TL;DR

This paper analyzes the impact of precoding on sensing accuracy in ISCC networks for edge AI inference, proposing a new design that improves accuracy by up to 15% in simulations.

Contribution

It introduces a discriminant gain metric, derives an explicit DG-precoding relationship, and develops an effective precoding algorithm for ISCC networks.

Findings

Proposed precoding algorithm improves sensing accuracy by up to 15% on synthetic data.

Achieves up to 10% accuracy improvement on real-world datasets.

Validates the effectiveness of the design through simulations at low SNR.

Abstract

This work explores the relationship between sensing accuracy and precoding coefficients for edge artificial intelligence (AI) inference in integrated sensing, communication and computation (ISCC) networks. We start by constructing a system model of an over-the-air-empowered ISCC network for edge AI inference, involving distributed edge sensors for feature extraction and an edge server for classification. Based on this model, we introduce a discriminant gain (DG) to characterize sensing accuracy and novelly derive an explicit function of the DG about precoding coefficients, giving valuable insights into precoding design. Guided by this, we propose an effective precoding algorithm to solve a non-convex DG-maximization problem. Simulation results demonstrate that the proposed design achieves up to 15% and 10% sensing accuracy improvements on synthetic and real-world datasets, respectively, over the conventional scheme at low SNR, thereby validating its effectiveness and superiority for edge AI inference in ISCC networks.