Semantic Sensing: A Task-Oriented Paradigm

TL;DR

This paper introduces semantic sensing (SemS), a task-oriented framework that optimizes sensing for recognition and estimation tasks using deep learning and information bottleneck principles within OFDM systems.

Contribution

It proposes a novel SemS framework that shifts focus from reconstruction to semantic recognition, with a unified deep learning-based design for OFDM transceivers.

Findings

Semantic pilot design improves classification accuracy.

Semantic sensing enhances ranging precision.

Framework outperforms reconstruction-based baselines under resource constraints.

Abstract

Sensing and communication are fundamental enablers of next-generation networks. While communication technologies have advanced significantly, sensing remains limited to conventional parameter estimation and is far from fully explored. Motivated by these limitations, we propose semantic sensing (SemS), a novel framework that shifts the design objective from reconstruction fidelity to semantic effective recognition. Specifically, we mathematically formulate the interaction between transmit waveforms and semantic entities, thereby establishing SemS as a semantics-oriented transceiver design. Within this architecture, we leverage the information bottleneck (IB) principle as a theoretical criterion to derive a unified objective, guiding the sensing pipeline to maximize task-relevant information extraction. To practically solve this optimization problem, we develop a deep learning (DL)-based framework that jointly designs transmit waveform parameters and receiver representations. The framework is implemented in an orthogonal frequency division multiplexing (OFDM) system, featuring a shared semantic encoder that employs a Gumbel-Softmax-based pilot selector to discretely mask task-irrelevant resources. At the receiver, we design distinct decoding architectures tailored to specific sensing objectives, comprising a 2D residual network (ResNet)-based classifier for target recognition and a correlation-driven 1D regression network for high-precision delay estimation. Numerical results demonstrate that the proposed semantic pilot design achieves superior classification accuracy and ranging precision compared to reconstruction-based baselines, particularly under constrained resource budgets.