Semantic Sensing: Toward a Task-Oriented Paradigm
Sensing and communication are fundamental enablers of next-generation networks. While communication technologies have advanced significantly, the potential of sensing beyond conventional parameter estimation remains far from fully explored. To address these limitations, we propose semantic sensing (SemS), a framework that extends task-oriented sensing through explicit modeling and recovery of task-relevant environmental information. Specifically, we mathematically formulate how transmit waveforms and receiver processing jointly support the recovery of task-relevant semantics, thereby establishing SemS as a semantics-oriented transceiver design. Within this architecture, we formulate an information-theoretic objective that guides the sensing pipeline to maximize task-relevant information extraction under resource constraints. To 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 semantic encoder that employs differentiable pilot selection to select informative time--frequency resources. At the receiver, we design a shared semantic decoder that estimates three semantic attributes, namely object category, proximity, and radial motion, to support target classification and approach warning through predefined task mappings. We also consider continuous target range estimation as a special case in which the semantic state and task output coincide. Numerical results demonstrate that the proposed semantic pilot design achieves superior classification accuracy and approach-warning Macro-F1 compared to reconstruction-based baselines, particularly under constrained resource budgets.