Toward Robust Semantic Communications: Proactive Importance-Ordered Restructuring for Enhanced Unequal Error Protection

Abstract

Semantic communications (SemCom) is a promising task-oriented paradigm in which semantic features exhibit non-uniform importance. Consequently, unequal error protection (UEP), which allocates resources based on semantic importance, plays a pivotal role in maximizing system utility. However, most existing schemes adopt passive importance evaluation, which neither proactively reshapes the importance distribution nor explores its impact on UEP performance. In this paper, we propose a novel importance-ordered semantic feature restructuring (ISFR) scheme that proactively enforces a descending importance hierarchy and jointly optimizes multi-dimensional resources to improve system utility. Specifically, modules with decreasing retention probabilities and increasing distortion levels are employed, which drive the model to concentrate key semantics into front-end features and thus strengthen importance differentiation. Moreover, a joint optimization problem that jointly optimizes channel matching, feature selection, modulation schemes, and power allocation is formulated to minimize the importance-weighted total semantic distortion. To solve this non-convex problem, a hierarchical decoupling strategy is proposed, which decomposes it into four tractable subproblems. This approach leverages the ordered prior to drastically prune the search space for feature selection and modulation, while integrating greedy-based channel matching and convex power allocation. Simulation results demonstrate that the proposed ISFR scheme outperforms traditional uniform importance-based schemes under harsh channel conditions and limited resources, validating the significant robustness improvement enabled by the concentration of key semantic information.

Figures (16)

• Figure 1: System model of the proposed ISFR and SI-UEP schemes. During training, ISFR induces a feature distribution with descending semantic importance via the BER matching and nested dropout modules, and the corresponding importance weights are evaluated and stored as prior. During inference, SI-UEP is performed via multi-dimensional resource allocation driven by importance and CSI.
• Figure 2: System model of the BER matching module and nested dropout module for ISFR.
• Figure 3: Superposition in ISFR via feature accumulation.
• Figure 4: Orderliness in ISFR via feature-wise masking.
• Figure 5: Block diagram of the proposed OPHD algorithm for SI-UEP.