Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities

The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information importance to risk aversion, we model agent behavior using Cumulative Prospect Theory (CPT), which incorporates risk-sensitive utility functions and nonlinear transformations of distributions, reflecting subjective perceptions of gains and losses. The objective is to maximize the aggregate utility across multiple CPT-modeled agents, which leads to a nonconvex, nonsmooth optimization problem. To efficiently solve this challenging problem, we propose a new algorithmic framework that combines successive convex approximation (SCA) with the projected subgradient method and Lagrangian relaxation, Our approach enables tractable optimization while preserving solution quality, offering both theoretical rigor and practical effectiveness in semantics-aware resource allocation.

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents

We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky choices under uncertainty, such as risk perception, loss aversion, and perceptual distortions in probability metrics. By identifying essential modifications in traditional resource allocation design principles required for agents with CPT preferences, we showcase the framework's relevance through its application to the problem of power allocation in multi-channel wireless communication systems.