Traditional communication resource allocation optimizes operational metrics such as rate, latency, energy efficiency, and reliability. Motivated by goal-oriented semantic communication, we develop a generalized allocation framework that transforms operational rewards through reference-dependent user utilities and evaluates them via expected utility, cumulative prospect theory (CPT), or related decision criteria. The formulation separates operational rewards, user-side valuations, and system-level decision criteria under uncertainty. We characterize utilities incorporating QoS/service reference levels, deficit sensitivity, post-reference saturation, and loss aversion. Weighted sum-reward maximization, proportional fairness, $α$-fairness, and stochastic expected utility maximization emerge as exact special cases, while soft QoS-aware allocation follows from a deficit-penalty generalization. For deterministic orthogonal systems, we derive utility-weighted marginal-reward KKT conditions characterizing global optimality under concave composite objectives. For nonorthogonal and stochastic systems, we develop successive convex approximation (SCA) algorithms and establish stationary-point guarantees under explicit smooth and composite nonsmooth assumptions, including a CPT branch-stationarity result under eventual nondegenerate ordering identification. For globally solved penalized problems, we derive an $O(1/λ)$ bound on excess aggregate soft-QoS deficit, and on the deficit itself whenever a QoS-feasible allocation exists. Additionally, we establish an $O(1/ε^2)$ outer iteration bound for the smooth fixed-step SCA best-response residual. Numerical examples demonstrate how reference points, marginal utility slopes, channel quality, and resource budgets influence active users and allocation policies.

翻译：暂无翻译