Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data and prompt, thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). To assess the quality of experience of ISAC-based AIGC services, we propose a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with the number of users. To solve the CAQA-AIGC problem with low complexity, a linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model (GDM) algorithms without LP, LPDRL-F converges faster and finds better resource allocation solutions, improving AvgCAQA by more than 10%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.

翻译：感知通信一体化（ISAC）可通过提供高效感知与传输能力增强人工智能生成内容（AIGC）网络。现有AIGC服务通常假设在输入数据与提示词准确的前提下可确保生成内容的准确性，因而仅关注内容生成质量（CGQ）。然而，该假设不适用于基于ISAC的AIGC网络，因其内容生成依赖于不精确的感知数据。此外，AIGC模型本身会引入取决于生成步数（即计算资源）的生成误差。为评估基于ISAC的AIGC服务的体验质量，本文提出内容准确性与质量感知的服务评估指标（CAQA）。由于分配更多资源至感知与生成环节虽能提升内容准确性，却可能降低通信质量，反之亦然，必须通过优化感知-生成（计算）-通信三维资源权衡，使所有AIGC用户的平均CAQA（AvgCAQA）最大化（CAQA-AIGC问题）。该问题属于NP难问题，其解空间随用户数量呈指数级增长。为低复杂度求解CAQA-AIGC问题，本文提出一种带动作滤波器的线性规划（LP）引导深度强化学习（DRL）算法（LPDRL-F）。通过LP引导机制与动作滤波器，LPDRL-F能将原始三维解空间降至二维，在降低复杂度的同时提升DRL的学习性能。仿真结果表明：相较于现有未采用LP的DRL与生成扩散模型（GDM）算法，LPDRL-F收敛速度更快且能获得更优资源分配方案，使AvgCAQA提升超10%。采用LPDRL-F后，CAQA-AIGC相较于仅关注CGQ的现有方案可实现AvgCAQA超50%的提升。