针对感知生成网络中内容准确率与质量的权衡，提出高效资源分配方法。
Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks
- 用线性规划引导深度强化学习，将三维资源分配降维为二维优化
- 相比纯强化学习方法，收敛更快且平均体验评分提升超10%
- 适合关注内容真实性和生成质量的AIGC系统设计者
融合感知与通信（ISAC）可提升人工智能生成内容（AIGC）网络的感知与传输效率。现有AIGC服务通常假设在输入数据和提示准确的前提下内容准确性可保障，仅关注生成质量（CGQ）。但在基于ISAC的AIGC网络中，生成依赖于不准确的感知数据，且生成误差随计算资源（生成步数）变化。为此，本文提出内容准确率与质量感知的服务评估指标（CAQA）。由于感知、计算与通信三者资源存在权衡，需优化以最大化所有用户平均CAQA（AvgCAQA），该问题为NP-hard，解空间随用户数呈指数增长。为此，提出一种线性规划（LP）引导的深度强化学习算法（LPDRL-F），结合动作过滤机制，将原三维解空间降维至二维，显著降低复杂度并提升学习性能。仿真表明，相比无LP的现有DRL与生成扩散模型（GDM）算法，LPDRL-F收敛更快，平均CAQA提升超10%；相较仅关注CGQ的现有方案，其平均CAQA提升超过50%。
原文摘要 · Abstract (English)
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.
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