发表机构
Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou)（物联网 thrust，香港科学与技术大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究互易主动BD-RIS辅助异构MEC的能量感知卸载与资源分配问题，基于分布式软演员-评论家算法改进版DSAC-T构建端到端联合优化框架，相比其他算法，实现了更好的能量-延迟奖励、可行性比率及更快在线决策时间
AI 中文摘要
主动式非对角可重构智能表面（BD-RIS）能实现混合发射与反射模式，为异构移动边缘计算（MEC）系统中的阻塞感知上行卸载提供方案。但实际混合模式主动BD-RIS由互易设备实现，会产生跨扇区能量泄漏。本文研究互易主动BD-RIS辅助异构MEC的能量感知卸载与资源分配，提出基于分布式软演员-评论家算法改进版DSAC-T的端到端联合优化框架，相比其他算法，DSAC-T实现了最佳能量-延迟奖励、最高可行性比率81.67%及快速在线决策时间。
英文摘要
Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems. However, practical hybrid mode active BD-RIS are realized by reciprocal devices, which inherently generate cross-sector energy leakage that will reshape the system-level energy-latency tradeoff. This paper studies energy-aware offloading and resource allocation for reciprocal active BD-RIS-assisted heterogeneous MEC, where offloading decisions, CPU/GPU computation allocation, transmit powers, receive processing, and active BD-RIS are tightly coupled. The resulting problem is a high-dimensional mixed integer nonconvex problem and is difficult to solve efficiently by conventional per-instance optimization. To address this challenge, we develop an end-to-end joint optimization framework based on a refined version of the distributional soft actor--critic algorithm, named as DSAC-T. By modeling return distributions rather than only expected values, DSAC-T improves policy stability under reward heterogeneity and feasibility-boundary sensitivity. Compared with other baseline algorithms, DSAC-T achieves the best energy-latency reward, the highest feasibility ratio of 81.67%, and a fast online decision time of 0.0267 s per scenario.
