Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2607.13160v1
• Date: Tue, 14 Jul 2026 18:10:14 GMT
• Title: Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach
• Abstract summary: Active beyond-diagonal intelligent surfaces (BDRISs) enable hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage.<n>This paper develops an end-to-end mobile and edge computing framework based on a refined version of the soft actor soft actor algorithm named as DSAC-T.<n>Compared with other baseline algorithms, DSAC-T the best energy-aware offloading reward, the highest feasibility ratio, and a fast online decision time 0.0267 s per scenario.
• Score: 33.021924217806756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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