Smart Mirrors for Signals: Balancing Energy and Speed express
Radio waves from a smartphone, traveling at the speed of light, easily reflect off walls and corners, losing strength. Smart surfaces are programmable mirrors: they catch the scattered signal and redirect it precisely to its target, like a deft magician making a beam bypass obstacles. But each such mirror suffers from parasitic reflections: some energy goes astray, creating interference, like glare in a photograph.
The DSAC-T algorithm acts like an expert tuner, adjusting the angles of thousands of microscopic reflectors in a fraction of a second. It accounts not only for direct gain but also for entropy – the spread of possible errors – minimizing the risk of failure. Instead of absorbing the signal like a black hole, the mirror focuses it precisely, and the decision comes in 0.027 seconds – faster than a blink. This approach cuts delays and energy waste, turning scattered glimmers into a cohesive stream of data.
Original abstract
as written by the authors arXiv:2607.13160
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.
A mirror surface contains hundreds of tiny antennas. Each can delay a wave by a fraction of a nanosecond, and together they form a directional beam, much like how many pixels on a display assemble into a sharp picture.