Active STAR-RIS Empowered Edge System for Enhanced Energy Efficiency and Task Management

The proliferation of data-intensive, low-latency applications has driven the adoption of multi-access edge computing (MEC) to meet the demand for high-performance computing at the network edge. However, ensuring reliable communication under non-line-of-sight (NLoS) conditions remains a significant challenge. While reconfigurable intelligent surfaces (RISs) and the more recent simultaneously transmitting and reflecting RISs (STAR-RISs) offer promising solutions, their passive nature and susceptibility to multiplicative fading limit performance gains. To address these challenges, we propose a novel active STAR-RIS-assisted MEC system that enhances signal strength and adaptability by enabling amplification and joint control over signal transmission and reflection. Our objective is to minimize the energy consumption of user devices, considering both local task computation and uplink task offloading, while maintaining task queue stability. We formulate a joint energy minimization problem with system constraints and long-term queue stability requirements. This problem is decomposed into subproblems: (i) sequential fractional programming is applied to optimize user transmit power, (ii) convex optimization is used to determine partial task offloading ratios, and (iii) a modified Lyapunov optimization combined with double deep Q-networks (DDQN) is proposed to iteratively solve the active STAR-RIS parameters (amplitude and phase shift), amplification control, and task admission at the user side. Numerical results indicate that our proposed system outperforms the conventional passive STAR-RIS-assisted system by 18.64% and the conventional passive RIS-assisted system by 30.43%, respectively.