Mobile edge computing (MEC) has been regarded as a promising technique to support latencysensitivity and computation-intensive serves. However, the low offloading rate caused by the random channel fading characteristic becomes a major bottleneck in restricting the performance of the MEC. Fortunately, reconfigurable intelligent surface (RIS) can alleviate this problem since it can boost both the spectrum- and energy- efficiency. Different from the existing works adopting either fully active or fully passive RIS, we propose a novel hybrid RIS in which reflecting units can flexibly switch between active and passive modes. To achieve a tradeoff between the latency and energy consumption, an optimization problem is formulated by minimizing the total cost. In light of the intractability of the problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition (SVD) to obtain sub-optimal solutions. Furthermore, in order to gain more insight into the problem, we consider two special cases involving a latency minimization problem and an energy consumption minimization problem, and respectively analyze the tradeoff between the number of active and passive units. Simulation results verify that the proposed algorithm can achieve flexible mode switching and significantly outperforms existing algorithms.

翻译：移动边缘计算（MEC）被视为支持对延迟敏感和计算密集型服务的有前景技术。然而，由随机信道衰落特性导致的低卸载速率成为制约MEC性能的主要瓶颈。幸运的是，可重构智能表面（RIS）能够缓解这一问题，因为它可以同时提升频谱效率和能量效率。与现有采用全主动或全被动RIS的工作不同，我们提出了一种新型混合RIS，其中反射单元可在主动与被动模式间灵活切换。为在延迟与能耗之间取得权衡，我们通过最小化总成本构建了一个优化问题。鉴于该问题的难解性，我们开发了一种基于交替优化的迭代算法，结合逐次凸近似方法、变量替换和奇异值分解（SVD）以获得次优解。此外，为更深入理解该问题，我们考虑了延迟最小化和能耗最小化这两个特例，并分别分析了主动与被动单元数量之间的平衡。仿真结果验证了所提算法能够实现灵活的模式切换，并显著优于现有算法。