Exploring Hybrid Active-Passive RIS-Aided MEC Systems: From the Mode-Switching Perspective

Mobile edge computing (MEC) has been regarded as a promising technique to support latency-sensitivity 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 via jointly optimizing the transmission time, the transmit power, the receive beamforming vector, the phase-shift matrix, the mode-switching factor, the amplification factor, the offloading ratio factor, and the computation ability of the user, where the constraints of the maximum energy of users, the maximum power of the RIS, the minimum computation tasks, the transmission time, the offloading ratio factor, the mode-switching factor, the unit moduli of passive units, and the computation ability are taken into account. Considering the complexity of the aforementioned problem, we develop an alternating optimization-based iterative algorithm by combining the successive convex approximation method, the variable substitution, and the singular value decomposition 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.