Reconfigurable intelligent surface (RIS) has been regarded as a promising technique due to its high array gain and low power. However, the traditional passive RIS suffers from the ``double fading'' effect, which has restricted the performance of passive RIS-aided communications. Fortunately, active RIS can alleviate this problem since it can adjust the phase shift and amplify the received signal simultaneously. Nevertheless, a high beamforming gain often requires a number of reflecting elements, which leads to non-negligible power consumption, especially for the active RIS. Thus, one challenge is how to improve the scalability of the RIS and the energy efficiency. Different from the existing works where all reflecting elements are activated, we propose a novel element on-off mechanism where reflecting elements can be flexibly activated and deactivated. Two different optimization problems for passive RIS and active RIS are formulated by maximizing the total energy efficiency. We develop two different alternating optimization-based iterative algorithms to obtain sub-optimal solutions. Furthermore, we consider special cases involving rate maximization problems for given the same total power budget, and respectively analyze the number configuration for passive RIS and active RIS. Simulation results verify that reflecting elements under the proposed algorithms can be flexibly activated and deactivated.

翻译：可重构智能表面因其高阵列增益和低功耗而被视为一项有前景的技术。然而，传统无源RIS存在“双衰落”效应，限制了无源RIS辅助通信的性能。幸运的是，有源RIS可以同时调整相位偏移并放大接收信号，从而缓解此问题。尽管如此，高波束赋形增益通常需要大量反射单元，这会导致不可忽视的功耗，尤其对于有源RIS而言。因此，如何提升RIS的可扩展性和能量效率成为一项挑战。不同于现有工作中激活所有反射单元，我们提出一种新颖的单元通断机制，使反射单元能够灵活地激活和去激活。通过最大化总能量效率，建立了无源RIS和有源RIS的两个不同优化问题。我们开发了两种基于交替优化的迭代算法以获得次优解。此外，我们考虑了在相同总功率预算下涉及速率最大化问题的特殊情况，并分别分析了无源RIS和有源RIS的数量配置。仿真结果验证了所提算法下的反射单元能够灵活地激活和去激活。