In opportunistic cognitive radio networks, when the primary signal is very weak compared to the background noise, the secondary user requires long sensing time to achieve a reliable spectrum sensing performance, leading to little remaining time for the secondary transmission. To tackle this issue, we propose an active reconfigurable intelligent surface (RIS) assisted spectrum sensing system, where the received signal strength from the interested primary user can be enhanced and underlying interference within the background noise can be mitigated as well. In comparison with the passive RIS, the active RIS can not only adapt the phase shift of each reflecting element but also amplify the incident signals. Notably, we study the reflecting coefficient matrix (RCM) optimization problem to improve the detection probability given a maximum tolerable false alarm probability and limited sensing time. Then, we show that the formulated problem can be equivalently transformed to a weighted mean square error minimization problem using the principle of the well-known weighted minimum mean square error (WMMSE) algorithm, and an iterative optimization approach is proposed to obtain the optimal RCM. In addition, to fairly compare passive RIS and active RIS, we study the required power budget of the RIS to achieve a target detection probability under a special case where the direct links are neglected and the RIS-related channels are line-of-sight. Via extensive simulations, the effectiveness of the WMMSE-based RCM optimization approach is demonstrated. Furthermore, the results reveal that the active RIS can outperform the passive RIS when the underlying interference within the background noise is relatively weak, whereas the passive RIS performs better in strong interference scenarios because the same power budget can support a vast number of passive reflecting elements for interference mitigation.

翻译：在机会式认知无线电网络中，当主用户信号相较于背景噪声非常微弱时，次用户需要较长的感知时间才能实现可靠的频谱感知性能，导致剩余传输时间不足。针对这一问题，我们提出了一种基于主动可重构智能表面（RIS）的频谱感知系统，该系统既可增强目标主用户的接收信号强度，同时也可抑制背景噪声中的底层干扰。与被动RIS相比，主动RIS不仅能调整每个反射单元的相位偏移，还能放大入射信号。值得注意的是，我们研究了反射系数矩阵（RCM）优化问题，以在给定最大可容忍虚警概率和有限感知时间条件下提高检测概率。随后，我们证明该问题可基于加权最小均方误差（WMMSE）算法原理等效转化为加权均方误差最小化问题，并提出了一种迭代优化方法以获取最优RCM。此外，为公平比较被动RIS与主动RIS，我们研究了在忽略直连链路且RIS相关信道为视距信道这一特殊场景下，使RIS达到目标检测概率所需功耗预算。通过大量仿真验证了基于WMMSE的RCM优化方法的有效性。实验结果进一步表明：当背景噪声中底层干扰相对较弱时，主动RIS性能优于被动RIS；而在强干扰场景下，被动RIS表现更优，因为相同功耗预算可支持更多被动反射单元用于干扰抑制。