Enhancing Spectrum Sensing via Reconfigurable Intelligent Surfaces: Passive or Active Sensing and How Many Reflecting Elements Are Needed?

Cognitive radio has been suggested as a solution to address the shortage of accessible spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. Nevertheless, the sensing performance is rather inadequate owing to the diminished sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, applying reconfigurable intelligent surfaces (RIS) for spectrum sensing can efficiently address the aforementioned problems. However, the passive RIS may experience the “double fading” effect, seriously limiting the effectiveness of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. In this paper, we utilize the passive and active RIS to further enhance detection probability and subsequently develop two different problems for both the passive and active RIS to achieve the detection probability maximization. Considering the complexity of the above problems, we design a one-stage optimization algorithm featuring inner approximation and a two-stage optimization algorithm that employs the bisection method to derive corresponding solutions, and further establish the upper bound and lower bound of the detection probability by employing the Rayleigh quotient. Moreover, we separately explore how many reflecting elements are needed for passive RIS and active RIS and investigate the detection performance comparison of the two types (passive and active) of RIS. Simulation results show that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes.

I. Introduction

The modern digital society is currently experiencing a constant surge in wireless traffic, which may lead to the fragmentation of spectrum resources and further aggravate the scarcity of available spectrum [1], [2], [3]. Cognitive radio is a revolutionary wireless communication technology aiming at improving spectrum resource utilization [4]. This is different from the traditional static spectrum allocation schemes, which have demonstrated poor performance in available spectrum utilization. In cognitive networks, secondary users (SUs) are able to share the spectrum resources of the primary network via the spectrum underlay mode or the spectrum overlay mode [5]. For the spectrum underlay mode, primary users (PUs) can share the spectrum with SUs simultaneously while the interference caused by the secondary network to the primary network must be maintained at an interference temperature level [6]. However, in unfavorable wireless transmission environments, the spectrum underlay mode may cause higher interference to the PUs such that their quality of service demands may not be guaranteed. Moreover, since the secondary network needs to restrict the interference power, it leads to severe performance degradation of the secondary network. For the spectrum overlay mode, cognitive radio possesses the capability to continuously monitor and analyze the surrounding spectrum environment in real-time by applying spectrum sensing technologies [7]. By collecting and analyzing wireless signals, cognitive radio can detect the spectrum occupied by PUs and identify the idle spectrum. Once cognitive radio detects that a frequency band is in an idle state, secondary networks can autonomously utilize that band for communication, thus avoiding interference to PUs, which can effectively alleviate spectrum scarcity for 5G wireless communication networks.