Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks. However, their interplay has yet to be explored. To obtain the mutual benefits between CR and ISAC, we focus on a reconfigurable intelligent surface (RIS)-enhanced cognitive ISAC system and explore using the additional degrees-of-freedom (DoFs) brought by the RIS to improve the performance of the cognitive ISAC system. Specifically, we mathematically prove that the position error bound (PEB) of each mobile sensor (MS) decreases with the increasing signal-to-noise ratio (SNR) of the received signals at each MS. We also formulate an optimization problem of maximizing the signal-to-noise-plus-interference ratios (SINRs) of the MSs while ensuring the requirements of the spectrum sensing (SS) and the secondary transmissions by jointly designing the SS time, the secondary base station (SBS) beamforming, and the RIS beamforming. The formulated non-convex problem can be solved by the proposed block coordinate descent (BCD) algorithm based on the Dinkelbach’s transform and the successive convex approximation (SCA) methods. Simulation results demonstrate that all the proposed iterative algorithms converge fast, and the SINRs of MSs can be effectively enhanced by increasing the transmit power of the SBS, the number of MS antennas, and the number of RIS elements. Moreover, higher MS SINRs lead to lower PEBs of MSs, thereby having the potential to improve the accuracy of radio environment map (REM) for CR networks. Additionally, the RIS needs to be deployed near the SBS or MSs to guarantee the performance gain brought by the RIS.

Integrated sensing and communications (ISAC) is an emerging technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited number of degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RISs) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.

Intelligent reflection surface (IRS) has been identified as a promising technology for improving the performance of wireless communication systems. This paper focuses on the downlink scenario of an IRS-enhanced multi-user multiple-input single-output system. However, the channel state information (CSI) of the channels associated with IRS is difficult to estimate due to the passive feature of IRS. Meanwhile, the ideal continuous phase shifts of IRS elements are challenging to obtain owing to the hardware imperfection. This paper establishes an optimization model to minimize the base station (BS) transmit power subject to the discrete phase shifts of IRS and the weighted sumrate outage probability, which takes into account the practical statistical CSI error. We first apply the decomposition-based large deviation inequality to safely approximate the weighted sum-rate outage probability since the closed form expression of outage probability is hard to obtain. Then, the alternating optimization framework is introduced to iteratively solve the joint BS and IRS beamforming. In particular, the negative square penalty method is used to solve the non-convex sub-problem, i.e., the IRS passive beamforming. Numerical results demonstrate the effectiveness of the proposed algorithm.