OFDM-Based Active STAR-RIS-Aided Integrated Sensing and Communication Systems

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which consists of numerous passive elements, has recently emerged in wireless communication systems as a promising technology providing 360$^\circ$ coverage and better performance. In our research, we introduce an active STAR-RIS (ASTARS)-aided integrated sensing and communications (ISAC) system designed to optimize the radar signal-to-noise ratio (SNR), enhancing detection and signal transmission efficiency. The introduction of an ISAC system aims to improve both communication efficiency and sensing capabilities. Also, we employ orthogonal frequency division multiplexing (OFDM) to address the frequency-selective fading problem. Furthermore, we evaluate the radar sensing capabilities by examining the range and velocity, and assess the performance through the mean-squared error (MSE) of their estimations. Our simulation results demonstrate that ASTARS outperforms STAR-RIS in our system configurations, and that the proposed optimization approach further enhances the system performance. Additionally, we confirm that an increase in the subcarrier spacing can reduce the transmission bit error rate (BER) under high-velocity conditions.

Two-Timescale Design for Active STAR-RIS Aided Massive MIMO Systems

Simultaneously transmitting and reflecting \textcolor{black}{reconfigurable intelligent surface} (STAR-RIS) is a promising implementation of RIS-assisted systems that enables full-space coverage. However, STAR-RIS as well as conventional RIS suffer from the double-fading effect. Thus, in this paper, we propose the marriage of active RIS and STAR-RIS, denoted as ASTARS for massive multiple-input multiple-output (mMIMO) systems, and we focus on the energy splitting (ES) and mode switching (MS) protocols. Compared to prior literature, we consider the impact of correlated fading, and we rely our analysis on the two timescale protocol, being dependent on statistical channel state information (CSI). On this ground, we propose a channel estimation method for ASTARS with reduced overhead that accounts for its architecture. Next, we derive a \textcolor{black}{closed-form expression} for the achievable sum-rate for both types of users in the transmission and reflection regions in a unified approach with significant practical advantages such as reduced complexity and overhead, which result in a lower number of required iterations for convergence compared to an alternating optimization (AO) approach. Notably, we maximize simultaneously the amplitudes, the phase shifts, and the active amplifying coefficients of the ASTARS by applying the projected gradient ascent method (PGAM). Remarkably, the proposed optimization can be executed at every several coherence intervals that reduces the processing burden considerably. Simulations corroborate the analytical results, provide insight into the effects of fundamental variables on the sum achievable SE, and present the superiority of 16 ASTARS compared to passive STAR-RIS for a practical number of surface elements.