STAR-RIS Enabled ISAC Systems: Joint Rate Splitting and Beamforming Optimization

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for echo signals from a target of interest. Moreover, to manage interference and improve communication quality, the rate splitting multiple access (RSMA) scheme is incorporated into the system. The signal-to-interference-plus-noise ratio (SINR) of the received sensing echo signals is a measure of sensing performance. We formulate a joint optimization problem of common rates, transmit beamforming at the BS, and passive beamforming vectors of the STAR-RIS. The objective is to maximize sensing SINR while guaranteeing the communication rate requirements for each GT. We present an iterative algorithm to address the non-convex problem by invoking Dinkelbach's transform, semidefinite relaxation (SDR), majorization-minimization, and sequential rank-one constraint relaxation (SROCR) theories. Simulation results manifest that the performance of the studied ISAC network enhanced by the STAR-RIS and RSMA surpasses other benchmarks considerably. The results evidently indicate the superior performance improvement of the ISAC system with the proposed RSMA-based transmission strategy design and the dynamic optimization of both transmission and reflection beamforming at STAR-RIS.

Rethinking Collaborative Perception from the Spatial-Temporal Importance of Semantic Information

Collaboration by the sharing of semantic information is crucial to enable the enhancement of perception capabilities. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension in collaborator selection and semantic information fusion, which instigates performance degradation. In this article, we propose a novel collaborative perception framework, IoSI-CP, which takes into account the importance of semantic information (IoSI) from both temporal and spatial dimensions. Specifically, we develop an IoSI-based collaborator selection method that effectively identifies advantageous collaborators but excludes those that bring negative benefits. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates a multi-scale transformer module and a short-term attention module to capture IoSI from spatial and temporal dimensions, and assigns varying weights for efficient aggregation. Extensive experiments on two open datasets demonstrate that our proposed IoSI-CP significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/IoSI-CP/.