In the paper, we investigate the coordination process of sensing and computation offloading in a reconfigurable intelligent surface (RIS)-aided base station (BS)-centric symbiotic radio (SR) systems. Specifically, the Internet-of-Things (IoT) devices first sense data from environment and then tackle the data locally or offload the data to BS for remote computing, while RISs are leveraged to enhance the quality of blocked channels and also act as IoT devices to transmit its sensed data. To explore the mechanism of cooperative sensing and computation offloading in this system, we aim at maximizing the total completed sensed bits of all users and RISs by jointly optimizing the time allocation parameter, the passive beamforming at each RIS, the transmit beamforming at BS, and the energy partition parameters for all users subject to the size of sensed data, energy supply and given time cycle. The formulated nonconvex problem is tightly coupled by the time allocation parameter and involves the mathematical expectations, which cannot be solved straightly. We use Monte Carlo and fractional programming methods to transform the nonconvex objective function and then propose an alternating optimization-based algorithm to find an approximate solution with guaranteed convergence. Numerical results show that the RIS-aided SR system outperforms other benchmarks in sensing. Furthermore, with the aid of RIS, the channel and system performance can be significantly improved.

翻译：本文研究了可重构智能表面（RIS）辅助的基站（BS）中心化共生无线电（SR）系统中感知与计算卸载的协调过程。具体而言，物联网（IoT）设备首先从环境中感知数据，随后在本地处理数据或将数据卸载至BS进行远程计算，同时RIS被用于提升阻塞信道质量并作为IoT设备传输其感知数据。为探索该系统中协同感知与计算卸载的机制，我们以最大化所有用户与RIS的总完成感知比特数为目标，在感知数据量、能量供给及给定时间周期的约束下，联合优化时间分配参数、各RIS被动波束成形、BS发射波束成形及所有用户的能量分配参数。所构建的非凸问题因时间分配参数的强耦合性而涉及数学期望，无法直接求解。我们采用蒙特卡洛方法与分式规划技术转换非凸目标函数，进而提出一种基于交替优化的算法，以获取具有收敛保证的近似解。数值结果表明，RIS辅助的SR系统在感知性能上优于其他基准方案。此外，借助RIS可显著提升信道与系统性能。