Multiobjective Optimization of AoI and Rate for Secure Dual-Functional RIS-Assisted Networks: A Cross-Layer Design

We propose a cross-layer optimization algorithm to minimize the average Age of Information (AoI) and at the same time maximizing the communication sum rate in a dual-functional reconfigurable intelligent surface (DF-RIS)-assisted Internet of Things (IoT) network, where the DF-RIS performs simultaneous information reflection and jamming to defend against an eavesdropper (Eve). Specifically, the transmission scheduling and resource allocation are adaptively co-designed to achieve secure low-latency communication in a more practical and detailed channel environment. For the case with perfect channel state information (CSI) of Eve and IoT devices, the transmission scheduling is transformed as a Markov decision process and optimized to find the optimal packet size by deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {Q}$ </tex-math></inline-formula>-network. The resource allocation algorithm is performed to optimize subcarrier channels, transmit power, the scheduling matrix of the DF-RIS, and discrete phase shifts of the DF-RIS based on the alternating optimization method. For the imperfect CSI leads to an infinite number of inequality constraints, a robust resource allocation algorithm based on the transformed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal{S}}$ </tex-math></inline-formula>-procedure is proposed, which can guarantee the security and low-latency of messages transmission. Simulation results show that the proposed algorithm can reduce the average AoI by at least 15.5% and increase communication sum rate by 9.2% than the existing baseline algorithms.