Resource Allocation and AoI-Aware Detection for ISAC with Stacked Intelligent Metasurfaces

2026-05-05

AI Summary

This work addresses energy efficiency enhancement in a downlink integrated sensing and communication (ISAC) system assisted by stacked intelligent reflecting surfaces (IRSs), while simultaneously satisfying heterogeneous quality-of-service (QoS) requirements of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and radar sensing. The authors propose a novel two-timescale optimization framework that leverages a puncturing mechanism to decompose the original problem into slot-level eMBB subproblems and mini-slot-level URLLC and sensing subproblems. Joint optimization of resource block allocation, transmit power, and IRS phase shifts is performed via an iterative algorithm that transforms the non-convex problem into tractable convex subproblems, enabling low-complexity solutions. Compared to a baseline without IRS, the proposed scheme achieves up to a 230% improvement in energy efficiency while significantly reducing the required number of base station antennas and effectively guaranteeing both communication and sensing performance, thereby revealing a fundamental trade-off between energy efficiency and heterogeneous QoS demands.

Abstract

Stacked intelligent metasurfaces (SIMs) provide wave-domain degrees of freedom that can empower integrated sensing and communication (ISAC) through flexible beampattern synthesis and interference management, while reducing hardware cost. In this paper, we investigate energy-efficient resource allocation for a downlink SIM-aided multi-user ISAC system that supports the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) via puncturing, while simultaneously illuminating sensing targets. We formulate an energy efficiency (EE) maximization problem that jointly optimizes resource block (RB) allocation, transmit power control, and SIM phase shifts. The formulated problem is highly challenging due to the large number of variables optimized on different time scales. To overcome this, we leverage the intrinsic two-timescale structure induced by the puncturing approach to decompose the original problem into two tractable subproblems: EE maximization for eMBB users in each time slot and EE maximization for URLLC users and sensing targets in each mini-slot. To address each subproblem, we develop an iterative algorithm that transforms the original non-convex formulation into a sequence of tractable subproblems, yielding convex updates for RB allocation and power control, along with low-complexity updates for SIM phase shifts. Simulation results show that the proposed design achieves up to 230% improvement in EE over a No-SIM baseline. In addition, it requires significantly fewer transmit antennas than conventional BS architectures, while preserving the EE achieved and satisfying the communication and sensing quality of service (QoS) requirements. Moreover, the results reveal fundamental trade-offs between EE and heterogeneous QoS requirements across communication and sensing functionalities.

Problem

• Resource Allocation
• Age of Information (AoI)
• Intelligent Metasurfaces
• Integrated Sensing and Communication (ISAC)
• Energy Efficiency

Innovation

• Stacked Intelligent Metasurfaces
• Integrated Sensing and Communication
• Two-Timescale Optimization
• Energy Efficiency Maximization
• AoI-Aware Resource Allocation