Search arXivSearch

arXiv · 2605.03558

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

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Elaheh Ataeebojd, Nhan Thanh Nguyen, Seonghoon Yoo, Joonhyuk Kang, Markku Juntti, Matti Latva-aho, Mehdi Rasti. 2026-05-05. Resource Allocation and AoI-Aware Detection for ISAC with Stacked Intelligent Metasurfaces. https://arxiv.org/abs/2605.03558

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Beyond HBM-on-GPU: Thermal Design Envelope for 3D Volumetric DRAM-on-GPU Integration

The scaling of GPUs for AI and HPC workloads is increasingly constrained by the capacity, bandwidth, and thermal limits of both 2.5D HBM-GPU and direct-stacked 3D HBM-on-GPU integration. This work establishes the thermal design envelope for 3D volumetric DRAM-on-GPU integration, in which vertically oriented DRAM dies and interleaved cooling cavities reshape heat flow and memory interfacing above the GPU. Using a package-level thermal model anchored to a consistent HBM-on-GPU baseline and driven by a realistic reticle-scale non-uniform GPU power map, we quantify the key parameters governing thermal feasibility. Stack height is the dominant limiter of peak temperature, while cooling-cavity conductivity shifts the feasible region, and mold insertion and stack orientation further modulate thermal behavior. A distributed memory-controller and network-on-chip tier introduces only a moderate thermal penalty. Although die-level parallelism increases bandwidth, the reduction in simulated training time saturates once execution becomes compute-bound. These results define a bounded co-design space across bandwidth, capacity, and thermal constraints for 3D volumetric DRAM-on-GPU integration.

cs.ET

Droop-Aware Foundation Model Power Flow

This paper develops a droop-aware extension of the GridFM power systems foundation model, embedding droop gains and frequency/voltage deadband parameters as per-bus node features to enable control-aware AC power-flow analysis. Existing power-flow datasets encode only static electrical features, conflating operating points from qualitatively different control regimes; this work resolves that gap by exposing droop and deadband parameters as structured node features, with deadband discontinuities handled through a smooth tanh approximation that preserves solver differentiability. A transformer-based graph neural network is pre-trained on masked reconstruction and fine-tuned on the resulting control-aware datasets. The framework is validated against PSCAD electromagnetic-transient simulations on a two-bus system (0.11% maximum steady-state error) and cross-validated against an independent PyPower droop solver on the IEEE 24-bus RTS. On the 24-bus system the surrogate attains R2 = 0.9996 for active generation and 0.0015 p.u. voltage-magnitude RMSE; scalability is confirmed on the IEEE 300-bus system (0.0036 p.u. RMSE, R2 = 0.9841 for voltage magnitude across 299,700 predictions). A three-mode control study further shows that the deadband widens the control-error distribution while leaving total droop compensation unchanged, establishing deadband width as an actionable node-level design feature.

cs.ET

GNN-based Path-aware multi-view Circuit Learning for Technology Mapping

Traditional technology mapping suffers from systemic inaccuracies in delay estimation due to its reliance on abstract, technology-agnostic delay models that fail to capture the nuanced timing behavior behavior of real post-mapping circuits. To address this fundamental limitation, we introduce GPA(graph neural network (GNN)-based Path-Aware multi-view circuit learning), a novel GNN framework that learns precise, data-driven delay predictions by synergistically fusing three complementary views of circuit structure: And-Inverter Graphs (AIGs)-based functional encoding, post-mapping technology emphasizes critical timing paths. Trained exclusively on real cell delays extracted from critical paths of industrial-grade post-mapping netlists, GPA learns to classify cut delays with unprecedented accuracy, directly informing smarter mapping decisions. Evaluated on the 19 EPFL combinational benchmarks, GPA achieves 19.9%, 2.1% and 4.1% average delay reduction over the conventional heuristics methods (techmap, MCH) and the prior state-of-the-art ML-based approach SLAP, respectively-without compromising area efficiency.

cs.ET