arXiv subjects

Sunjung Kang

Publications and source records attributed to Sunjung Kang.

4 recordsLinked to original sources

arXiv2026

Optimal Multi-RIS Placement: Coverage-Guaranteed Sum Rate Maximization Under Inhomogeneous User Distributions

Reconfigurable Intelligent Surface (RIS) has emerged as a promising next-generation technology that improves the throughput and coverage of a wireless system. The realization of the full potential of RISs in a wireless system is tied to their strategic spatial deployment. While existing literature on RIS placement primarily focuses on maximizing coverage, when multiple RIS placements guarantee the required coverage (happens quite often), these approaches fail to exploit prior user trends to choose the one that is most probable to maximize throughput. Thus, to enable throughput maximization while guaranteeing fairness, we formulate a novel hierarchical problem that maximizes the expected sum rate of the system while guaranteeing a certain probabilistic coverage, with the requisite minimum number of RISs deployed. To solve this multi-layered non-convex problem, firstly, we obtain a set of optimal points where we can deploy RISs to provide the coverage guarantee. Then, the least number of RISs that can guarantee the required coverage is obtained by a greedy minimum partitioning. Finally, a Bayesian optimization based approach is used to compute the optimal RIS placement. Numerical results are provided to show that the proposed framework consistently identifies placements that jointly achieve good coverage and throughput, without impractical assumptions.

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arXiv2026

Exploring Performance Tradeoffs in Age-Aware Remote Monitoring with Satellites

We investigate a remote monitoring framework with multiple sensing modalities including IoT sensors on the ground, mobile UAVs in the air, and a periodically available satellite constellation. While the IoT sensors cover small areas and remain fixed, the UAVs can move between locations and cover larger areas, and the satellites can observe the entire region but have high latency and low reliability. We divide the deployment region into cells and model it as a graph, with the nodes representing individual cells and edges representing possible UAV mobility patterns. To evaluate the freshness of collected information from this graph, we adopt the Age of Information (AoI) metric, measured separately for each cell. Under a given deployment of IoT nodes and UAV mobility patterns, our objective is to ascertain whether the system should actually utilize monitoring updates from satellites - a seemingly simple yet surprisingly elusive question. For stationary randomized scheduling policies, we develop closed-form expressions and lower bounds for the weighted-sum AoI and utilize this analysis to explore performance tradeoffs as system parameters vary. We also provide a Lyapunov style max-weight policy and detailed simulations that provide crucial insights for deploying such systems in practice.

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arXiv2025

Timely Trajectory Reconstruction in Finite Buffer Remote Tracking Systems

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance and healthcare. In such systems, maintaining both real-time state awareness (for online decision making) and accurate reconstruction of historical trajectories (for offline post-processing) are essential. While the Age of Information (AoI) metric has been extensively studied as a measure of freshness, it does not capture the accuracy with which past trajectories can be reconstructed. In this work, we investigate reconstruction error as a complementary metric to AoI, addressing the trade-off between timely updates and historical accuracy. Specifically, we consider three policies, each prioritizing different aspects of information management: Keep-Old, Keep-Fresh, and our proposed Inter-arrival-Aware dropping policy. We compare these policies in terms of impact on both AoI and reconstruction error in a remote tracking system with a finite buffer. Through theoretical analysis and numerical simulations of queueing behavior, we demonstrate that while the Keep-Fresh policy minimizes AoI, it does not necessarily minimize reconstruction error. In contrast, our proposed Inter-arrival-Aware dropping policy dynamically adjusts packet retention decisions based on generation times, achieving a balance between AoI and reconstruction error. Our results provide key insights into the design of efficient buffer management policies for resource-constrained IoT networks.

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arXiv2023

A Learning-based Distributed Algorithm for Scheduling in Multi-hop Wireless Networks

We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

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