arXiv subjects

Shuying Gan

Publications and source records attributed to Shuying Gan.

6 recordsLinked to original sources

arXiv2026

Partially Observable Restless Bandits for Age-Optimal Scheduling over Markov Channels

There is a surge of need for fresh information with the overwhelming proliferation of the Internet of Things (IoT) applications. To characterize the information freshness perceived by the destination, the age of information (AoI) has been proposed. In this paper, we consider an IoT system with multiple devices sending status update packets to a central controller through time-correlated Markov channels and assume that the instantaneous channel states are not available to the central controller before making scheduling decisions. To ensure information freshness, we investigate a timely scheduling problem that minimizes the total expected time-average AoI under a strict communications bandwidth constraint. We formulate this problem as a partially observable restless multi-armed bandit problem. Using Lagrangian relaxation, we decouple the relaxed problem into multiple sub-problems and prove the threshold structure of their optimal policies. Armed with this property, we establish the indexability for the decoupled problem and design an algorithm to compute the Whittle's index. To reduce implementation complexity, we further derive the Whittle-like index in closed-form for low-complexity scheduling. Simulation results show that the proposed index-based policies outperform the baselines, remain close to the optimal policy or relaxed lower bound, and are especially effective when scheduling resources are limited or the network size is large.

cs.IT↗

arXiv2025

Minimizing Task-Oriented Age of Information for Remote Monitoring with Pre-Identification

The emergence of new intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. We apply TAoI to a wireless monitoring system tasked with identifying targets and transmitting their images for subsequent analysis. To minimize TAoI and determine the optimal transmission policy, we formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP). Our analysis demonstrates that the optimal policy is threshold-based with respect to TAoI. Building on this, we propose a low-complexity relative value iteration algorithm tailored to this threshold structure to derive the optimal transmission policy. Additionally, we introduce a simpler single-threshold policy, which, despite a slight performance degradation, offers faster convergence. Comprehensive experiments and simulations validate the superior performance of our optimal transmission policy compared to two established baseline approaches.

cs.IT↗

arXiv2025

Task-oriented Age of Information for Remote Inference with Hybrid Language Models

Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) through their advanced reasoning capabilities, but their extensive parameter sets introduce significant inference latency, posing a challenge to ensure the timeliness of inference results. While Small Language Models (SLMs) offer faster inference speeds with fewer parameters, they often compromise accuracy on complex tasks. This study proposes a novel remote inference system comprising a user, a sensor, and an edge server that integrates both model types alongside a decision maker. The system dynamically determines the resolution of images transmitted by the sensor and routes inference tasks to either an SLM or LLM to optimize performance. The key objective is to minimize the Task-oriented Age of Information (TAoI) by jointly considering the accuracy and timeliness of the inference task. Due to the non-uniform transmission time and inference time, we formulate this problem as a Semi-Markov Decision Process (SMDP). By converting the SMDP to an equivalent Markov decision process, we prove that the optimal control policy follows a threshold-based structure. We further develop a relative policy iteration algorithm leveraging this threshold property. Simulation results demonstrate that our proposed optimal policy significantly outperforms baseline approaches in managing the accuracy-timeliness trade-off.

cs.IT↗

arXiv2025

Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation

The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has driven the development of robust, resource-efficient frameworks. This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture. Our framework optimizes the transmission of meaningful information by incorporating a multi-task-aware scoring mechanism that identifies and prioritizes semantically significant data across multiple concurrent tasks. A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions. By jointly optimizing semantic relevance and transmission efficiency, the framework ensures minimal performance degradation under resource constraints. Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection. These results underscore the framework's adaptability to heterogeneous channel environments and its scalability for multi-task applications, positioning it as a promising solution for next-generation semantic communication networks.

cs.CV↗

arXiv2024

Task-oriented Age of Information for Remote Monitoring Systems

The emergence of intelligent applications has fostered the development of a task-oriented communication paradigm, where a comprehensive, universal, and practical metric is crucial for unleashing the potential of this paradigm. To this end, we introduce an innovative metric, the Task-oriented Age of Information (TAoI), to measure whether the content of information is relevant to the system task, thereby assisting the system in efficiently completing designated tasks. Also, we study the TAoI in a remote monitoring system, whose task is to identify target images and transmit them for subsequent analysis. We formulate the dynamic transmission problem as a Semi-Markov Decision Process (SMDP) and transform it into an equivalent Markov Decision Process (MDP) to minimize TAoI and find the optimal transmission policy. Furthermore, we demonstrate that the optimal strategy is a threshold-based policy regarding TAoI and propose a relative value iteration algorithm based on the threshold structure to obtain the optimal transmission policy. Finally, simulation results show the superior performance of the optimal transmission policy compared to the baseline policies.

cs.NI↗

arXiv2023

Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading

Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications. However, attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns, thereby incurring the pattern privacy (PP) issue. Therefore, we propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving PP. Firstly, we formulate the dynamic computation offloading procedure as a Markov decision process (MDP). Next, we develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this problem while addressing the PP issue by injecting noise into the generated offloading decisions. This is achieved by modifying the deep Q-network (DQN) with a Function-output Gaussian process mechanism. We provide a theoretical privacy guarantee and a utility guarantee (learning error bound) for the DP-DQO algorithm and finally, conduct simulations to evaluate the performance of our proposed algorithm by comparing it with greedy and DQN-based algorithms.

cs.NI↗