Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system's decision-making utility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-making utility. Leveraging the GoT, we consider a sampler & decision-maker pair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent the curse of dimensionality in obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposed sampler & decision-maker co-design surpasses the current literature on AoI and its variants in terms of both goal achievement utility and sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.
In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around data's role and its usefulness to the communication's goal. First, we shed light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we highlight the shortcomings of several proposed performance measures destined to deal with the traditional communication paradigm's limitations, namely the Age of Information (AoI) and the error-based metri…
Generative AI Agent Empowered Power Allocation for HAP Propulsion and Communication Systems
Beihang University（北京航空航天大学）；Pengcheng Laboratory（鹏城实验室）；Queen’s University Belfast（贝尔法斯特女王大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Kyung Hee University（庆熙大学）
Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the Semantics of Information, i.e., the significance and the usefulness of messages with r…
Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
Zhejiang University（浙江大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；King’s College London（伦敦国王学院）；Department of Engineering, King’s College London（伦敦国王学院工程系）；University of Miami（迈阿密大学）；Institute for Data Science and Computing, University of Miami（迈阿密大学数据科学与计算研究所）；The University of Hong Kong（香港大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子电气工程系）
Generative AI-Empowered Semantic Twin Channel Model for ISAC
School of Information and Communications Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；Terahertz Wireless Communications Laboratory, Shanghai Jiao Tong University（上海交通大学太赫兹无线通信实验室）
Towards World Model-Empowered Integrated Sensing, Communication, and Decision for Complex Unmanned Systems
School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University（上海交通大学集成电路学院（信息与电子工程学院））；Pervasive Communication Research Center, Purple Mountain Laboratories（紫金山实验室泛在通信研究中心）；Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Purple Mountain Laboratories（紫金山实验室）
We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this probl…
Virginia Tech（弗吉尼亚理工大学）；University of Maryland（马里兰大学）；The Ohio State University（俄亥俄州立大学）
This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair that is jointly trained while considering channel effects. The encoder converts data samples into feature vectors of small dimension and transmits them with a small number of channel uses thereby reducing the number of transmissions and latency. Instead of reconstructing input samples, the decoder performs a task, e.g., classification, on the received signals. Applying dif…
University of Luxembourg（卢森堡大学）；Centre for Security Reliability and Trust（安全、可靠性与信任中心）
Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design o…
