Welcome to Resource Allocation

Resource Allocation is a critical process in both project management and systems engineering that involves assigning available resources in the most efficient manner. This strategic distribution focuses on optimizing the use of time, materials, personnel, and budget to achieve specific objectives. Effective resource allocation ensures that projects are completed on time, within budget, and with the desired quality, while also maximizing overall productivity and profitability. This process requires careful planning, monitoring, and adjustment to respond to changing circumstances and priorities.

Representative Publications

Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts
Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Ping Zhang, Dong In Kim
Under Review

The paper explores a dual perspective multi-agent systems and mixture of experts for generative AI Networking.
Paper | Code | YouTube | Bilibili

Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Khaled B. Letaief
Under Review

This paper proposes mobile edge networks supported MoE-based GAI.
Paper | Code

Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach
Hongyang Du, Guangyuan Liu, Yijing Lin, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim
Under Review

This paper proposes a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints, select specialized DRL experts, and weigh each decision.
Paper | Code | YouTube | Bilibili

Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study
Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Shuguang Cui, Xuemin Shen, Ping Zhang
IEEE Network Magazine

This paper highlights how prompt engineering and AIGC are related to mobile networks.
Paper | YouTube | Bilibili

Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts
Hongyang Du, Guangyuan Liu, Dusit Niyato, Jiayi Zhang, Jiawen Kang, Zehui Xiong, Bo Ai, Dong In Kim
ICASSP 2024

This paper demonstrates how multi-modal prompt (text+image) can improve AIGC used in semantic communication with covert communication technique.
Paper | Code | YouTube | Bilibili

Flexible-Position MIMO for Wireless Communications: Fundamentals, Challenges, and Future Directions
Jiakang Zheng, Jiayi Zhang, Hongyang Du, Dusit Niyato, Sumei Sun, Bo Ai, Khaled B. Letaief
IEEE Wireless Communications Magazine

The paper discusses a new type of an antenna system that allows antenna elements to be adjusted location-wise; generative AI is also used to optimize the location and movement paths of the antenna elements.
Paper | YouTube | Bilibili

YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction
Baoxia Du, Hongyang Du, Haifeng Liu, Dusit Niyato, Peng Xin, Jun Yu, Mingyang Qi, You Tang
IEEE Internet of Things Journal

The paper introduces a new semantic communication application that utilizes a generative AI method to solve edge resource allocation to construct efficient digital twins.
Paper | YouTube | Bilibili

Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Junshan Zhang, Xuemin (Sherman) Shen
IEEE Journal on Selected Areas in Communications

This paper proposes the inverse semantic communications.
Paper | Code

Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services
Hongyang Du, Jiazhen Liu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Junshan Zhang, Dong In Kim
IEEE Journal on Selected Areas in Communications

This paper proposes a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users.
Paper | Code

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