WirelessAgent: A Unified Agent Design for General Wireless Resource Allocation Problem without Current Channel State Information
Ran Yi, Ruopeng Xu, Dongshu Zhao, Zhaoyang Zhang, Baolin Chen, Kai-Kit Wong, Hyundong Shin, Zhaohui Yang

TL;DR
This paper presents WirelessAgent, an AI-driven, unified agent design that addresses wireless resource allocation without relying on current channel state information, optimizing multiple objectives through adaptive algorithms.
Contribution
It introduces a novel sense-repair-decide-act workflow and AI-based prediction model for resource allocation without current CSI, enabling natural language interaction and multi-objective optimization.
Findings
AI model reduces root mean square error by up to 67%
Data-driven scheduling balances system performance effectively
Proposed design validates through simulation results
Abstract
This paper investigates the agent design for solving the wireless resource allocation problem without sufficient channel state information (CSI), which cannot be effectively solved via conventional method. In the considered wireless agent design, we provide the general sense-repair-decide-act workflow, which can be used to intelligently solve general wireless resource allocation problem. A multi-objective optimization problem is formulated to adaptively satisfy different user requirements including both spectrum and energy efficiency. This work addresses the challenge of incomplete CSI for multiple optimization objectives. To solve this problem, we use an artificial intelligence (AI) model to predict missing channel data and construct an agent on the Coze platform, allowing the network operators to optimize multiple objectives through natural language conversations. To tackle the…
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