WirelessAgent: A Unified Agent Design for General Wireless Resource Allocation Problem without Current Channel State Information

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 resource scheduling under different objectives, we develop adaptive algorithms. Simulation results validate the effectiveness of our proposed design, demonstrating that the proposed AI method reduces the root mean square error by approximately up to 67\% compared to the traditional approach. Moreover, the data-driven scheduling balances system performance compared to conventional baseline approaches.