# AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization

arXiv: https://arxiv.org/abs/2605.06443 · HTML: https://mlsanity.com/paper/2605.06443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 7 May 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 10 of 10)

- [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020)
- [Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://mlsanity.com/paper/2007.00038/) (2020)
- [LLM-OptiRA: LLM-Driven Optimization of Resource Allocation for Non-Convex Problems in Wireless Communications](https://mlsanity.com/paper/2505.02091/) (2025)
- [A Deep Learning-Based Framework for Low Complexity Multi-User MIMO Precoding Design](https://mlsanity.com/paper/2207.03765/) (2022)
- [Interference Exploitation-based Hybrid Precoding with Robustness Against Phase Errors](https://mlsanity.com/paper/1810.04490/) (2018)
- [Hybrid Analog-Digital Precoding for Interference Exploitation](https://mlsanity.com/paper/1810.12044/) (2018)
- [Robust Symbol Level Precoding for Overlay Cognitive Radio Networks](https://mlsanity.com/paper/2301.08393/) (2023)
- [MIMO Precoding Design with QoS and Per-Antenna Power Constraints](https://mlsanity.com/paper/2306.02343/) (2023)
- [Symbol-Level Precoding for MU-MIMO System with RIRC Receiver](https://mlsanity.com/paper/2307.14797/) (2023)
- [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026)

## Abstract (arXiv)

Precoding is a key technique for interference management and performance improvement in multi-antenna wireless systems. However, existing precoding methods are typically developed for specific system models, objectives, and constraint sets, which limits their adaptability to the heterogeneous and evolving scenarios expected in future 6G networks. To address this limitation, we propose AgenticPrecoding, a universal multi-agent framework that automates end-to-end precoding derivation directly from user-level communication requirements. Specifically, AgenticPrecoding decomposes the derivation process into four coordinated stages: problem formulation, solver selection, prompt upsampling, and code generation, assigning each stage to a specialized agent tailored to its specific reasoning demands. We employ two LoRA-adapted reasoning agents to inject precoding-specific domain knowledge for problem formulation and solver selection, while two general-purpose Large Language Models (LLMs) handle prompt refinement and executable code generation. Furthermore, a feedback-driven refinement mechanism is incorporated to enhance code executability, constraint feasibility, and solution quality. Extensive experiments across 10 representative precoding scenarios demonstrate that AgenticPrecoding achieves superior cross-scenario adaptability compared to conventional optimization-based and LLM-based baselines.