# Balancing Latency and Model Accuracy for Fluid Antenna-Assisted LM-Embedded MIMO Network

arXiv: https://arxiv.org/abs/2508.01689 · HTML: https://mlsanity.com/paper/2508.01689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 17 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020)
- [Power-Efficient Wireless Streaming of Multi-Quality Tiled 360 VR Video in MIMO-OFDMA Systems](https://mlsanity.com/paper/2105.09865/) (2021)
- [A Riemannian ADMM](https://mlsanity.com/paper/2211.02163/) (2022)
- [Movable Antenna-Aided Federated Learning with Over-the-Air Aggregation: Joint Optimization of Positioning, Beamforming, and User Selection](https://mlsanity.com/paper/2411.06721/) (2024)
- [Joint Beamforming and Antenna Movement Design for Moveable Antenna Systems Based on Statistical CSI](https://mlsanity.com/paper/2308.06720/) (2023)
- [User Association and Resource Allocation in Large Language Model Based Mobile Edge Computing System over 6G Wireless Communications](https://mlsanity.com/paper/2310.17872/) (2023)
- [Secure Wireless Communication via Movable-Antenna Array](https://mlsanity.com/paper/2311.07104/) (2023)
- [Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System](https://mlsanity.com/paper/2407.03481/) (2024)
- [Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection](https://mlsanity.com/paper/2503.00011/) (2025)

## Abstract (arXiv)

This paper addresses the challenge of large model (LM)-embedded wireless network for handling the trade-off problem of model accuracy and network latency. To guarantee a high-quality of users' service, the network latency should be minimized while maintaining an acceptable inference accuracy. To meet this requirement, LM quantization is proposed to reduce the latency. However, the excessive quantization may destroy the accuracy of LM inference. To this end, a promising fluid antenna (FA) technology is investigated for enhancing the transmission capacity, leading to a lower network latency in the LM-embedded multiple-input multiple-output (MIMO) network. To design the FA-assisted LM-embedded network with the lower latency and higher accuracy requirements, the latency and peak signal to noise ratio (PSNR) are considered in the objective function. Then, an efficient optimization algorithm is proposed under the block coordinate descent framework. Simulation results are provided to show the convergence behavior of the proposed algorithm, and the performance gains from the proposed FA-assisted LMembedded network over the other benchmark networks in terms of network latency and PSNR.