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

TL;DR

This paper proposes a fluid antenna-assisted LM-embedded MIMO network that balances latency and accuracy through quantization and optimization, demonstrating improved performance over benchmarks.

Contribution

It introduces a novel combination of fluid antenna technology and quantization strategies with an optimization algorithm to enhance network latency and inference accuracy.

Findings

The proposed algorithm converges efficiently.

Fluid antenna technology reduces network latency.

Performance gains in PSNR and latency over benchmarks.

Abstract

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.