Performance Analysis of LTE Systems with Artificial Intelligence-Assisted Interpretation

Anisia-Teodora FUGARU; Mădălina CURTA; Anamaria SÂRBU

doi:10.2478/bsaft-2026-0006

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Performance Analysis of LTE Systems with Artificial Intelligence-Assisted Interpretation

Scientific Bulletin

Volume 31 (2026): Issue 1 (June 2026)

By: Anisia-Teodora FUGARU, Mădălina CURTA and Anamaria SÂRBU

Open Access

|Jun 2026

Abstract

This paper presents an interactive software platform for link-level performance analysis of Long Term Evolution (LTE) systems, integrating a parametric analytical engine with an artificial intelligence-driven interpretation module. Unlike full Monte Carlo physical-layer simulators, the proposed platform employs sigmoid-based analytical approximations of Block Error Rate (BLER) as a function of SNR, enabling rapid and reproducible performance estimation suitable for academic and educational use. By transforming analytical outputs into structured numerical metrics, the performance evaluation of LTE physical layer configurations is treated as an automated analytical task powered by a large language model. The platform evaluates key parameters such as adaptive modulation schemes, MIMO spatial configurations, and standardized 3GPP radio channel models, generating structured academic reports without manual intervention. Experimental results compare BLER and spectral efficiency across multiple configurations, with MIMO 2×2 achieving a 4 dB SNR gain over SISO and operating at 60.08% of the theoretical Shannon capacity limit. The platform employs a dual-path verification architecture: a first computation path pre-calculates all eight IEEE-standard metrics from raw simulation data and embeds them as explicit anchor values in the AI prompt, while a fully independent second path recalculates the same metrics from the same raw data and cross-validates the AI-generated report. Under this architecture, the AI interpretation engine achieves 87.5% metric attribution accuracy, measuring the rate at which the language model correctly associates pre-provided anchor values with their respective metric definitions in the generated report, and produces 1,000-word technical reports in under 40 seconds, significantly reducing the expertise barrier for LTE performance engineering.

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Articles in this issue

DOI: https://doi.org/10.2478/bsaft-2026-0006 | Journal eISSN: 3100-5098 | Journal ISSN: 3100-508X

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Language: English

Page range: 75 - 86

Submitted on: Mar 23, 2026

Accepted on: May 4, 2026

Published on: Jun 24, 2026

Published by: Nicolae Balcescu Land Forces Academy

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

3GPP,

adaptive modulation,

channel models,

LTE,

large language model,

throughput

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other,

Law,

Law, other,

Business and economics,

Business management,

Management, organization, corporate governance,

Social sciences,

Political science,

Military policy

© 2026 Anisia-Teodora FUGARU, Mădălina CURTA, Anamaria SÂRBU, published by Nicolae Balcescu Land Forces Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 31 (2026): Issue 1 (June 2026)