Research Article
Performance Analysis of LTE Technology in Unlicensed Spectrum for Mobile Communication
Kadoke Marco*,
Kilavo Hassan
Issue:
Volume 10, Issue 1, December 2025
Pages:
1-8
Received:
23 May 2025
Accepted:
10 June 2025
Published:
9 September 2025
Abstract: Long Term Evolution (LTE) is a mobile technology aimed at delivering high-speed internet connectivity. With the rising demand from wireless devices such as smartphones and tablets, interest has grown in operating LTE within the unlicensed spectrum to access broader bandwidths. This study employs systematic literature review and simulation-based analysis to evaluate LTE performance in unlicensed bands. Findings indicate that LTE achieves throughput comparable to licensed operation. However, increasing transmit power and duty cycle enhances LTE performance while significantly degrading Wi-Fi performance. Coexistence methods like Listen Before Talk (LBT) and Carrier Sensing Adaptive Transmission (CSAT) underperform compared to proposed strategies. Effective coexistence mechanisms are essential to prevent interference and ensure balanced performance across technologies.
Abstract: Long Term Evolution (LTE) is a mobile technology aimed at delivering high-speed internet connectivity. With the rising demand from wireless devices such as smartphones and tablets, interest has grown in operating LTE within the unlicensed spectrum to access broader bandwidths. This study employs systematic literature review and simulation-based ana...
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Research Article
Exploitation of Feedforward Neural Networks to Improve HPA Predistortion Performance and Application to OTFS Signals
Hariniony Bienvenu Rakotonirina*
,
Marie Emile Randrianandrasana
Issue:
Volume 10, Issue 1, December 2025
Pages:
9-27
Received:
17 November 2025
Accepted:
4 December 2025
Published:
20 December 2025
DOI:
10.11648/j.awcn.20251001.12
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Views:
Abstract: 6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinearities, which degrade spectral purity (ACPR or Adjacent Channel Power Ratio) and increase the Bit Error Rate (BER). Digital predistortion (DPD) is the most effective method for HPA linearization, but classical polynomial models struggle to capture complex nonlinearities especially when applied to demanding signals like OTFS. In this paper, we propose and evaluate an innovative DPD approach based on a feedforward neural network. A multi-criteria analysis demonstrates that this method significantly outperforms polynomial predistortion: it achieves precise predistortion function approximation with a Mean Squared Error (MSE) of 7.38 × 10-6, improves ACPR by 22 dB (from -15 dB to -36 dB), and attains a BER nearly identical to that of a linear amplifier even in a Rayleigh fading channel. Moreover, it enables the HPA to operate in saturation (low IBO ou Input Back-off, ~70% efficiency) while preserving optimal transmission quality, thereby breaking the traditional trade-off between energy efficiency and linearity. Our approach is simple, robust, and computationally lightweight, paving the way for highly efficient 6G transmission chains tailored for mobile environments.
Abstract: 6G (Next-generation mobile telephony) communication systems require modulation schemes robust against channel fading, and Orthogonal Time Frequency Space (OTFS) has emerged as a key technology to achieve this goal. However, OTFS exhibits high amplitude variations (high PAPR), making it particularly susceptible to High Power Amplifier (HPA) nonlinea...
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