arXiv subjects

Vicente Angelo de Sousa Junior

Publications and source records attributed to Vicente Angelo de Sousa Junior.

3 recordsLinked to original sources

arXiv2026

People effects on IoT indoor wireless channel characterization

Wireless communication under 1 GHz is suitable for Internet of Things (IoT) applications due to larger coverage capability with less power consumption. Bearing in mind that people and elements contained in the environment can cause variations in the channel, this paper aims to evaluate the effect of the presence of people on a 900-MHz indoor narrowband wireless channel, as we characterize the small-scale phenomena. With the increase in the number of people, a greater variation in the communication channel was noticed, which is reflected in the parameters of the probability distributions used in the characterization of the random part of the signal. In addition, second-order statistics were used to analyze the data and an adherence test was applied to confirm the behavior of the signal in relation to the distributions.

cs.NI↗

arXiv2026

Spreading Factor Assignment Strategy for Coverage and Capacity Flexible Tradeoff

LoRa is a physical layer technology with the ability to connect multiple devices in a wide area of coverage, with low power consumption and with interference robustness. In this Letter, we investigate the efficiency of LoRa to send multiple uplink streams, analyzing strategies of allocating the spreading factor for coverage or capacity enhancement.

cs.NI↗

arXiv2026

User-Level Handover Decision Making Based on Machine Learning Approaches

This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.

cs.LG↗