Over recent years, wireless indoor positioning systems (WIPS) have attracted considerable research interest. However, high-performance WIPS proposed in the literature requires that the building have at least three access points (APs). This paper proposes an WIPS using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses beam fingerprints and classification models based on KNN (K-nearest neighbor) and Bayes rule. The beam fingerprint is composed of RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment for each beam codebook in the off-line phase. In the online phase, RSS samples of the best beams are collected by user equipment (UE) during the beamtracking process, which are then classified based on beam fingerprints into predefined coordinates. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.

This paper proposes a hybrid algorithm based on Bayesian inference and K-Nearest Neighbor to estimate the threedimensional indoor positioning implemented from a fingerprint technique. Additionally, a comparison was made between the main algorithms discussed in literature. The experiments were conducted in a typical building with two floors with 180m 2 and four access points. The proposed solution showed a precision in the location of the rooms of 97% and 90% the estimates were at maximum three meters away from the actual location, furthermore, such method has lower variability than other algorithms, with deviation in relation to the mean reaches of 37.62%.

Resumo-Há um crescente número de trabalhos sobre WIPSs (WLAN-based Indoor Positioning System), sistema de posicionamento local sem infraestrutura dedicada, baseado em redes locais sem fios (WLANs). Algoritmos de localização para esses sistemas são constantemente propostos, requerendo sua implementação para validação e configuração. O presente trabalho apresenta um simulador interativo para análise de algoritmos de localização 2D baseados em Mapas de Assinatura de RSSI (indicador de intensidade do sinal recebido). O simulador foi implementado em Matlab. Por fim, um WIPS foi implementado em uma residência para a validação do simulador. Palavras-Chave-Sistema de poscionamento local, localização 2D, WLAN, simulador.

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Abstract-Wireless

Machine learning has been widely used as a solution to deal with beam management training overhead for 5th generation wireless communication systems. However, the types of information adopted for the training base have not been sufficient to achieve a robust intelligent system. Channel path parameters provide valuable information that can increase the accuracy of this type of solution. In this work, we present initial results of the application of the Kalman filter to estimate path parameters aiming at a robust training base. Simulated results in 3D ray-tracing show promising results on obstruction conditions.

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