Identifying line-of-sight (LOS) and non-LOS (NLOS) channel conditions can improve the performance of many wireless applications, such as signal strength-based localization algorithms. For this purpose, channel state information (CSI) obtained by commodity IEEE 802.11n devices can be used, because it contains information about channel impulse response (CIR). However, because of the limited sampling rate of the devices, a high-resolution CIR is not available, and it is difficult to detect the existence of an LOS path from a single CSI measurement, but it can be inferred from the variation pattern of CSI over time. To this end, we propose a recurrent neural network (RNN) model, which takes a series of CSI to identify the corresponding channel condition. We collect numerous measurement data under an indoor office environment, train the proposed RNN model, and compare the performance with those of existing schemes that use handcrafted features. The proposed method efficiently learns a non-linear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.

In this study, the channel characteristics and cell coverage of the 28 GHz millimeter-wave (mmWave) band outdoors are investigated by developing an efficient threedimensional ray-tracing simulation. First, the accuracy of the simulation is verified by comparing its results with actual measurements. The path loss (PL) agrees well in both lineof- sight (LOS) and non-line-of-sight (NLOS) regions, whereas the shadowing factor exhibits differences in the NLOS regions. Additional simulations are conducted for downtown Gangnam, a representative high-rise urban area in Seoul, Korea. The simulated and measured coverages of 300 base stations operating in 900 MHz long-term evolution (LTE) band are compared and analyzed. For the LOS regions, PL of 28 GHz is 30 dB higher because of the free-space PL gaps, whereas PL varied in the NLOS regions because of multipath fading. In addition, the outage probability is determined to evaluate the validity of mmWave cell deployment and coverage for high-rise urban micro-cell environments. The contributions of this work include LOS and outage probability models with low root-meansquare errors, indicating improvement over previously developed models.

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks.

As positioning solutions integrate multiple components to improve accuracy, the number of parameters that require calibration has increased. This paper studies a calibration-free positioning technique using Wi-Fi ranging and pedestrian dead reckoning (PDR), where every parameter in the system is optimized in real-time. This significantly decreases the time and effort required to perform manual calibration procedures and enables the positioning solution to achieve robust performance in various situations. Additionally, this paper studies an efficient way of performing irregular Wi-Fi ranging procedures to improve battery life and network performance of mobile devices. The positioning performance of the proposed method was verified using a real-time Android application on several mobile devices under a large indoor office environment. Without any calibration, the proposed method achieved up to 1.38 m average positioning accuracy for received signal strength (RSS)-based ranging scenarios, which differs only by 30 cm from the benchmark assuming perfect calibration. In addition, the proposed method achieved up to 1.04 m accuracy for round trip time (RTT)-based ranging scenarios with a 40 MHz bandwidth configuration, which differs only by 10 cm from the benchmark.

Because the characteristics of wireless propagation channels (especially indoor channels) are too diverse and complex, the distance estimation strategy of range-based positioning techniques should adaptively change depending on the environment. In this paper, we study unsupervised learning techniques that efficiently do this without human intervention. As users simply move around an area of interest with mobile devices, the proposed method autonomously learns the characteristics of the surrounding environments and changes the ranging strategy accordingly. To this end, we use either model-based or neural network (NN)-based ranging modules for estimating the distance from neighboring anchor nodes, calculate the position of the devices using trilateration techniques, and define cost functions that indirectly evaluate the accuracy of the ranging module based on the trilateration results. Moreover, by assigning a unique trainable variable to each device, the proposed method is also able to compensate for different characteristics between devices without ground truth data. The performance of the proposed method is verified with a real-time location tracking application using received signal strength (RSS) measurements from conventionalWi-Fi access points (APs) or round trip time (RTT) measurements from APs that support the fine timing measurement (FTM) protocol. In cases where a model-based ranging module is used, the proposed method closely achieves the benchmark performance, which perfectly optimizes all the trainable variables on the test data. If NNs are adopted in the ranging module, the proposed method even outperforms the benchmark and achieves an average positioning accuracy of up to 2.397 m using RSS measurements, and up to 1.547 m using RTT measurements under the 40 MHz bandwidth configuration.

The millimeter-wave (mmWave) band has attracted great attention as promising spectrum for the next-generation cellular systems. However, mmWave communication systems have encountered several challenges because of different electromagnetic characteristics compared with existing spectrum below-6 GHz systems. In order to evaluate the technical feasibility of mmWave provision, 28 GHz mmWave band channel models were presented by developing an efficient three-dimensional (3-D) ray-tracing simulation. The accuracy of 3-D ray-tracing simulation was verified by comparing its results with actual measurements. The path loss (PL) agreed well in both line-of-sight (LOS) and non-line-of-sight (NLOS) regions, whereas the shadowing factor exhibited differences in the NLOS regions. The contributions of this work include confirmation of the applicability of single- and dual-slope LOS and NLOS PL models with different threshold distances for NLOS regions with low root-mean-square errors.