IMPRESS: indoor mobility prediction framework for pre-emptive indoor-outdoor handover for mmwave networks

Millimeter-wave (mmWave) communication, the main success behind the fifth generation of mobile communication networks, will increase the ultra-dense small cell deployment under its limited coverage characteristics. Therefore, providing a seamless connection to its users, to whom transitioning between indoor and outdoor in a heterogeneous network environment particularly is a significant issue that needs to be addressed. In this paper, we present a two-fold contribution with a comprehensive study on mm-wave handovers. A user-based indoor mobility prediction via Markov chain with an initial transition matrix is proposed in the first step. Based on this acquired knowledge of the user’s movement pattern in the indoor environment, we present a pre-emptive handover algorithm in the second step. This algorithm aims to keep the QoS high for indoor users when transitioning between indoor and outdoor in a heterogeneous network environment. The proposed algorithm shows a reduction in the handover signalling cost by more than 50%, outperforming conventional handover algorithms

Coverage Analysis for Indoor-Outdoor Coexistence for Millimetre-Wave Communication

Milimeter-wave (mm-wave) communication, which has already been a part of the fifth generation of mobile communication networks (5G), would result in ultra dense small cell deployments due to its limited coverage characteristics. In such an environment, outdoor base stations (BS) will get closer to the buildings, in which users are covered and served by indoor small cells that in turn degrades the user Quality of Experience (QoE) owing to the increased interference caused by the outdoor BSs. In this paper, indoor coverage analysis is conducted by considering a scenario, which includes a multi-storey building and two identical indoor femtocell and outdoor BS operating at 28 GHz. During the simulations, impacts of the outdoor BS's transmit power and distance to the building on the indoor coverage are investigated. In addition, various material types, namely one layer brick, International Telecommunication Union (ITU) 28 GHz concrete, ITU 28 GHz glass, and ITU 28 GHz wood, for the building walls are tested. Results reveal that dielectric properties of the materials are the key factors in determining the severity of the interference caused by the outdoor BS, paving the way for including the effects of material type in network designing and smart city planning

Investigation of the Incidence of Legionella and Free-Living Amoebae in Swimming Pool Waters and Biofilm Specimens in Istanbul by Different Methods

Legionella bacteria living in free form or in biofilm and free-living amoebae (FLA) can infect humans through swimming pools and can cause various diseases. FLA may also threaten the health of swimmers because they are capable of being hosts for Legionella and some other bacteria. The aim of this study was to investigate the presence of total aerobic heterotrophic bacteria (TAHB), FLA and Legionella bacteria in swimming pool waters and biofilm samples in Istanbul by using culture and FISH methods. Water plate count agar (wPCA), buffered charcoal yeast extract (BCYE) agar supplemented with glycine-vancomycin-polymyxin-cycloheximide (GVPC) and Escherichia coli cultivated non-nutrient agar (NNA) were used for the culture of TAHB, Legionella and FLA. For the FISH method analysis, Leg 705 and Leg PNE1 probes labeled with fluorescent dye for Legionella and ACANTHA probe for Acanthamoeba genus FLA were used. Legionella pneumophila serogroup 1 ATCC 33152, L.pneumophila serogroup 3 ATCC 33155 and Acanthamoeba castellani ATCC 50373 were used as positive controls. TAHB were grown in 92% and 84% of water and biofilm samples. Although Legionella bacteria could not be grown in any of the water samples, it was detected in 6 (24%) water samples by FISH method. Although these bacteria could be grown in 1 (4%) of biofilm samples, 7 (28%) were detected by FISH method. FLA were found to be 16% by culture in water samples and 28% by FISH analysis. These amoebae were detected 8% and 20% in biofilm samples by culture and FISH method, respectively. It was determined that one of the isolates of FLA had thermotolerant activity (potentially pathogenic). L.pneumophila serogroup 1 was detected in one water sample and in four biofilm samples. According to the culture method, TAHB and FLA were found to be more common in water samples than in biofilm samples and Legionella bacteria were more common in biofilm samples than in water samples (p <= 0.05). In the detection of Legionella bacteria, the superiority of FISH method compared to culture method was found to be statistically significant (p <= 0.05). In this study, it was found that the number of TAHB in the controlled swimming pools was within the limits determined by the Ministry of Health (<= 200 cfu/ml). It will be appropriate to examine both water and biofilm samples for the investigation of TAHB, FLA and Legionella. It may be appropriate to use both culture and FISH methods to detect the presence of FLA in water and biofilm samples. This study is the first study to investigate the presence of Legionella and FLA in swimming pools in Istanbul, and further studies are needed to examine more pool water and biofilm samples. With the data obtained, the health principles and controls of swimming pools will be re-considered and will be contributed to public health

Seamless indoor/outdoor coverage in 5G

Densely deployment of the small cells in 5G networks will bring high‐quality service to the end users as well as will solve the small footprint coverage problem of millimeter‐waves. The increase in the number of small cells will require self‐organized systems to enable the seamless transaction between heterogeneous network environment. Therefore, a survey‐style study on self‐organized seamless coverage in 5G, covering millimeter‐wave features and its indoor and outdoor coverage along with some machine learning techniques are presented in this article

Indoor Mobility Prediction for mmWave Communications using Markov Chain

Millimeter-wave (mm-wave) communication, which has already been a part of the fifth generation of mobile communication networks (5G), would result in ultra dense small cell deployments due to its limited coverage characteristics. To enable seamless handovers between indoor and outdoor environments, a mobility prediction of an indoor user is studied by deploying Markov chains. Based on the effect of external factors on the user’s mobility, a simulation scenario is created to model the trajectory of an indoor user w.r.t the most visited areas before leaving the indoor environment. Based on that, a method for initializing the transition matrix of Markov chains is proposed, via Q-learning. The proposed solution is compared to a standard online learning Markov chain model in terms of different mobility models and learning rates. Results show that the proposed solution is always able to outperform the standard method in terms of prediction accuracy