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 multistorey 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.
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.
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 twofold 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.
Quantum error correction is studied in a framework consisting of an open quantum system and its environment, jointly subjected to a unitary action, and an interaction-free reference system. It has been shown that coherent information between the initially correlated open system and reference system is conserved in the transmission stage of any quantum communication process, provided that the tripartite input is any pure Markov state and the overall evolution preserves its form. This conservation constitutes the necessary and sufficient condition for accomplishment of perfect error correction by a recovery channel even in the presence of initial system-environment correlations. Explicit expressions of the recovery operators and examples of the joint unitary evolution preserving the form of inputs are given for all classes of pure Markov states.
Any tripartite state which saturates the strong subadditivity relation for the quantum entropy is defined as the Markov state. A tripartite pure state describing an open system, its environment and their purifying system is a pure Markov state iff the bipartite marginal state of the purifying system and environment is a product state. It has been shown that as long as the purification of the input system-environment state is a pure Markov state the reduced dynamics of the open system can be described, on the support of initial system state, by a quantum channel for every joint unitary evolution of the system-environment composite even in the presence of initial correlations. Entanglement, discord and classical correlations of the initial system-environment states implied by the pure Markov states are analyzed and it has been shown that all these correlations are entirely specified by the entropy of environment. Some implications concerning perfect quantum error correction procedure and quantum Markovian dynamics are presented.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.