282 research outputs found
Delay-Optimal Relay Selection in Device-to-Device Communications for Smart Grid
The smart grid communication network adopts a hierarchical structure which consists of three kinds of networks which are Home Area Networks (HANs), Neighborhood Area Networks (NANs), and Wide Area Networks (WANs). The smart grid NANs comprise of the communication infrastructure used to manage the electricity distribution to the end users. Cellular technology with LTE-based standards is a widely-used and forward-looking technology hence becomes a promising technology that can meet the requirements of different applications in NANs. However, the LTE has a limitation to cope with the data traffic characteristics of smart grid applications, thus require for enhancements. Device-to-Device (D2D) communications enable direct data transmissions between devices by exploiting the cellular resources, which could guarantee the improvement of LTE performances. Delay is one of the important communication requirements for the real-time smart grid applications. In this paper, the application of D2D communications for the smart grid NANs is investigated to improve the average end-to-end delay of the system. A relay selection algorithm that considers both the queue state and the channel state of nodes is proposed. The optimization problem is formulated as a constrained Markov decision process (CMDP) and a linear programming method is used to find the optimal policy for the CMDP problem. Simulation results are presented to prove the effectiveness of the proposed scheme
Optimizing the Energy Efficiency of Short Term Ultra Reliable Communications in Vehicular Networks
We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design
Distributed drone base station positioning for emergency cellular networks using reinforcement learning
Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network
Offline to Online Conversion
We consider the problem of converting offline estimators into an online
predictor or estimator with small extra regret. Formally this is the problem of
merging a collection of probability measures over strings of length 1,2,3,...
into a single probability measure over infinite sequences. We describe various
approaches and their pros and cons on various examples. As a side-result we
give an elementary non-heuristic purely combinatoric derivation of Turing's
famous estimator. Our main technical contribution is to determine the
computational complexity of online estimators with good guarantees in general.Comment: 20 LaTeX page
Drone Base Station Positioning and Power Allocation Using Reinforcement Learning
Large scale natural disasters can cause unpredictable losses of human lives and man-made infrastructure. This can hinder the ability of both survivors as well as search and rescue teams to communicate, decreasing the probability of finding survivors. In such cases, it is crucial that a provisional communication network is deployed as fast as possible in order to re-establish communication and prevent additional casualties. As such, one promising solution for mobile and adaptable emergency communication networks is the deployment of drones equipped with base stations to act as temporary small cells. In this paper, an intelligent solution based on reinforcement learning is proposed to determine the best transmit power allocation and 3D positioning of multiple drone small cells in an emergency scenario. The main goal is to maximize the number of users covered by the drones, while considering user mobility and radio access network constraints. Results show that the proposed algorithm can reduce the number of users in outage when compared to a fixed transmit power approach and that it is also capable of providing the same coverage, with lower average transmit power and using only half of the drones necessary in the case of fixed transmit power
Lattice dynamics localization in low-angle twisted bilayer graphene
A low twist angle between the two stacked crystal networks in bilayer
graphene enables self-organized lattice reconstruction with the formation of a
periodic domain. This superlattice modulates the vibrational and electronic
structures, imposing new rules for electron-phonon coupling and the eventual
observation of strong correlation and superconductivity. Direct optical images
of the crystal superlattice in reconstructed twisted bilayer graphene are
reported here, generated by the inelastic scattering of light in a nano-Raman
spectroscope. The observation of the crystallographic structure with visible
light is made possible due to lattice dynamics localization, the images
resembling spectral variations caused by the presence of strain solitons and
topological points. The results are rationalized by a nearly-free-phonon model
and electronic calculations that highlight the relevance of solitons and
topological points, particularly pronounced for structures with small twist
angles. We anticipate our discovery to play a role in understanding Jahn-Teller
effects and electronic Cooper pairing, among many other important
phonon-related effects, and it may be useful for characterizing devices in the
most prominent platform for the field of twistronics.Comment: 9 pages, 8 figure
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