Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency

Applicability of shape parameterizations for giant dipole resonance in warm and rapidly rotating nuclei

We investigate how well the shape parameterizations are applicable for studying the giant dipole resonance (GDR) in nuclei, in the low temperature and/or high spin regime. The shape fluctuations due to thermal effects in the GDR observables are calculated using the actual free energies evaluated at fixed spin and temperature. The results obtained are compared with Landau theory calculations done by parameterizing the free energy. We exemplify that the Landau theory could be inadequate where shell effects are dominating. This discrepancy at low temperatures and high spins are well reflected in GDR observables and hence insists on exact calculations in such cases.Comment: 10 pages, 2 figure

A Mathematical Game of Reaching an Island in a Deep Lake with the Aid of a Rope

The problem of reaching an island in a deep lake by a man who cannot swim, with the help of just a rope, has been generalised. The rope is such that it can be used to tie the two trees only, one on the edge of the lake and other in the island. The least length of the rope that the man would require has been obtained. The particular case of an elliptical lake with one tree at an end of a latus rectum and the other at the corresponding focus in an island there has been discussed

Characterization of Methane Based Graphene Synthesis on H13 Tool Steel

As the demand to weld higher strength materials through friction stir welding increases, the need for better non-consumable rotating tool increases as it has to be able to endure high frictional and thermal deformation while the workpiece undergoes intense plastic deformation at high temperatures. This project aims to make use of graphene (Young’s Modulus = 1.0 TPa) in the improvement of friction stir welding tools. Nickel is coated on H13 Tool Steel substrate via Magnetron Sputtering to act as a catalyst for graphene growth. Graphene synthesis with methane gas is done through Chemical Vapour Deposition (CVD) process. Characterization of the interface layers are done with Raman Spectroscopy, X-ray Photoelectron Spectroscopy, Field Emission Scanning Electron Microscope, and Energy Dispersive Spectroscopy. In this report, characterisation experiments revealed an absence of graphene on bare substrate as well as the allegedly nickel coated substrate. Analysis of the surface elements revealed no nickel coating which is inferred to be the main cause of the absence of graphene. Interestingly, FE-SEM revealed crystal like structure and graphitic is nature as determined by XPS and EDS technique

An Evaluation of Pareto, Lognormal and PPS Distributions: The Size Distribution of Cities in Kerala, India

The Pareto-Positive Stable (PPS) distribution is introduced as a new model for describing city size data of a region in a country. The PPS distribution provides a flexible model for fitting the entire range of a set of city size data and the classical Pareto and Zipf distributions are included as a particular case

Ethyl 1-(2-hy­droxy­eth­yl)-2-p-tolyl-1H-benzimidazole-5-carboxyl­ate

The asymmetric unit of the title compound, C19H20N2O3, contains two mol­ecules (A and B) with slightly different orientations of the ethyl groups with respect to the attached carboxyl­ate groups. Intra­molecular C—H⋯O hydrogen bonds generate S(8) ring motifs in both mol­ecules A and B. In each mol­ecule, the benzimidazole ring system is essentially planar, with maximum deviations of 0.023 (1) and 0.020 (1) Å, respectively, for mol­ecules A and B. The dihedral angle between the benzimidazole ring system and the phenyl ring is 37.34 (5)° for mol­ecule A and 42.42 (5)° for mol­ecule B. In the crystal, O—H⋯N and C—H⋯O hydrogen bonds link the mol­ecules into [100] columns with a cross-section of two-mol­ecule by two-mol­ecule wide, and further stabilization is provided by weak C—H⋯π and π–π inter­actions [centroid separations = 3.5207 (7) and 3.6314 (8) Å]