Intelligent recommendation applications in smart cities require the precise location of the users. The traditional global positioning system (GPS) uses satellite signals for the precise positioning of the user but is vulnerable to signal blockage in the complex indoor environment. The unforeseeable propagation losses due to multi-path effects as well as the permittivity and permeability difference of the materials lead to non-linear attenuation in the electromagnetic (EM) beam generated by the beacon devices in the indoor environment. Therefore, a robust indoor localization algorithm is required to precisely localize the users in the indoor environment with severe EM blockages. In this paper, we propose a novel hybrid RS-DeepNet framework that uses received signal strength (RSS) from WiFi devices for indoor localization of users. The proposed RS-DeepNet is a deep learning architecture that utilizes multiple gated recurrent layers (GRU) and a K-nearest neighbors (KNN) classifier to estimate the precise location of the user in the indoor setup. Simulation results show that the proposed RS-DeepNet outperforms the state-of-theart approaches and efficiently localizes the users in two indoor scenarios and achieves a lowest mean absolute error of 4.81 and 1.68 meters, respectively.

A document by Arif Ullah . Click on the document to view its contents.

The deployment of new airborne small cells in conjunction with an existing network equipped with a multiple-input multiple-output (MIMO) system can be a promising solution to meet the high throughput and coverage needs of future networks. In this study, we present the potential benefits of deploying a large number of antenna elements in downlink vertical heterogeneous cellular networks (VHCNets). The network model consists of a terrestrial base station (TBS) with an array of massive antenna elements and an aerial base station (ABS) with a single antenna to provide connectivity for ground user equipment (GUEs). The locations of the TBS and ABS are modeled as two-dimensional and three-dimensional Poisson point processes, respectively. An appropriate air-to-ground channel model encompasses both line-of-sight (LOS) and non-line-of-sight (NLOS) communication between GUEs and ABS. Using the concepts of stochastic geometry, the performance of the VHCNet was analyzed in terms of coverage probability, area spectral efficiency, and average ergodic rate. The approximated expressions for these performance metrics were validated using Monte Carlo simulations, and a close match was observed between the analytical and simulated results. Based on the simulation results, subsequent use of a massive number of antennas at TBSs in conjunction with ABSs in the VHCNet can improve the overall performance of the network.