Heterogeneous Wireless Networks: Traffic Offloading, Resource Allocation and Coverage Analysis

Unlike 2G systems where the radius of macro base station (MBS) could reach several kilometers, the cell radius of LTE-Advanced and next generation wireless networks (NGWNs) such as 5G networks would be random and up to a few hundred meters in order to overcome the radio signal propagation impairments. Heterogeneous wireless networks (HetNets) are becoming an integral part of the NGWNs especially 5G networks, where small cell base stations (SBSs), wireless-fidelity (WiFi) access points (APs), cellular BSs and device-to-device (D2D) enabled links coexist together. HetNets represent novel approaches for the mobile data offloading, resource allocation and coverage probability problems that help to optimize the network traffic. However, heterogeneity and interworking among different radio access technologies bring new challenges such as bandwidth resource allocation, user/cell association, traffic offloading based on the user activity and coverage probability in HetNets. This dissertation attempts to address three key research areas: traffic offloading, bandwidth resource allocation and coverage probability problems in HetNets. In the first part of this dissertation, we derive the mathematical framework to calculate the required active user population factor (AUPF) of small cells based on the probabilistic traffic models. The number of total mobile users and number of active mobile users have different probabilistic distributions such as different combinations of Binomial and Poisson distributions. Furthermore, AUPF is utilized to investigate the downlink BS and backhaul power consumption of HetNets. In the second part, we investigate two different traffic offloading (TO) schemes (a) Path loss (PL) and (b) Signal-to-Interference ratio (SIR) based strategies. In this context, a comparative study on two techniques to offload the traffic from macrocell to small cell is studied. Additionally, the AUPF, small cell access scheme and traffic type are included into a PL based TO strategy to minimize the congested macrocell traffic. In the third part, the joint user assignment and bandwidth resource allocation problem is formulated as a mixed integer non-linear programming (MINLP). Due to its intractability and computational complexity, the MINLP problem is transformed into a convex optimization problem via a binary variable relaxation approach. Based on the mathematical analysis of the problem, a heuristic algorithm for joint user assignment and bandwidth allocation is presented. The proposed solution achieves a near optimal user assignment and bandwidth allocation at reduced computational complexity. Lastly, we investigate the transition between traditional hexagonal BS deployment to random BS placement in HetNets. Independent Poisson Point Processes (PPPs) are used to model the random locations of BSs. Lloyds algorithm is investigated for analyzing the coverage probability in a network which functions as a bridge between random and structural BS deployments. The link distance distribution is obtained by using the Expectation-Maximization (EM) algorithm which is further utilized for calculating the coverage probability

Intracell interference characterization and cluster interference for D2D communication

The homogeneous spatial Poisson point process (SPPP) is widely used for spatial modeling of mobile terminals (MTs). This process is characterized by a homogeneous distribution, complete spatial independence, and constant intensity measure. However, it is intuitive to understand that the locations of MTs are neither homogeneous, due to inhomogeneous terrain, nor independent, due to homophilic relations. Moreover, the intensity is not constant due to mobility. Therefore, assuming an SPPP for spatial modeling is too simplistic, especially for modeling realistic emerging device-centric frameworks such as device-to-device (D2D) communication. In this paper, assuming inhomogeneity, positive spatial correlation, and random intensity measure, we propose a doubly stochastic Poisson process, a generalization of the homogeneous SPPP, to model D2D communication. To this end, we assume a permanental Cox process (PCP) and propose a novel Euler-Characteristic-based approach to approximate the nearest-neighbor distribution function. We also propose a threshold and spatial distances from an excursion set of a chi-square random field as interference control parameters for different cluster sizes. The spatial distance of the clusters is incorporated into a Laplace functional of a PCP to analyze the average coverage probability of a cellular user. A closed-form approximation of the spatial summary statistics is in good agreement with empirical results, and its comparison with an SPPP authenticates the correlation modeling of D2D nodes

Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines

ABSTRACT: Radio air interface identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) signal identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto-correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over-the-air real-world measurements are taken to show that wireless signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in-phase/quadrature (I/Q) samples, training set size, or signal-to-noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well-known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/F-1-scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing

Monitoring, Surveillance, and Management of the Electromagnetic Spectrum: Current Issues in Electromagnetic Spectrum Monitoring

This paper discusses the current aspects of the surveillance and management of the electromagnetic spectrum under various topics that are vital to wireless communications. On the basis of the recent technological advancements in conjunction with the discussions on the global approaches toward electromagnetic spectrum monitoring, the paper addresses short-, medium-, and long-term strategies in wireless communications for monitoring, governing, and managing the electromagnetic spectrum of leading countries and multinational organizations. Furthermore, it proposes a novel spectrum monitoring strategy for next-generation wireless systems and outlines the implications of the proposed strategy

Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels

Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in relation to that when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016.10a is the main dataset utilized for testing and evaluation of the proposed methods. However, the channel effects incorporated in this dataset and some others may lack the appropriate modeling of the real-world conditions since it only considers two distributions for channel models for a single tap configuration. Therefore, in this paper, a more comprehensive dataset, named as HisarMod2019.1, is also introduced, considering real-life applicability. HisarMod2019.1 includes 26 modulation classes passing through the channels with 5 different fading types and several numbers of taps for classification. It is shown that the proposed model performs better than the existing models in terms of both accuracy and training time under more realistic conditions. Even more, surpassed their performance when the RadioML2016.10a dataset is utilized

Reconfigurable Intelligent Surfaces Empowered THz Communication in LEO Satellite Networks

The revolution in the low Earth orbit (LEO) satellite networks will bring changes on their communication models and a shift from the classical bent-pipe architectures to more sophisticated networking platforms. Thanks to technological advancements in microelectronics and micro-systems, the terahertz (THz) band has emerged as a strong candidate for inter-satellite links (ISLs) due to its promise of high data rates. Yet, the propagation conditions of the THz band need to be properly modeled and controlled by utilizing reconfigurable intelligent surfaces (RISs) to leverage their full potential. In this work, we first provide an assessment of the use of the THz band for ISLs, and quantify the impact of misalignment fading on error performance. Then, in order to compensate for the high path loss associated with high carrier frequencies, and to further improve the signal-to-noise ratio (SNR), we propose the use of RISs mounted on neighboring satellites to enable signal propagation. Based on a mathematical analysis of the problem, we present the error rate expressions for RIS-assisted ISLs with misalignment fading. Also, numerical results show that RIS can leverage the error rate performance and achievable capacity of THz ISLs

Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors

The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid “close-in”, and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed