Capacity Analysis of LTE-Advanced HetNets with Reduced Power Subframes and Range Expansion

The time domain inter-cell interference coordination techniques specified in LTE Rel. 10 standard improves the throughput of picocell-edge users by protecting them from macrocell interference. On the other hand, it also degrades the aggregate capacity in macrocell because the macro base station (MBS) does not transmit data during certain subframes known as almost blank subframes. The MBS data transmission using reduced power subframes was standardized in LTE Rel. 11, which can improve the capacity in macrocell while not causing high interference to the nearby picocells. In order to get maximum benefit from the reduced power subframes, setting the key system parameters, such as the amount of power reduction, carries critical importance. Using stochastic geometry, this paper lays down a theoretical foundation for the performance evaluation of heterogeneous networks with reduced power subframes and range expansion bias. The analytic expressions for average capacity and 5th percentile throughput are derived as a function of transmit powers, node densities, and interference coordination parameters in a heterogeneous network scenario, and are validated through Monte Carlo simulations. Joint optimization of range expansion bias, power reduction factor, scheduling thresholds, and duty cycle of reduced power subframes are performed to study the trade-offs between aggregate capacity of a cell and fairness among the users. To validate our analysis, we also compare the stochastic geometry based theoretical results with the real MBS deployment (in the city of London) and the hexagonal-grid model. Our analysis shows that with optimum parameter settings, the LTE Rel. 11 with reduced power subframes can provide substantially better performance than the LTE Rel. 10 with almost blank subframes, in terms of both aggregate capacity and fairness.Comment: Submitted to EURASIP Journal on Wireless Communications and Networking (JWCN

Stochastic Geometry Based Analysis of Capacity, Mobility and Energy Efficiency for Dense Heterogeneous Networks

In recent years, the increase in the population of mobile users and the advances in computational capabilities of mobile devices have led to an exponentially increasing traffic load on the wireless networks. This trend is foreseen to continue in the future due to the emerging applications such as cellular Internet of things (IoT) and machine type communications (MTC). Since the spectrum resources are limited, the only promising way to keep pace with the future demand is through aggressive spatial reuse of the available spectrum which can be realized in the networks through dense deployment of small cells. There are many challenges associated with such densely deployed heterogeneous networks (HetNets). The main challenges which are considered in this research work are capacity enhancement, velocity estimation of mobile users, and energy efficiency enhancement. We consider different approaches for capacity enhancement of the network. In the first approach, using stochastic geometry we theoretically analyze time domain inter-cell interference coordination techniques in a two-tier HetNet and optimize the parameters to maximize the capacity of the network. In the second approach, we consider optimization of the locations of aerial bases stations carried by the unmanned aerial vehicles (UAVs) to enhance the capacity of the network for public safety and emergency communications, in case of damaged network infrastructure. In the third approach, we introduce a subsidization scheme for the service providers through which the network capacity can be improved by using regulatory power of the government. Finally, we consider the approach of device-to-device communications and multi-hop transmissions for enhancing the capacity of a network. Velocity estimation of high speed mobile users is important for effective mobility management in densely deployed small cell networks. In this research, we introduce two novel methods for the velocity estimation of mobile users: handover-count based velocity estimation, and sojourn time based velocity estimation. Using the tools from stochastic geometry and estimation theory, we theoretically analyze the accuracy of the two velocity estimation methods through Cramer-Rao lower bounds (CRLBs). With the dense deployment of small cells, energy efficiency becomes crucial for the sustained operation of wireless networks. In this research, we jointly study the energy efficiency and the spectral efficiency in a two-tier HetNet. We optimize the parameters of inter-cell interference coordination technique and study the trade-offs between the energy efficiency and spectral efficiency of the HetNet

Multi-UAV Allocation Framework for predictive crime deterrence and data acquisition

The recent decline in the number of police and security force personnel has raised a serious security issue that could lead to reduced public safety and delayed response to crimes in urban areas. This may be alleviated in part by utilizing micro or small unmanned aerial vehicles (UAVs) and their high-mobility on-board sensors in conjunction with machine-learning techniques such as neural networks to offer better performance in predicting times and places that are high-risk and deterring crimes. The key to the success of such operation lies in the suitable placement of UAVs. This paper proposes a multi-UAV allocation framework for predictive crime deterrence and data acquisition that consists of the overarching methodology, a problem formulation, and an allocation method that work with a prediction model using a machine learning approach. In contrast to previous studies, our framework provides the most effective arrangement of UAVs for maximizing the chance to apprehend offenders whilst also acquiring data that will help improve the performance of subsequent crime prediction. This paper presents the system architecture assumed in this study, followed by a detailed description of the methodology, the formulation of the problem, and the UAV allocation method of the proposed framework. Our framework is tested using a real-world crime dataset to evaluate its performance with respect to the expected number of crimes deterred by the UAV patrol. Furthermore, to address the engineering practice of the proposed framework, we discuss the feasibility of the simulated deployment scenario in terms of energy consumption and the relationship between data analysis and crime prediction

Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform

Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas

UAV-assisted edge infrastructure for challenged networks

Challenged Networks (CNs) are characterized by frequent varying network conditions and intermittent connectivity. In general, CNs emerge in different scenarios including the disaster and emergency situations when the traditional cellular infrastructure is dysfunctional or unavailable as well as in the undeserved areas such as rural and developing regions. This paper aims to evaluate the performance of a mobile edge infrastructure adopting Unmanned Aerial Vehicles (UAVs) for CN scenarios. Specifically, we assume that the UAVs can host micro Base Stations (BSs) and edge computing resources, which can be dynamically moved over the zones where the terrestrial mobile network is not properly working. After presenting the proposed UAV-mounted edge architecture, we propose a simple model to evaluate the performance in terms of coverage to users. Our preliminary results show that the UAV-based mobile edge architecture can guarantee a good coverage to users, even if the number of traditional BSs that are not working correctly is large

Incentivizing Spectrum Sharing Via Subsidy Regulations For Future Wireless Networks

Traditional regulatory methods for spectrum licensing have been recently identified as one of the causes for under-utilization of the valuable radio spectrum. Governmental regulatory agencies such as the Federal Communications Commission (FCC) are seeking ways to remove stringent regulatory barriers and facilitate broader access to the spectrum resources. The goal of such new FCC-backed efforts is to allow for an improved and ubiquitous sharing of the precious radio spectrum between commercial service providers. In this paper, an interdisciplinary framework for spectrum management is proposed in which the government, using its regulatory power, can motivate spectrum sharing among the service providers in order to gain a net social benefit. In this framework, a noncooperative game is used to analyze how to foster more sharing of the radio spectrum via the use of regulatory power. The providers are incentivized with subsidized spectrum bands from the regulators. In return, the providers will be asked to provide coverage to the users that are not subscribed to them so as to maintain their subsidy incentives from the government. In a simplification of the model, the providers’ perfect equilibrium strategies are found numerically, and the existence of perfect equilibrium for the government\u27s strategy is discussed. Our numerical results using real base station locations from two cellular providers show that through subsidization, the government can provide small service providers a fair chance to compete with the large providers, thereby avoiding monopolization in the market