Stochastic Geometry Modeling of Cellular Networks: Analysis, Simulation and Experimental Validation

Due to the increasing heterogeneity and deployment density of emerging cellular networks, new flexible and scalable approaches for their modeling, simulation, analysis and optimization are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance evaluation and optimization. In this paper, we investigate the accuracy of this emerging abstraction for modeling cellular networks, by explicitly taking realistic base station locations, building footprints, spatial blockages and antenna radiation patterns into account. More specifically, the base station locations and the building footprints are taken from two publicly available databases from the United Kingdom. Our study confirms that the abstraction model based on stochastic geometry is capable of accurately modeling the communication performance of cellular networks in dense urban environments.Comment: submitted for publicatio

Wireless Body Area Networking: Joint Physical-Networking Layer Simulation and Modeling

An electronic device equipped with sensors and antennas is the main part of the wireless body area networking (WBAN). Such a device is placed near human body and it usually works in a populated environment with many surrounding objects (e.g., building walls). The human body and the objects can change the radiation characteristics of the antenna and impact the performance of the wireless communication system. The wireless communication system’s performance is also affected by the networking layers established on top of the physical layer. Therefore, any designing method for WBAN application should be pervasive, offering a joint physical-networking layer simulation and modeling strategy. To this end, in this chapter, a comprehensive simulation and modeling method is presented. First, antenna design limitations and challenges for wireless body area networking are studied with emphasis on evaluating the antenna’s performance near the human body. Then, the antenna miniaturization techniques to reduce the antennas’ dimension are reviewed. Later, a system level analysis and modeling are used to study short-range communication between the wearable antennas with remote nodes using IEEE 802.11g wireless networking protocol

Modeling network-controlled device-to-device communications in SimuLTE

In Long Term Evolution-Advanced (LTE-A), network-controlled device-to-device (D2D) communications allow User Equipments (UEs) to communicate directly, without involving the Evolved Node-B in data relaying, while the latter still retains control of resource allocation. The above paradigm allows reduced latencies for the UEs and increased resource efficiency for the network operator, and is therefore foreseen to support several services, from Machine-to-machine to vehicular communications. D2D communications introduce research challenges that might affect the performance of applications and upper-layer protocols, hence simulations represent a valuable tool for evaluating these aspects. However, simulating D2D features might pose additional com-putational burden to the simulation environment. To this aim, a careful modeling is required in order to reduce computational overhead. In this paper we describe our modeling of net-work-controlled D2D communications in SimuLTE, a system-level LTE-A simulation library based on OMNeT++. We describe the core modeling choices of SimuLTE, and show how these allow an easy extension to D2D communications. Moreover, we describe in detail the modeling of specific problems arising with D2D communications, such as scheduling with frequency reuse, connection mode switching and broadcast transmission. We document the computational efficiency of our modeling choices, showing that simulation of D2D communications is not more complex than simulation of classical cellular communications of comparable scale. Results show that the heaviest computational burden of D2D communication lies in estimating the Sidelink channel quality. We show that SimuLTE allows one to evaluate the interplay between D2D communication and end-to-end performance of UDP- and TCP-based services. Moreover, we assess the accuracy of using a binary interference model for frequency reuse, and we evaluate the trade-off between speed of execution and accuracy in modeling the reception probability

EARLY PERFORMANCE PREDICTION METHODOLOGY FOR MANY-CORES ON CHIP BASED APPLICATIONS

Modern high performance computing applications such as personal computing, gaming, numerical simulations require application-specific integrated circuits (ASICs) that comprises of many cores. Performance for these applications depends mainly on latency of interconnects which transfer data between cores that implement applications by distributing tasks. Time-to-market is a critical consideration while designing ASICs for these applications. Therefore, to reduce design cycle time, predicting system performance accurately at an early stage of design is essential. With process technology in nanometer era, physical phenomena such as crosstalk, reflection on the propagating signal have a direct impact on performance. Incorporating these effects provides a better performance estimate at an early stage. This work presents a methodology for better performance prediction at an early stage of design, achieved by mapping system specification to a circuit-level netlist description. At system-level, to simplify description and for efficient simulation, SystemVerilog descriptions are employed. For modeling system performance at this abstraction, queueing theory based bounded queue models are applied. At the circuit level, behavioral Input/Output Buffer Information Specification (IBIS) models can be used for analyzing effects of these physical phenomena on on-chip signal integrity and hence performance. For behavioral circuit-level performance simulation with IBIS models, a netlist must be described consisting of interacting cores and a communication link. Two new netlists, IBIS-ISS and IBIS-AMI-ISS are introduced for this purpose. The cores are represented by a macromodel automatically generated by a developed tool from IBIS models. The generated IBIS models are employed in the new netlists. Early performance prediction methodology maps a system specification to an instance of these netlists to provide a better performance estimate at an early stage of design. The methodology is scalable in nanometer process technology and can be reused in different designs

Towards Data-driven Simulation of End-to-end Network Performance Indicators

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations

Nonlinear Black-Box Models of Digital Integrated Circuits via System Identification

This Thesis concerns the development of numerical macromodels of digi- tal Integrated Circuits input/output buffers. Such models are of paramount importance for the system-level simulation required for the assessment of Sig- nal Integrity and Electromagnetic Compatibility effects in high-performance electronic equipments via system-level simulations. In order to obtain accurate and efficient macromodels, we concentrate on the black-box modeling approach, exploiting system identification methods. The present study contributes to the systematic discussion of the IC mod- eling process, in order to obtain macromodels that can overcome strengths and limitations of the methodologies presented so far. The performances of different parametric representations, as Sigmoidal Basis Functions (SBF) ex- pansions, Echo State Networks (ESN) and Local Linear State-Space (LLSS) models are investigated. All representations have proven capabilities for the modeling of unknown nonlinear dynamic systems and are good candidates too be used for the modeling problem at hand. For each model representation, the most suitable estimation algorithm is considered and a systematic analy- sis is performed to highlight advantages and limitations. For this analysis, the modeling process is applied to a synthetic nonlinear device representative of IC ports, and designed to generate stiff responses. The tests carried out show that LLSS models provide the best overall performance for the modeling of digital devices, even with strong nonlinear dynamics. LLSS models can be estimated by means of an efficient algorithm providing a unique solution. Local stability of models is preconditioned and verified a posteriori. The effectiveness of the modeling process based on LLSS representations is verified by applying the proposed technique to the modeling of real devices involved in a realistic data communication link (an RF-to-Digital interface used in mobile phones). The obtained macromodels have been successfully used to predict both the functional signals and the power supply and ground fluctuations. Besides, they turn out to be very efficient, providing a signifi- cant simulation speed-up for the complete data link