Cellular Network Model Research Articles

Editorial: Third Quarter 2013, IEEE Communications Surveys & Tutorials

This issue includes twenty five articles covering different aspects of communication networks. In particular, issues related to energy efficient multimedia transmission, interference coordination in cellular networks, resource allocation and reservation, game theoretic approaches for spectrum access via carrier sensing, cognitive networks, biologically inspired algorithms for computer networking, stochastic geometry modeling for cellular and cognitive wireless networks, human activity recognition via wearable sensors, intrusion detection in sensor networks, source location privacy in sensor networks, indoor inertial position systems, cloud computing for smart mobile devices, content delivery in mobile networks, power savings in data centers, mobile social networks, vehicular networks, protocol stack for the Internet of Things, transition from IPv4 to IPv6, active queue management, Ethernet security, and context provisioning. A brief account for each of the articles included in this issue is given.

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations.

Discrete qualitative models of interaction networks

Logical models for cellular signaling networks are recently attracting wide interest: Their ability to integrate qualitative information at different biological levels, from receptor-ligand interactions to gene-regulatory networks, is becoming essential for understanding complex signaling behavior. We present an overview of Boolean modeling paradigms and discuss in detail an approach based on causal logical interactions that yields descriptive and predictive signaling network models. Our approach offers a mathematically well-defined concept, improving the efficiency of analytical tools to meet the demand of large-scale data sets, and can be extended into various directions to include timing information as well as multiple discrete values for components.

Systematic Verification of Upstream Regulators of a Computable Cellular Proliferation Network Model on Non-Diseased Lung Cells Using a Dedicated Dataset

We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.

Multicriteria Optimization of Cellular Networks

When designing modern cellular networks, it is challenging to account for many contradictory criteria and constantly changing external conditions of the networks (e.g., traffic). We need to solve multicriteria problems with high-dimensional vectors of parameters. A prerequisite to solution of these problems is correct determination of the feasible solution set, which is directly related to the statement of optimization problem. This is a major challenge in all multicriteria engineering optimization problems and represents significant difficulties for the expert. In this paper, we show how to define the feasible solution set for cellular network optimal design problems and thus answer the fundamental question of where to search for optimal solutions in such problems. We use the Parameter Space Investigation (PSI) method implemented in the Multicriteria Optimization and Vector Identification (MOVI) software system and apply it to a mathematical model of cellular network. In addition to developing methodology for stating and solving the problem of multicriteria optimization of cellular network, we have found that 1) defining the feasible solution set is directly related to the correct statement of the optimization problem, 2) once the feasible solution set has been determined, the criteria convolution can be applied to find the optimal solution in the feasible solution set, 3) it is possible to perform online tuning of the cellular network parameters.

Existence and attractivity of k-almost automorphic sequence solution of a model of cellular neural networks with delay

In this paper we discuss the existence and global attractivity of k-almost automorphic sequence solution of a model of cellular neural networks. We consider the corresponding difference equation analogue of the model system using suitable discretization method and obtain certain conditions for the existence of solution. Almost automorphic function is a good generalization of almost periodic function. This is the first paper considering such solutions of the neural networks.

Stochastic geometry based models for modeling cellular networks in urban areas

Recently a new approach to modeling cellular networks has been proposed based on the Poisson point process (PPP). Unlike the traditional, popular hexagonal grid model for the locations of base stations, the PPP model is tractable. It has been shown by Andrews et al. (in IEEE Trans Commun 59(11):3122---3134, 2011) that the hexagonal grid model provides upper bounds of the coverage probability while the PPP model gives lower bounds. In this paper, we perform a comprehensive comparison of the PPP and the hexagonal grid models with real base station deployments in urban areas worldwide provided by the open source project OpenCellID. Our simulations show that the PPP model gives upper bounds of the coverage probabilities for urban areas and is more accurate than the hexagonal grid model. In addition, we show that the Poisson cluster process is able to accurately model the base station location distribution.

Optimization of the (MAP1, MAP2)/(PH1, PH2)/N retrial queue model of wireless cellular networks with channel allocation

We consider a queueing model with N channels and an orbit with infinite waiting positions to model a specific cell in wireless networks. It is assumed that handoff and new calls have different arrival rates of Markov Arrival Process (MAP) and their channel holding time distributions are different Phase-type (PH) distributions. We analyze the Markov chain underlying the queueing system and obtain its ergodicity condition. Using matrix analytic techniques, we derive many performance measures such as the blocking probability of new calls and the dropping probability of handoff calls, the idle probability of the target cell, the mean queue lengths of two types of calls. We give numerical results to show the impact of system parameters on performance measures.

Boolean modeling in systems biology: an overview of methodology and applications

Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology.

Coverage and Economy of Cellular Networks with Many Base Stations

The performance of a cellular network can be significantly improved by employing many base stations (BSs), which shortens transmission distances. However, there exist no known results on quantifying the performance gains from deploying many BSs. To address this issue, we adopt a stochastic-geometry model of the downlink cellular network and analyze the mobile outage probability. Specifically, given Poisson distributed BSs, the outage probability is shown to diminish inversely with the increasing ratio between the BS and mobile densities. Furthermore, we analyze the optimal tradeoff between the performance gain from increasing the BS density and the resultant network cost accounting for energy consumption, BS hardware and backhaul cables. The optimal BS density is proved to be proportional to the square root of the mobile density and the inverse of the square root of the cost factors considered.

A Competitive Layer Model for Cellular Neural Networks

This paper discusses a Competitive Layer Model (CLM) for a class of recurrent Cellular Neural Networks (CNNs) from continuous-time type to discrete-time type. The objective of the CLM is to partition a set of input features into salient groups. The complete convergence of such networks in continuous-time type has been discussed first. We give a necessary condition, and a necessary and sufficient condition, which allow the CLM performance existence in our networks. We also discuss the properties of such networks of discrete-time type, and propose a novel CLM iteration method. Such method shows similar performance and storage allocation but faster convergence compared with the previous CLM iteration method (Wersing, Steil, & Ritter, 2001a). Especially for a large scale network with many features and layers, it can significantly reduce the computing time. Examples and simulation results are used to illustrate the developed theory, the comparison between two CLM iteration methods, and the application in image segmentation.

An accelerated-time simulation of car traffic on a motorway using a CAS

In this paper we introduce a new model, called the GRAM model, that provides an accelerated-time simulation of traffic on a motorway. The model provides an accelerated-time simulation using ideas and techniques from both cellular automata and neural network models. It has been implemented in a package using a Computer Algebra System (CAS) for the development of algorithms and a Java environment to display the simulated results graphically.The graphic interface shows the actual state of the cars in the system and provides visual information about the current situation of the traffic on the motorway. Therefore, this graphical approach can be used to help in the process of designing the traffic control system since the effects of making any change can be seen immediately.As an example of using GRAM, some simulations have been generated in a stretch of motorway where two different designs (with and without an accelerating lane) have been considered. As a result, it is possible to analyse the results of including an accelerating lane on the number of traffic jams on the motorway.

A study on semiflows generated by cooperative full‐range CNNs

ABSTRACTThe paper considers the full‐range (FR) model of cellular neural networks (CNNs) characterized by ideal hard‐limiter nonlinearities with two vertical segments in the current–voltage characteristic. It is shown that when the FRCNNs are cooperative, i.e., there are excitatory interconnections between distinct neurons, the generated solution semiflow is monotone and that monotonicity implies some fundamental restrictions on the geometry of omega‐limit sets. The result on monotonicity is a generalization to the class of differential inclusions describing the dynamics of FRCNNs of a classic result due to Kamke for cooperative ordinary differential equations. The paper also points out difficulties to use the standard theory of eventually strongly monotone (ESM) semiflows for addressing convergence of FRCNNs. By means of counterexamples, it is shown that, even assuming the irreducibility of the interconnections, the semiflow generated by a cooperative FRCNN is not ESM; furthermore, also the limit set dichotomy can be violated. Copyright © 2012 John Wiley & Sons, Ltd.

Controllability of Boolean control networks via the Perron–Frobenius theory

Boolean control networks (BCNs) are recently attracting considerable interest as computational models for genetic and cellular networks. Addressing control-theoretic problems in BCNs may lead to a better understanding of the intrinsic control in biological systems, as well as to developing suitable protocols for manipulating biological systems using exogenous inputs. We introduce two definitions for controllability of a BCN, and show that a necessary and sufficient condition for each form of controllability is that a certain nonnegative matrix is irreducible or primitive, respectively. Our analysis is based on a result that may be of independent interest, namely, a simple algebraic formula for the number of different control sequences that steer a BCN between given initial and final states in a given number of time steps, while avoiding a set of forbidden states.

Modeling and Analysis of K-Tier Downlink Heterogeneous Cellular Networks

Cellular networks are in a major transition from a carefully planned set of large tower-mounted base-stations (BSs) to an irregular deployment of heterogeneous infrastructure elements that often additionally includes micro, pico, and femtocells, as well as distributed antennas. In this paper, we develop a tractable, flexible, and accurate model for a downlink heterogeneous cellular network (HCN) consisting of K tiers of randomly located BSs, where each tier may differ in terms of average transmit power, supported data rate and BS density. Assuming a mobile user connects to the strongest candidate BS, the resulting Signal-to-Interference-plus-Noise-Ratio (SINR) is greater than 1 when in coverage, Rayleigh fading, we derive an expression for the probability of coverage (equivalently outage) over the entire network under both open and closed access, which assumes a strikingly simple closed-form in the high SINR regime and is accurate down to -4 dB even under weaker assumptions. For external validation, we compare against an actual LTE network (for tier 1) with the other K-1 tiers being modeled as independent Poisson Point Processes. In this case as well, our model is accurate to within 1-2 dB. We also derive the average rate achieved by a randomly located mobile and the average load on each tier of BSs. One interesting observation for interference-limited open access networks is that at a given SINR, adding more tiers and/or BSs neither increases nor decreases the probability of coverage or outage when all the tiers have the same target-SINR.

Construction of microbial cell factories for industrial bioprocesses

AbstractThe development of industrial microbial processes is gaining unprecedented momentum. Increased concern for environmental issues and the prospect of declining petroleum resources has shifted the industrial focus increasingly to microorganisms as biocatalysts. At the same time systems biology and synthetic biology supply industry and academia with new tools to design optimal microbial cell factories.Among the tools are systems biology approaches allowing the modelling of cellular networks for rational strain design, single cell analyses methods for gaining insight into population hetereogeneity, and an exciting combination of tools from structural biology and synthetic biology, permitting the catalysis of new (unnatural) enzymatic reactions or the production of new (unnatural) chemicals.This perspective article outlines recent advances and new developments within the field of microbial cell factory design. Copyright © 2012 Society of Chemical Industry

Analysis of Multiple Queue Model in Cellular Networks with Sub Rating of Channels

In cellular Networks we generally consider a single queue for each cell, some authors proposed a model with a dedicated queue for each transceiver in the cell. We have extended the idea of dedicated queue for each transceiver in the cell with sub-rating channels to improve the Quality of Service (QOS) of the system. In this paper we have compared three models, in model-I we used guard channels to give priority to handoff attempts and a buffer for finite size is provided to give priority to handoff data attempts, further in model-II we have taken sub-rating channel scheme (SCS). In subrating scheme a full rate channel is temporarily divided into two half rate channels in the blocked cell; one half rate channel serve the originating call and another serves handoff call. We proposed a dedicated queue for each transceiver in the cell with sub-rating in model-III. The Fixed Channel Assignment Scheme is considered for all models. The probabilities of handoff failure, blocking probability of new calls, forced termination of handoff calls, probability of noncompleted calls for all models are calculated for varying assumed of values arrival rate of new data calls, arrival rate of new voice calls, buffer size of channels and service rates. We compared and analyzed the numerical results to validate the proposed models.

Analysis of Cellular Wireless Networks with Non-Classical Traffic and Channel Reservation

This paper describes a traffic model for cellular wireless network with non-classical traffic (i.e., gamma inter-arrivals and general service times). Empirical studies have shown that electromagnetic propagation condition, irregular users’ behaviours, management policies, equipment failures, charging capacity of battery and so on have significant influence in call drops. Hence, the exponential assumption for both the inter-arrival and service times does not always hold true, as it does not represent the real dynamics involved in an operational network. The proposed Ek/G/C model takes into account the non-exponential inter-arrival time and service time distributions and gives a more pragmatic model for future cellular wireless networks. The performance of the proposed model of the cellular wireless network has been re-evaluated with channel reservation using both classical and non-classical traffic. The results show that non-classical model gives better approximation of the system performance in terms of the blocking probabilities.

Design and Simulation of a Proposed Location Management Model for Cellular Networks

Design and Simulation of a Proposed Location Management Model for Cellular Networks

Distributed approximation of cellular coverage

We consider the following model of cellular networks. Each base station has a given finite capacity, and each client has some demand and profit. A client can be covered by a specific subset of the base stations, and its profit is obtained only if its demand is provided in full. The goal is to assign clients to base stations, so that the overall profit is maximized subject to base station capacity constraints.In this work, we present a distributed algorithm for the problem, that runs in polylogarithmic time, and guarantees an approximation ratio close to the best known ratio achievable by a centralized algorithm.