Cellular Network Model Research Articles

Modeling and Analysis of Downlink Heterogeneous Cellular Networks

Owing to the irregular deployment of different types of base stations (BSs), cellular networks are now becoming more and more complex. In this study, a tractable and accurate framework based on hybrid model is proposed for heterogeneous cellular networks (HCNs). According to the distribution characteristics of different types of BSs in HCNs, the plane distributed macro BSs are termed as tier-1 cells, whereas the small cells, which are spatial distributed, are modeled as tier-2. First, we derive the probability of a target user communicate with macro BS or small cell. Second, we calculate the coverage probability that the target user communicate with macro or small cell respectively. Finally, we obtain the general closed expressions of whole coverage probability. In some special cases, the expressions present simple closed forms and can be calculated easily. Compared with the presented model, the simulation results show that our model is accurate within 1-2dB.

Global convergence of CNNs with neutral type delays and D operator

A model of cellular neural networks with neutral type delays and D operator is proposed. Applying appropriate differential inequality techniques, several sufficient conditions are derived to ensure...

Theoretical Insights into Coverage Analysis of Cellular Networks

Recently, tools from stochastic geometry have gained much attention for modeling and analysis of dense cellular networks. Although, extensive studies are available in literature in this respect however, the approaches are generalized and often lack significance towards practical scenarios where network conditions vary dynamically. The main objective of this research is to provide some insights into the stochastic geometry based analysis of cellular networks, through suitable modifications, so that it can be used to estimate parameters of interest i.e., intensity and coverage probability for different tiers and scenarios under consideration. The main definition for probability of coverage has been re-defined and complete closed form expression is derived. The intensity for different tiers have been estimated, with the help of proposed approach, beyond which no more gain in coverage probability can be achieved. Monte-Carlo simulations have been performed for validation of proposed approach and results are also compared with state-of-the-art approach.

Unified Stochastic Geometry Modeling and Analysis of Cellular Networks in LOS/NLOS and Shadowed Fading

Statistical characterization of the signal-to-interference-plus-noise ratio (SINR) via its cumulative distri- bution function is ubiquitous in a vast majority of technical contributions in the area of cellular networks, since it boils down to averaging the Laplace transform of the aggregate interference, a benefit accorded at the expense of confinement to the simplistic Rayleigh fading. In this paper, to capture diverse fading channels that arise in realistic outdoor/indoor wireless communication scenarios, we tackle the problem differently. By exploiting the moment generating function of the SINR, we succeed in analytically assessing cellular networks performance over the shadowed $\kappa$ – $\mu$ , $\kappa$ – $\mu$ , and $\eta$ – $\mu$ fading models. These channel models offer high flexibility by capturing diverse fading channels, including Rayleigh, Nakagami- $m$ , Rician, and Rician shadow fading distributions. These channel models have been recently promoted for their capability to accurately model dense urban environments, future femtocells, and device-to-device shadowed channels. In addition to unifying the analysis for different channel models, this paper integrates the coverage, the achievable rate, and the bit error probability, which are largely treated separately in the literature. The developed model and analysis are validated over a broad range of simulation setups and parameters.

Large deviations in relay-augmented wireless networks

We analyze a model of relay-augmented cellular wireless networks. The network users, who move according to a general mobility model based on a Poisson point process of continuous trajectories in a bounded domain, try to communicate with a base station located at the origin. Messages can be sent either directly or indirectly by relaying over a second user. We show that in a scenario of an increasing number of users, the probability that an atypically high number of users experiences bad quality of service over a certain amount of time decays at an exponential speed. This speed is characterized via a constrained entropy minimization problem. Further, we provide simulation results indicating that solutions of this problem are potentially nonunique due to symmetry breaking. Also, two general sources for bad quality of service can be detected, which we refer to as isolation and screening.

Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain.

One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties. This study deals with a four-unit neuroglial cellular network model where each unit incorporates pyramidal cells, interneurons, and astrocytes. Three different pathways of hyperexcitability generation-Na - ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel-were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration, and CFC were then measured and analyzed. Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz). Longer duration SEDs exhibit CFC features similar to those reported by our team. First, Identifying the exponential relationship between network excitability and SED durations; second, highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium); and third, elucidation of the biophysical basis for CFC coupling features.

Modeling and performance analysis for mobile cognitive radio cellular networks

In this paper, teletraffic performance and channel holding time characterization in mobile cognitive radio cellular networks (CRCNs) under fixed-rate traffic with hard-delay constraints are investigated. To this end, a mathematical model to capture the effect of interruption of ongoing calls of secondary users (SUs) due to the arrival of primary users (PUs) is proposed. The proposed model relies on the use of an independent potential interruption time associated with the instant of possible interruption for each ongoing call in every visited cell. Then, a Poisson process is used to approximate the secondary users’ call interruption process due to the arrival of PUs. Based on this model and considering that unencumbered service time (UST) and cell dwell time (CDT) of SUs are independent generally distributed random variables, analytical formulae for both the probability distributions of channel holding times and inter-cell handoff attempts rate are derived. Also, a novel approximated closed-form mathematical expression for call forced termination probability of SUs is derived under the restriction that the UST is exponentially distributed. Additionally, by considering all the involved time variables exponentially distributed and employing fractional channel reservation to prioritize intra- and inter-cell handoff call attempts over new call requests, a queuing analysis to evaluate the call-level performance of CRCNs in terms of the maximum Erlang capacity is developed. The accuracy of our proposed mathematical models is extensively investigated under a variety of different evaluation scenarios for all the considered call-level performance metrics. Numerical results demonstrate that channel holding time statistics are highly sensitive to both interruption probability of ongoing secondary calls and type of probability distribution functions used to model CDT and UST. From the teletraffic perspective, numerical results reveal that the system Erlang capacity largely depends on the relative value of the mean secondary service time to the mean primary service time and the primary channels’ utilization factor. Also, the obtained results show that there exists a critical utilization factor of the primary resources from which it is no longer possible to guarantee the required quality of service of SUs and, therefore, services with hard-delay constraints cannot be even supported in CRCNs.

Models of Models: A Translational Route for Cancer Treatment and Drug Development.

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Cooperative Content Download-and-Share: Motivating D2D in Cellular Networks

Device-to-device (D2D) communications is regarded as a promising technology for future cellular networks, which can significantly improve both the network spectral efficiency and individual user experience. However, the lack of motivation to utilize the D2D links hinders the deployment of D2D communications in cellular networks. We study the cooperative downloading problem via D2D communications, and propose a scheme, termed as cooperative content download-and-share (CoCoDaS), to stimulate the demand of D2D communications by a simple pricing model in cellular networks, in order to offload traffic burden from base station. In CoCoDaS, the users with the same content demand cooperatively download a large-sized file from the base station (BS) and share their downloaded segments of such content with each other via D2D communications. Simulations verify the efficiency of our proposed CoCoDaS scheme in terms of the reduction of the BS’s burden on data services and the utilization of D2D communications.

Exponential Stability and Periodic Solutions of Impulsive Neural Network Models with Piecewise Constant Argument

In this paper we introduce an impulsive cellular neural network models with piecewise alternately advanced and retarded argument. The model with the advanced argument is system with strong anticipation. Some sufficient conditions are established for the existence and global exponential stability of a unique periodic solution. The approaches are based on employing Banach’s fixed point theorem and a new integral inequality of Gronwall type with impulses and deviating arguments. The criteria given are easily verifiable, possess many adjustable parameters, and depend on impulses and piecewise constant argument deviations, which provides flexibility for the design and analysis of cellular neural network models. Several numerical examples and simulations are also given to show the feasibility and effectiveness of our results.

Cellular non-linear network model of microbial fuel cell

A cellular non-linear network (CNN) is a uniform regular array of locally connected continuous-state machines, or nodes, which update their states simultaneously in discrete time. A microbial fuel cell (MFC) is an electro-chemical reactor using the metabolism of bacteria to drive an electrical current. In a CNN model of the MFC, each node takes a vector of states which represent geometrical characteristics of the cell, like the electrodes or impermeable borders, and quantify measurable properties like bacterial population, charges produced and hydrogen ion concentrations. The model allows the study of integral reaction of the MFC, including temporal outputs, to spatial disturbances of the bacterial population and supply of nutrients. The model can also be used to evaluate inhomogeneous configurations of bacterial populations attached on the electrode biofilms.

Stochastic geometry modeling and energy efficiency analysis of millimeter wave cellular networks

Current wireless networks face unprecedented challenges because of the exponentially increasing demand for mobile data and the rapid growth in infrastructure and power consumption. This study investigates the optimal energy efficiency of millimeter wave (mmWave) cellular networks, given that these networks are some of the most promising 5G-enabling technologies. Based on the stochastic geometry, a mathematical framework of coverage probability is proposed and the optimal energy efficiency is obtained with coverage performance constraints. Numerical results show that increasing the base station density damages coverage performance exceeding the threshold. This work demonstrates an essential understanding of the deployment and dynamic control of energy-efficient mmWave cellular networks.

Secure massive MIMO‐enabled full‐duplex 2‐tier heterogeneous networks by exploiting in‐band wireless backhauls

AbstractTo enhance the spectrum efficiency and secrecy performance, by using in‐band full‐duplex (FD) wireless backhaul technique, a secure massive multiple input multiple output (MIMO)–enabled heterogeneous cellular network (HCN) model is presented, where the macro base stations (MBSs) are equipped with massive MIMO antenna array, whereas the small cell base stations (SBSs) and user equipment have the single antenna. The ultradense small cells underlay the macro cells and offload traffic from macro cells. With FD mode, the massive MIMO MBSs and single‐antenna SBSs are responsible, respectively, for the access traffic of macro and small cells as well as the backhaul transmission between MBSs and SBSs over the same time‐frequency resource. The transmissions of macro and small cells are exposed to non‐colluding and passive eavesdroppers that attempt to decode the signals of legitimate users. For such HCN with in‐band wireless backhaul, by modeling the network elements as the independent Possion point processes and using the stochastic geometry, we first investigate the secrecy probabilities of the macro cell downlink and small cell uplink transmissions. At the same time, to achieve a comprehensive view, we also investigate the corresponding signal‐interference‐noise ratio (SINR) coverage performance. The presented numerical results show that when the intensity of user equipment is small relatively, exploiting the in‐band backhaul interference as jamming signals can enhance the secrecy probabilities of both the macro and small cell transmissions so that the in‐band FD backhaul mode outperforms the out‐band FD one. However, for the SINR coverage, the opposite results are achieved. That is to say, the in‐band backhaul transmission degrades the SINR coverage probabilities because of the existence of wireless backhaul interference. Besides of these, in numerical analysis, we also investigate the effect of the network parameters on the secrecy and coverage performance of both the macro and small cells. The derived results can be used for guiding the design of HCNs.

Energy Efficiency of Multiuser Multiantenna Random Cellular Networks With Minimum Distance Constraints

Compared with conventional regular hexagonal cellular models, random cellular network models resemble real cellular networks much more closely. However, most studies of random cellular networks are based on the Poisson point process and do not take into account the fact that adjacent base stations (BSs) should be separated with a minimum distance to avoid strong interference among each other. In this paper, based on the hard core point process (HCPP), we propose a multi-user multi-antenna random cellular network model with the aforementioned minimum distance constraint for adjacent BSs. Taking into account the effects of small scale fading and shadowing, interference and capacity models are derived for the multi-user multi-antenna HCPP random cellular networks. Furthermore, a spectrum efficiency model as well as an energy efficiency model is presented, based on which, the maximum achievable energy efficiency of the considered multi-user multiantenna HCPP random cellular networks is investigated. Simulation results demonstrate that the energy efficiency of conventional Poison point process (PPP) cellular networks is underestimated when the minimum distance between adjacent BSs is ignored.

Weighted Pseudo Anti‐Periodic Solutions for Cellular Neural Networks with Mixed Delays

AbstractA model of cellular neural networks with mixed delays is proposed. Applying contraction mapping fixed point theorem and differential inequality technique, several sufficient conditions are derived to ensure the existence and exponential stability of weighted pseudo anti‐periodic solutions for the proposed neural networks. Finally, a numerical simulation example is given to illustrate the effectiveness of the obtained results.

A Probabilistic Distance-Based Modeling and Analysis for Cellular Networks With Underlaying Device-to-Device Communications

Device-to-device (D2D) communications in cellular networks are promising technologies for improving network performance. However, they may cause severe intra/inter-cell interference that can considerably degrade the performance of cellular users, and vice versa. Therefore, interference analysis has been one of the most important research topics in such a system. Focusing on an uplink resource reusing scenario, this paper presents a framework based on a probabilistic distance and path-loss model to obtain the distributions of signal, interference, and further Signal-to-Interference-plus-Noise Ratio (SINR), based on which, the performance metrics that are functions of SINR can be analyzed, such as outage probability and capacity. Different from the previous work, this proposed framework: 1) obtains interference and SINR distributions for both cellular and D2D communications, through which insights into their performance metrics and mutual influence are provided and 2) has no limitations on cell shapes, except that they are approximated by polygons or circles. The framework can also be applied to a downlink reusing scenario. Our results indicate that the developed framework is helpful for network planners to effectively tune the network parameters, and thus to achieve the optimum system performance for both cellular and D2D communications.

Call Admission Scheme for Multidimensional Traffic Assuming Finite Handoff User

Usually, the number of users within a cell in a mobile cellular network is considered infinite; hence,M/M/n/kmodel is appropriate for new originated traffic, but the number of ongoing calls around a cell is always finite. Hence, the traffic model of handoff call will beM/M/n/k/N. In this paper, aK-dimensional traffic model of a mobile cellular network is proposed using the combination of limited and unlimited users case. A new call admission scheme (CAS) is proposed based on both thinning scheme and fading condition. The fading condition of the wireless channel access to a handoff call is prioritized compared to newly originated calls.

Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial

This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a basic cellular network model. Then, it characterizes signal-to-interference-plus-noise-ratio (SINR) and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and rate analysis is presented. Although the main focus of the paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. The paper then extends the baseline unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. Finally, we point out future research directions.

A Unified Stochastic Geometry Model for MIMO Cellular Networks With Retransmissions

This paper presents a unified mathematical paradigm, based on stochastic geometry, for downlink cellular networks with multiple-input-multiple-output (MIMO) base stations. The developed paradigm accounts for signal retransmission upon decoding errors, in which the temporal correlation among the signal-to-interference-plus-noise ratio (SINR) of the original and retransmitted signals is captured. In addition to modeling the effect of retransmission on the network performance, the developed mathematical model presents twofold analysis unification for the MIMO cellular networks literature. First, it integrates the tangible decoding error probability and the abstracted (i.e., modulation scheme and receiver type agnostic) outage probability analysis, which are largely disjoint in the literature. Second, it unifies the analysis for different MIMO configurations. The unified MIMO analysis is achieved by abstracting unnecessary information conveyed within the interfering signals by Gaussian signaling approximation along with an equivalent SISO representation for the per-data stream SINR in the MIMO cellular networks. We show that the proposed unification simplifies the analysis without sacrificing the model accuracy. To this end, we discuss the diversity-multiplexing tradeoff imposed by different MIMO schemes and shed light on the diversity loss due to the temporal correlation among the SINRs of the original and retransmitted signals. Finally, several design insights are highlighted.

A novel handoff algorithm for hierarchical cellular networks

In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks. First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow. Then, according to the mobility classification, network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.