Spatial Distribution Of Base Stations Research Articles

Modeling and Analysis of Multi-Relay Cooperative Communications in C-V2X Networks

To compensate for the limitations of existing dedicated short range communications (DSRC), cellular vehicle-to-everything (C-V2X) has been proposed recently, which is also a promising technology for future intelligent transportation systems (ITS). Using stochastic geometry approach, this paper presents the modeling and analysis of success probability in multi-relay cooperative C-V2X networks. The spatial distribution of base stations (BSs) and vehicles in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{2}$ </tex-math></inline-formula> are modeled as a 2D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP), respectively. We focus on the success probability of a source vehicle sending a message to the nearest destination vehicle assisted by the closest BS. Each vehicle is equipped with single antenna whereas each BS is equipped with multiple antennas, which act as independent relays. We consider two decoding schemes, i.e., selection combining (SC) and maximum ratio combing (MRC), and obtain the analytical expressions for joint success probability during two continuous time slots, taking into account the interference correlation (i.e., the spatial correlation of vehicle location). The analytical model is validated using Monte Carlo simulations in MATLAB, and the effects of major parameters on success probability are investigated.

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

This paper proposes novel and generalized expressions to characterize the performance of modern cellular networks under realistic user mobility behavior. The η-μ distribution is employed to derive the received power probability density function, the average bit error rate for different modulation schemes, and the coverage probability assuming a Poisson point process spatial distribution of base stations in downlink. The user is assumed to experience fading with Maximum Ratio Combining (MRC) and move according to a random way-point mobility model. The proposed expressions are applicable to different widely-used fading environments, such as Rayleigh and Nakagami-m as particular cases, by an appropriate selection of the η-μ parameters. Monte Carlo simulation was used to show the validity of the proposed expressions. The generalized expressions allow the system designer to quantify the effects of user mobility on the cellular network performance, in different propagation environments, and network topologies as a function of the number of base stations and MRC branches.

The impact of mobility model on handover rate in heterogeneous multi-tier wireless networks

Heterogeneous multi-tier wireless networks deploy many small cells to overcome the scarcity of bandwidth for broadband wireless access. However, increasing the number of cells increases the rate of required handover as a mobile user passes through the boundaries of these cells leads to more signaling overhead. We address the impact of mobile users’ mobility model in the urban areas on the handover rate. We use two real data sets to model the mobility pattern of pedestrians and drivers, considering their different velocity, move length, and pause time distributions. We use Poisson Point Processes to model the spatial distribution of base stations and assume that the number of handovers is equal to the number of times the users’ trajectories cross the cell’s boundaries. We derive the distribution function of the handover rate by using the distribution function of the velocity of users. Simulation results are provided to justify the deployed mobility model and the derived analytical results for the handover rate. We find that while the trend of the handover rate against the density of base stations and bias values at different tiers deploying the derived realistic mobility models and simple random waypoint model is the same, the total number of handovers in the network has different behavior. Interestingly, we found that if the number of users increases beyond a threshold, the handover rate decreases, suggesting that the hand over rate is bounded.

Stochastic Geometry Analysis of Sojourn Time in Multi-Tier Cellular Networks

Impact of mobility will be increasingly important in future generation wireless services and the related challenges will need to be addressed. Sojourn time, the time duration that a mobile user stays within a cell, is a mobility-aware parameter that can significantly impact the performance of mobile users and it can also be exploited to improve resource allocation and mobility management methods in the network. In this paper, we derive the distribution and mean of the sojourn time in multi-tier cellular networks, where spatial distribution of base stations (BSs) in each tier follows an independent homogeneous Poisson point process (PPP). To obtain the sojourn time distribution in multi-tier cellular networks with maximum biased averaged received power association, as the first step, we derive the area of contact, based on which we then derive the linear contact distribution function and chord length distribution of each tier. We also study the relation between mean sojourn time and other mobility-related performance metrics. We show that the mean sojourn time is inversely proportional to the handoff rate, and the complementary cumulative distribution function (CCDF) of sojourn time is bounded from above by the complement of the handoff probability. Moreover, we study the impact of user velocity and network parameters on the sojourn time.

Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

Spectrum management and compatibility studies with Python

Abstract. We developed the pycraf Python package, which provides functions and procedures for various tasks related to spectrum-management compatibility studies. This includes an implementation of ITU-R Rec. P.452 (ITU-R, 2015), which allows to calculate the path attenuation arising from the distance and terrain properties between an interferer and the victim service. A typical example would be the calculation of interference levels at a radio telescope produced from a radio broadcasting tower. Furthermore, pycraf provides functionality to calculate atmospheric attenuation as proposed in ITU-R Rec. P.676 (ITU-R, 2013).Using the rich ecosystem of scientific Python libraries and our pycraf package, we performed a large number of compatibility studies. Here, we will highlight a recent case study, where we analysed the potential harm that the next-generation cell-phone standard 5G could bring to observations at a radio observatory. For this we implemented a Monte-Carlo simulation to deal with the quasi-statistical spatial distribution of base stations and user devices around the radio astronomy station.

Spatial and Social Paradigms for Interference and Coverage Analysis in Underlay D2D Network

The homogeneous Poisson point process is widely used to model spatial distribution of base stations and mobile terminals. The same process can be used to model underlay device-to-device (D2D) network; however, neglecting homophilic relation for D2D pairing presents underestimated system insights. In this paper, we model both spatial and social distributions of interfering D2D nodes as proximity-based independently marked homogeneous Poisson point process. The proximity considers physical distance between D2D nodes whereas social relationship is modeled as Zipf-based marks. We apply these two paradigms to analyze the effect of interference on coverage probability of distance-proportional power-controlled cellular user. Effectively, we apply two types of functional mappings (physical distance, social marks) to Laplace functional of PPP. The resulting coverage probability has no closed-form expression; however, for a subset of social marks, the mark summation converges to digamma and polygamma functions. This subset constitutes the upper and lower bounds on coverage probability. We present numerical evaluation of these bounds on coverage probability by varying number of different parameters. The results show that by imparting simple power control on cellular user, ultradense underlay D2D network can be realized without compromising the coverage probability of cellular user.

Large-Scale Cellular Network Modeling From Population Data: An Empirical Analysis

Accurate estimates of the spatial distribution of base stations (BSs) are crucial for the analysis of core performance and connectivity metrics in cellular networks. The appropriate tuning of these models, particularly through the fitting of point processes (PPs), enables practical simulations and theoretical analyses with realistic network configurations. However, the commonly made assumption of stationarity in model parameters does not hold for larger networks, with especially population inhomogeneities causing large network structure variations. We use BS location information from different operators in Germany, to explore the efficacy and limitations of using population data as a covariate to approximate cellular deployments. Our analysis shows that the overall network density is highly correlated already for small areas. Furthermore, we find that for moderately populated areas, purely population-driven PPs yield a good statistical match to our real data. The validity boundaries discussed in this letter provide a useful reference for determining at which length scales, more complex, interaction-based, PPs are necessary.

No blind spot: network coverage enhancement through joint cooperation and frequency reuse

Both coordinated multi-point transmission and frequency reuse are effective approaches to mitigate inter-cell interference and improve network coverage. The motivation of this work is to explore the manner to effectively utilize the spectrum resource by reasonably combining cooperation and frequency reuse. The Matern cluster process, which is appropriate to model networks with hot spots, is used to model the spatial distribution of base stations. Two cooperative mechanisms, coherent and non-coherent joint transmission (JT), are analyzed and compared. We also evaluate the effect of multiple antennas and imperfect channel state information. The simulation reveals that the proposed approach to combine cooperation and frequency reuse is effective to improve the network coverage for users located at both the center and the boundary of the cooperative region.

On the Spatial Distribution of Base Stations and Its Relation to the Traffic Density in Cellular Networks

The spatial distribution of base stations (BSs) and traffic demands is essential for efficient network planning and BS sleeping, which are key elements of green cellular networking. This paper investigates their statistics, relation, and modeling, based on large-scale measurement data from commercial cellular networks. The spatial distribution of BSs shows not only high nonuniformity over a region but also diverse patterns in different regions, and thus the widely used homogeneous Poisson point process can only approximate the BS patterns in a specific small area. Therefore, the inhomogeneous PPP (IPPP), in particular, the Cox point process with spatially varying intensity is used to model the BS distribution over any spatial scale. To model the intensity distribution of the IPPP, we exploit the relation, shown to be sublinear, between the BS distribution and the peak hour (PH) traffic density, based on the finding that the PH traffic density can be approximated by a log-normal distribution. Finally, we propose a spatial modeling framework for network simulations, and discuss potential applications of the proposed spatial distribution model of the BS patterns and the PH traffic density.