Random Cellular Networks Research Articles

Approximation for probability of coverage in softly inhibitive cellular networks

AbstractThe Strauss point process is a very popular model for describing the random cellular networks, yet several key statistical properties such as intensity, empty space function, and probability generating functional have remained elusive. This article addresses these issues by first leveraging the Poisson saddle point method to approximate the distance‐conditioned intensity for Strauss point processes. Subsequently, the author derives an analytically tractable expression for the distribution of empty space distance based on a conditional thinning mechanism. Additionally, the author establishes an upper bound for the probability generating functional in Strauss point processes, which is crucial for evaluating the Laplace transform of cumulative interference in relevant cellular networks. These findings facilitate the systematic derivation of spatially averaged probability of coverage, and the accuracy of analytic results is validated through simulations.

Physical Layer Security in Multi-Antenna Cellular Systems: Joint Optimization of Feedback Rate and Power Allocation

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results.

An Energy-Efficient Model of Random Cognitive Radio Network: Rayleigh-Lognormal Environment

Motivated by the current demand for improvements in transmission rate and energy efficiency of random wireless cellular networks, we investigate the theoretical model of random cognitive radio network in Rayleigh-lognormal fading environment. In such a network, we derive an analytical expression for the connection probability, transmission rate, and energy efficiency of a secondary network in a single-tier downlink scenario, considering the probabilities of unoccupied channel selection and of successful transmission, where source-destination pairs are randomly located according to Poisson point processes. Moreover, we approach the problem of optimization of transmission rate and energy efficiency using a required connection probability constraint to improve the system performance. Our numerical results indicate that there exists an optimal combination of transmission power and secondary transmitter density where transmission rate and energy efficiency are maximized.

Mean almost periodicity and moment exponential stability of semi-discrete random cellular neural networks with fuzzy operations.

By using the semi-discretization technique of differential equations, the discrete analogue of a kind of cellular neural networks with stochastic perturbations and fuzzy operations is formulated, which gives a more accurate characterization for continuous-time models than that by Euler scheme. Firstly, the existence of at least one p-th mean almost periodic sequence solution of the semi-discrete stochastic models with almost periodic coefficients is investigated by using Minkowski inequality, Hölder inequality and Krasnoselskii’s fixed point theorem. Secondly, the p-th moment global exponential stability of the semi-discrete stochastic models is also studied by using some analytical skills and the proof of contradiction. Finally, a problem of stochastic stabilization for discrete cellular neural networks is studied.

랜덤 셀룰러 네트워크에서의 셀 주밍 및 절전 기법을 이용한 아웃티지 성능 개선

셀 주밍(cell zooming)은 기지국 송신 전력 세기를 조절함으로써 셀룰러 네트워크의 트래픽 분산과 에너지 효율 향상을 도모하는 기법이다. 본 논문에서는 셀 주밍과 절전 기법을 푸아송 보로노이 테셀레이션(Poisson Voronoi Tessellation, PVT) 랜덤 셀룰러 네트워크(random cellular network)에 적용하여 네트워크의 아웃티지 성능을 향상시킬 수 있는 알고리즘을 제안한다. 제안하는 알고리즘은 기지국 중심의 분산된 형태이며 작동에 적은 양의 네트워크 상태 정보만을 필요로 한다. 기지국들은 각각 사용자들에게 제공하고 있는 서비스 품질에 따라 상태 정보를 분류하고, 인접 기지국들 간의 정보 교환을 통해 셀 주밍 및 절전 기법을 적용한다. 시뮬레이션 결과는 제안하는 알고리즘이 아웃티지 발생을 줄일 수 있음을 보여준다. 아웃티지 감소 성능은 얻고자 하는 에너지 효율개선 정도가 클수록 감소하는데, 알고리즘 적용 전과 비교하여 에너지 효율이 감소하지 않는 구간에서 최소 50%의 아웃티지 감소 성능을 보인다.

A Model Based Poisson Point Process for Downlink Cellular Networks Using Joint Scheduling

This paper proposes a model based on a random cellular network to analyse performance of Joint Scheduling in which a typical user measures signal-to-interference-plus-noise ratio (SINR) on different resource blocks from K nearest BSs in order to find out the BS with the highest SINR to establish communication. The paper derives the general form of average coverage probability of a typical user in the case of $$K>2$$ and its close-form expression in the case of $$K=2$$ . The analytical results which are verified by Monte Carlo simulation indicates that (1) using the Joint Scheduling can improve the user’s performance up to $$34.88 \%$$ in the case of the path loss exponent $$\alpha = 3$$ ; (2) the effect of the density of BSs on the user association probability is infinitesimal.

Analysis of Blockage Sensing by Radars in Random Cellular Networks

We characterize the detection probability of blockage sensing by radars deployed on towers in cellular networks. If the signal-to-interference ratio of the reflected pilot signal is larger than the predefined threshold and there is no other blockage between the radar and the corresponding blockage, the radar successfully detects the blockage. Modeling radar and blockage locations using stochastic geometry, we derive the detection probability as a function of the system parameters, chiefly the radar and blockage densities. Leveraging the obtained expression, we provide some guidelines for efficiently deploying radars to enhance the detection probability.

Energy Harvesting and Cell Zooming in $K-$ Tier Heterogeneous Random Cellular Networks

In this paper, we study the performance of ${K}$ –tier heterogeneous random cellular networks (HetNets) in terms of energy efficiency (EE) and spectral efficiency (SE). In order to improve efficiency, two cell zooming (CZ) techniques, namely telescopic and ON/OFF schemes, are applied to these networks. Accordingly, we investigate two scenarios in this paper. First, we focus on a single tier scenario and derive a new expression for the ergodic capacity of a Poisson–Voronoi tessellation random cellular network using stochastic geometry. Besides, the EE and SE of this model are depicted for several user densities and CZ parameters. In the second scenario, we investigate multitier HetNets in which the base stations gather their required energy from the environment. Therefore, two important concepts in green cellular networks, namely CZ and energy harvesting, are addressed in this paper. Subsequently, numerical results show the performance improvement in terms of the coverage and blocking probabilities in addition to EE and SE.

Performance analysis of fractional frequency reuse in uplink random cellular networks

In this paper, we develop a network model based on 3GPP standards to analyse the performance of the uplink random cellular network using Frequency Reuse (FR) algorithms. The operation of FR is separated into two phases in which the Base Station (BS) measures the uplink Signal-Interference-plus-Noise Ratio (SINR) to classify each user into either Cell-Centre User (CCU) or Cell-Edge User (CEU) during the establishment phase. This is followed by the data transfer process between the user and its serving BS during the communication phase. Compared with the related works, we propose the following novel approaches: (i) we define the two-phase operation for both CCU and CEU; (ii) the density of interfering users causing interference to the CEU under Strict FR is inversely proportional to a FR factor; (iii) the interference originating from CCUs and CEUs are evaluated separately. Although Strict FR provides more benefits for the user such as low power consumption and higher performance than Soft FR, the network using Soft FR can achieve a significantly higher cell data rate which is up to 58.96% higher than that using Strict FR. A very interesting phenomenon is found in this paper for a sparse Strict FR network with the density of BSs λ=0.1BS∕km2 in which the average uplink SINR of the user during establishment phase increases with the power control exponent while the corresponding average data rate of the CCU during the communication phase reduces. The paper also derives the approximation analytical approach using Gaussian Quadratures to obtain the close-form expressions of user’s performance.

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.

A Tele-Traffic-Aware Optimal Base-Station Deployment Strategy for Energy-Efficient Large-Scale Cellular Networks

With the explosive proliferation of mobile devices and services, the user density of large-scale cellular networks continues to increase, which results in further escalating traffic generation. Yet, there is an urging demand for reducing the network’s energy dissipation. Hence, we model the energy efficiency of large-scale cellular networks for characterizing its dependence on the base station (BS) density as well as for quantifying the impact of tele-traffic on the achievable energy efficiency under specific quality of service requirements. This allows us to match the BS deployment to the network’s tele-traffic while conserving precious energy. More specifically, we formulate a practical tele-traffic-aware BS deployment problem for optimizing the network’s energy efficiency while satisfying the users’ maximum tolerable outage probability. This is achieved by analyzing the optimal BS-density under specific tele-traffic conditions. Furthermore, we study the energy saving potential of our optimal BS deployment strategy under diverse practical parameters and provide insights into the attainable energy savings in dense random cellular networks. Our simulation results confirm the accuracy of our analysis and verify the impact of the parameters considered on the network’s energy efficiency. Our results also demonstrate that the proposed tele-traffic-aware optimal BS deployment strategy significantly outperforms the existing approaches in terms of its energy efficiency.

5G green cellular networks considering power allocation schemes

It is important to assess the effect of transmit power allocation schemes on the energy consumption on random cellular networks. The energy efficiency of 5G green cellular networks with average and water-filling power allocation schemes is studied in this paper. Based on the proposed interference and achievable rate model, an energy efficiency model is proposed for MIMO random cellular networks. Furthermore, the energy efficiency with average and water-filling power allocation schemes are presented, respectively. Numerical results indicate that the maximum limits of energy efficiency are always there for MIMO random cellular networks with different intensity ratios of mobile stations (MSs) to base stations (BSs) and channel conditions. Compared with the average power allocation scheme, the water-filling scheme is shown to improve the energy efficiency of MIMO random cellular networks when channel state information (CSI) is attainable for both transmitters and receivers.

On modeling coverage and rate of random cellular networks under generic channel fading

In this paper we provide an analytic framework for computing the expected downlink coverage probability, and the associated rate of cellular networks, where base stations are distributed in a random manner . The provided expressions are in computable integral forms that accommodate generic channel fading conditions. We develop these expressions by modeling the cellular interference using stochastic geometry analysis, then we employ them for comparing the coverage resulting from various channel fading conditions namely Rayleigh and Rician fading, in addition to the fading-less channel . Furthermore, we expand the work to accommodate the effects of random frequency reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted to validate the theoretical analysis, where the results show a very close match.

Spatial Spectrum and Energy Efficiency of Random Cellular Networks

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.

Energy Efficient Scheme for Cellular Network Using G-Leach

This paper proposes an energy efficient scheme for cellular network using G-LEACH (Genetic Low Energy Adaptive Clustering Hierarchy). By using the genetic algorithm based upon adaptive clustering protocol the energy consumption of the every node can be reduced and then the lifetime of each node in the cellular network can be increased. The proposed algorithm is compared with LEACH (Low -Energy Adaptive Clustering Hierarchy). The G-LEACH is implemented by using three phase. They are preparation phase, set-up phase, and at last steady-state phase. The preparation phase is the initial phase of the algorithm, in this phase the cluster head extraction process is executed by within all the random cellular network nodes. All nodes send the condition and position within the cluster as a resubmit message to the base station of the cellular network. By using the resubmit message base station exploring the node as cluster head which has the most eminent energy for foster process. By using this phase the energy consumption of the node is reduced. In the set-up phase the base station disseminate the message to all the node and help the random node to form a cluster within the cellular network. At last the steady-state phase is executed merely once ahead the set-up phase in the process of cellular network. Simulation result shows the comparison between LEACH and G-LEACH and energy consumed by the each node in the cellular network

Downlink Performance Analysis for a Generalized Shotgun Cellular System

In this paper, we analyze the signal-to-interference-plus-noise ratio (SINR) performance at a mobile station (MS) in a random cellular network. The cellular network is formed by base stations (BSs) placed in a one-, two-, or three-dimensional space according to a possibly non-homogeneous Poisson point process, which is a generalization of the so-called shotgun cellular system. We develop a sequence of equivalence relations for the SCSs and use them to derive semi-analytical expressions for the coverage probability at the MS when the transmissions from each BS may be affected by random fading with arbitrary distributions as well as attenuation following arbitrary path-loss models. For homogeneous Poisson point processes in the interference-limited case with power-law path-loss model, we show that the SINR distribution is the same for all fading distributions and is not a function of the base station density. In addition, the influence of random transmission power, power control, and multiple channel reuse groups on the downlink performance is also discussed. The techniques developed for the analysis of SINR have applications beyond cellular networks and can be used in similar studies for cognitive radio networks, femtocell networks, and other heterogeneous and multi-tier networks.

Hierarchical random cellular neural networks for system-level brain-like signal processing

Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain’s dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms.

Analysis of the insulation capability of polyester insulator

The lifetime of polyester resin samples has been determined with the ASTM D2303 inclined plane tracking test method (IPTTM) for different environmental effects. Most of the external factors have a unique degradation effect, hence the tracking image observed on polyester resin highly depends on these factors. The structure and topography of the tracking patterns generated on the surface of polyester resin have been examined using the fractal dimension model. The accuracy and success of the fractal dimension model can significantly improve the possibility of determining the variety of aging processes that an insulator has been subjected to over a long time period. In order to verify the results, the structures have also been analyzed by using Markov random fields (MRF) and cellular neural networks (CNN).

On the Aboav–Weaire law

The most general form of the Aboav–Weaire empirical topological law of random cellular networks is derived from conditions on local averages in two dimensions. We assume the average number of edges in a local cluster depends on the number of actual edges and its moments together with the probability distribution function. Based on this constitutive assumption we show that the Aboav–Weaire law depends only on the first and second moments of the distribution function. We show that the Aboav–Weaire law is a direct consequence of the existence of the well-known microscopic topological transformations. We study the effect of the constitutive equation’s coefficients on the Aboav–Weaire law. The properties near to the equilibrium are also investigated to explain the role of the actual parameters of the network.

The stationary state of epithelia.

A tissue is a geometrical, space-filling, random cellular network; it remains in this steady state while individual cells divide. Cell division is a local, elementary topological transformation which establishes statistical equilibrium of the structure. We describe the physical conditions to maintain stationary the epidermis (of mammals or of the cucumber), in spite of the fact that cells constantly divide and die. Specifically, we study the statistical equilibrium of the basal layer, a corrugated surface filled with cells, constituting a two-dimensional topological froth. Cells divide and detach from the basal layer, and these two topological transformations are responsible for the stationary state of the epidermis. The topological froth is capable of responding rapidly and locally to external constraints, and is a good illustration of the plasticity of random cellular networks. Statistical equilibrium is controlled by entropy, both as a measure of disorder and as information, and is characterized by observable relations between average cell shapes and sizes. The technique can be applied to any random cellular network in dynamical equilibrium. Mitosis as the dominating topological transformation and the fact that the distribution of cell shapes is very narrow are the only inputs specific to biology.