Radio Network Planning Research Articles

MIFF

Human Mobility Extraction with cellular Signaling Data (SD) is essential for human mobility understanding, epidemic control, and wireless network planning. SD log the detailed interactions between cellphones and cellular towers, but suffer from a spatio-temporal uncertainty problem due to cellular network tower-level load rebalancing (switching users between towers) and cellphone usage activities. To date, most models focus on utilizing better data like RSSI or GPS, do not directly address uncertainty. To address the SD uncertainty issue, we utilize two insights based on (i) individuals' regular mobility patterns and (ii) common co-movement mobility patterns between cellphone users as suggested by fundamental human mobility nature. Accordingly, we design a Multi-Information Fusion Framework (MIFF) to assist in extracting road-level human mobility based on cell-tower level traces. To evaluate the effectiveness of MIFF, we conduct experiments on one-month SD obtained from a cellular service operator, and SD manually collected by handheld mobile devices in two cities in China. Four transportation modes, namely railways, cars, buses, and bikes are evaluated. Experimental results show that with MIFF, our road-level trajectory extraction accuracy can be improved by 5.0% on Point correct matching index and 68.5% on Geographic Error on average.

An intelligent electromagnetic environment reconstruction method based on super-resolution generative adversarial network

In this paper, an intelligent electromagnetic environment reconstruction method is proposed based on a super-resolution generative adversarial network (SRGAN). The altitude matrices together with the low-resolution matrices obtained by measured power values are employed as inputs. Then, an electromagnetic environment reconstruction method capable of generating the high-resolution power coverage matrix in the selected area is designed. Data enhancement is employed to expand the dataset and a modified generator network with squeeze and excitation modules is used during the training process. To validate the proposed method, the simulation analyses are carried out in typical suburb environments based on a ray-tracing tool. Numerical results indicate that, compared with classical methods such as nearest-neighbor interpolation, bilinear interpolation, and bicubic interpolation, the proposed method can provide more accurate reconstruction results for power coverage. In addition, the peak signal to noise ratio (PSNR) of the proposed method is higher than those of the classical methods. The proposed intelligent electromagnetic environment reconstruction method can be useful for the planning, deployment, and optimization of wireless networks.

Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

Hybrid Kriging and multilayer perceptron neural network technique for coverage prediction in cellular networks

ABSTRACT Coverage prediction is a crucial issue in wireless network planning and design. Typically, coverage prediction techniques start by model identification given a set of measurements at specified locations. Then, this model is used to predict the signal strength at other locations using some machine-learning like approach. However, when the drive tests (or test-beds) become costly because of some environmental constraints, the performance of the machine learning-based model is questionable due to the insufficiency of the training dataset. In this context, we suggest exploiting the geostatistical interpolation technique named Ordinary Kriging to enrich the training data set. For this purpose, a set of received signal strengths from wireless transmitters has been collected at known locations through cellular network technology, which are then used to generate an enriched dataset according to the Ordinary Kriging interpolation technique. The results show that the hybrid Ordinary Kriging and machine learning model significantly enhances path loss accuracy and offers a new setting for data reproducibility. A database is constructed from received signal strength measurements which is not enough to produce accurate results when it is used to train a feed forward neural network for the task of coverage prediction. A geospatial interpolated technique named Kriging is used in order to produce a big amount of data. The neural network is trained by both of the original and the interpolated datasets. The coverage prediction results more reproducible and accurate than the results without the interpolation stage.

Complex Permittivity Estimation for Cloths Based on QPSO Method Over (40 to 50) GHz

To determine the propagation characteristics in building for fifth-generation (5G) wireless communication, it is necessary to obtain the complex permittivities of the construction materials at millimeter wave (mmWave) band. Cloth materials are widely used in house, which have a great impact on the propagation of mmWave; however, their dielectric properties in mmWave band are rarely reported. In this communication, a new method for the estimation of the complex permittivities based on quantum-behaved particle swarm optimization (QPSO) algorithm and free-space measurement is proposed. Compared with classical particle swarm optimization (PSO) and chaos PSO (CPSO), QPSO is robust and efficient in searching the global optima. The complex permittivities of four cloth materials (linen, leatherette, polyester fiber, and latex mattress) are extracted from the measured transmission and reflection coefficients for perpendicular polarization over 40-50 GHz, and the accuracy is verified by comparing the calculated and measured reflection coefficients at other incident angles. The results presented in this communication are helpful for modeling the propagation of mmWave in indoor environment, and the proposed estimation method can serve as a good tool for the determination of dielectric properties of construction materials in wireless network planning.

How Friendly Are Building Materials as Reflectors to Indoor LOS MIMO Communications?

The tremendous popularity of Internet-of-Things (IoT) applications and wireless devices has prompted a massive increase of indoor wireless traffic. To further explore the potential of indoor IoT wireless networks, creating constructive interactions between indoor wireless transmissions and the built environments becomes necessary. The electromagnetic (EM) wave propagation indoors would be affected by the EM and physical properties of the building material, e.g., its relative permittivity and thickness. In this article, we construct a new multipath channel model by characterizing wall reflection (WR) for an indoor Line-of-Sight (LOS) single-user multiple-input–multiple-output (MIMO) system and derive its ergodic capacity in the closed-form. Based on the analytical results, we define the wireless friendliness of building material based on the spatially averaged indoor capacity and propose a scheme for evaluating the wireless friendliness of building materials. Monte Carlo simulations validate our analytical results and manifest the significant impact of the relative permittivity and thickness of building material on indoor capacity, indicating that the wireless friendliness of building materials should be considered in the planning and optimization of indoor wireless networks. The outcomes of this article would enable appropriate selection of wall materials during building design, thus enhancing the capacity of indoor LOS MIMO communications.

A Framework for Unsupervised Planning of Cellular Networks Using Statistical Machine Learning

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

Enhanced Virtual Laboratory Experience for Wireless Networks Planning Learning

Online education is benefiting from advanced digital resources that now can offer interactive activities. Videos, animations, virtual and remote laboratories, and online games are just some examples of these activities. Educational standards and Virtual Learning Environments VLEs allow the integration of all these materials into learning objects that can be deployed in online courses or MOOCs. Virtual Laboratories provide an opportunity to train students and give to them the confidence for future interactions with real laboratory settings, with advantages such as: cost, portability, concurrency, and safety. Educational video games can improve the students' knowledge acquisition and develop abilities such as: mental speed, reaction, connection between thoughts and movements and concentration. This paper shows the implementation of a virtual laboratory learning experience that includes the use of video games, multimedia content and virtual simulations. The objective of this resource is to help in the learning of wireless network planning problem and improve the students' knowledge in the field of wireless networks key concepts. The virtual laboratory allows students to solve simple problems of antennas distribution and progressively increase the level by adding constraints and including new concepts in the design of the network.

A Comprehensive Study on Simulation Techniques for 5G Networks: State of the Art Results, Analysis, and Future Challenges

Ιn this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.

Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures

The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above analysis demonstrate that the denser the small-cell deployment, the higher the area throughput, and that a sectored microcell configuration can double the throughput for ultra-dense networks compared to dense networks. Furthermore, dense 5G networks can actually have cell radii below 400 m and down to 120 m for the ultra-dense sectored network that also reached spectral efficiency 9.5 bps/Hz/Km2 with no MIMO or beamforming.

Rework the Radio Link Budget for 5G and Beyond

The 5th generation of mobile communication system (5G) enables the use of millimeter wave frequency bands and beamforming with narrow-beam directional antennas for mobile communication. Accurate estimation of radio link budget which enables direct assessment of achievable cell range or maximum throughput and facilitates network parametrization before deployment is one of the most challenging problems in radio network planning. In contrast to traditional cellular systems, where omni-directional or sectoral antennas are deployed with half-power beam-width much larger than angular spread of the radio channel, the beam-width of antenna arrays assumed for 5G in sub-6GHz and millimeter wave bands can be comparable to or smaller than channel angular spread in scattering environment. Since the effective antenna pattern is determined jointly by the nominal antenna pattern and channel angular spread, it is no longer appropriate to use nominal pattern in radio link budget analysis or system level simulations. Simplified approach, where nominal pattern is assumed for all typical propagation conditions, results in overestimation of the signal power in serving links and underestimation of interference, which in consequence gives erroneous estimation of link budget and leads to unsatisfactory network design and deployment. To avoid inaccurate calculation of link budget while maintaining simplicity it is proposed to modify the simplified approach by using effective antenna patterns. On the other hand, effective antenna pattern can be further optimized by matching its half-power beam-width to the angular spread of the radio channel. It is demonstrated via simulations how to rework the radio link budget for accurate estimation of system performance in high bands for 5G and beyond, along with benefits of effective antenna pattern optimization.

Path Loss Propagation Evaluation and Modelling based ECC-Model in Lowland Area on 1800 MHz Frequency

Propagation modeling is the most important part of mobile wireless network planning. Wireless network planning requires an accurate calculation of the path, which depends on different environmental conditions. It requires accurate path loss modeling of the characteristics of a specific region. The study aimed to obtain a path loss propagation model by modifying the ECC model and using linear, logarithmic regression in lowland areas. The measurement used drive test method, located in the Jakabaring area that represented the lowland area. This research used four existing path loss models, namely Okumura-Hatta, COST-Hatta, Ericsson Model, and ECC Model. It was found that the Okumura-Hatta model had the largest RMSE value, 34.90, followed by the Ericsson model, 27.07, while the ECC model had the smallest RMSE value, 8.43. The ECC model required to be modified using logarithmic, linear regression to obtain the proposed model. The results of the evaluation showed that the proposed model improved with RMSE 4.93, MAPE 2.71, and MAD 3.91, whereas the values of the existing ECC Model before modification were 8.43 for RMSE, 4.72 for MAPE and 7.09 for MAD. The proposed model provided an accurate prediction of the path loss propagation in a lowland environment. The results of the study can be used for planning engineers to plan, design, and implement the wireless communication networks in lowland area conditions.

Cellular Communications Coverage Prediction Techniques: A Survey and Comparison

A lot of effort and time is utilized in the planning and building of the cellular wireless networks to use minimum infrastructural components to provide the best network coverage as well as delivery of quality of service. Generally, path loss models are used for the prediction of wireless network coverage. Therefore, detailed knowledge of the appropriate path loss model suitable for the proposed geographical area is needed to determine the coverage quality of any wireless network design. However, to the best of our knowledge, despite the importance of path loss models, as used for the prediction of wireless network coverage, there doesn't exist any comprehensive survey in this field. Therefore, the purpose of this paper is to survey the existing techniques and mechanisms which can be addressed in this domain. Briefly, the contributions of this paper are: (1) providing a comprehensive and up to date survey of the various network coverage prediction techniques, indicating the different frequency ranges the models were developed, (2) the different suitable terrains for each of the model and the best suit mobile generation were presented, and lastly, (3) providing comparative analysis to aid the planning and implementation of the cellular networks.

До ГІС-технології створення висотного клатера рослинності для цілей планування та оптимізації LTE та 5G мереж стільникового зв’язку

До ГІС-технології створення висотного клатера рослинності для цілей планування та оптимізації LTE та 5G мереж стільникового зв’язку

Radio-propagation measurements and modeling in indoor stairwells at millimeter-wave bands

A good understanding of signal wave attenuation along a stairwell is necessary for successful wireless network planning. In this study, the propagation characteristics in indoor multi-floor stairwell environments are examined under the millimeter frequency band. An extensive signal measurement is conducted in two stairwells. These measurements provide information important for ensuring consistent radio transmission of data in such environment, which then enables immediate response during emergency operations. In these measurements, directional horn antennas for co-polarization and cross-polarization in transmitters and receivers are used for different millimeter frequencies (26, 28, 32, and 38 GHz). The feasibility of using the millimeter wave, which is suitable for indoor 5G wireless networks, in two different indoor stairwell environments is investigated. Four different models are employed for path loss investigation. Under single frequencies, the close-in free-space reference distance and floating-intercept path loss models are adopted. For multiple frequencies, the close-in free space reference distance with frequency dependent path loss exponent and alpha-beta-gamma models are applied. The measurements provide data on the received power at multi-floor stairwells through the stair steps, which are useful for path loss study. Accordingly, the path loss exponent values, standard deviations, and other parameters are obtained.

A Comparative Study on Meta-Heuristic Algorithms For Solving the RNP Problem

The continuous increases in the size of current telecommunication infrastructures have led to the many challenges that existing algorithms face in underlying optimization. The unrealistic assumptions and low efficiency of the traditional algorithms make them unable to solve large real-life problems at reasonable times.The use of approximate optimization techniques, such as adaptive metaheuristic algorithms, has become more prevalent in a diverse research area. In this paper, we proposed the use of a self-adaptive differential evolution (jDE) algorithm to solve the radio network planning (RNP) problem in the context of the upcoming generation 5G. The experimental results prove the jDE with best vector mutation surpassed the other metaheuristic variants, such as DE/rand/1 and classical GA, in term of deployment cost, coverage rate and quality of service (QoS).

THE USE OF DEM OPEN DATASOURCES FOR THE PURPOSES OF RADIO NETWORK PLANNING AND OPTIMIZATION

The article considers the bases of use the DEM open data sources such as SRTM version 3.0 and AsterGDEM2 for the purposes of radio network planning and optimization. The calculations were made within the sample area determined by zoning of the territory by the terrain slopes with a confidence interval 95% ± 3%. The network Pass Loss prediction was calculated with the Atoll software based on Okumara-Hata formula for the standard Universal Mobile Telecommunications System (UMTS) using each of the next digital terrain models: the topographic data of scale 1:2 000, the topographic data of scale 1: 50 000, SRTM version 3.0 and AsterGDEM2. The mean square errors were calculated with the ArcGIS ModelBuilder extension using such functions for the raster datasets as classification, mosaic, zonal statistics etc. The conclusions were made based on the permitted RMSE values 6-7 dB for urban areas, 10-15 dB for suburban and village areas.

Wireless Network Planning Strategy Based on Optimal Energy Consumption and Network Load

Saving power on base stations (BSs) and improving user communication quality become critical issues in wireless cellular networks. In this paper, we propose a BS planning algorithm based on multi-objective optimization. The optimization objective is the lowest energy consumption and load balancing between networks. The NSGA-II algorithm is used to solve the problem, and then the TOPSIS method is used to find the optimum scheme. The simulation results show that the proposed algorithm can effectively reduce the system energy consumption and improve the load balancing between networks.

Application and realization of ray tracing in network planning of wireless private network

In order to meet the business requirements of wireless private network in network planning, especially the network requirements of intelligent power private network, such as greater capacity and higher precision. In this paper, deterministic propagation model is used to simulate the network in network planning to improve the accuracy of calculation, and at the same time, must ensure the speed of the planning software. The reverse ray tracing algorithm and mirroring method are used for calculation. Simulation results show that when calculating the direct and low-order reflection paths and ignoring the high-order reflection and diffraction paths that have little effect on the results can ensure a certain calculation rate. And achieve the purpose of balance accuracy and rate.

Optimizing Radio Network Planning Evolution Towards Microcellular Systems

Service demand in cellular radio networks is generally heterogeneous and non uniform, leading to asymmetrical topologies. This complexity along with the multiple radio access technologies, the radio channel and practical constraints, make the radio network planning process a challenging task that leaves little room for intuitive solutions. In this paper we formulate the framework that allows for radio network planning analysis to be performed in the context of an evolutionary macro–micro-relay combination approach, or wide area microcell deployments. Coverage, capacity and cost requirements as well as different practical constraints such as reuse of existing 2G and 3G sites, are considered along with a multi-objective optimization algorithm adapted to the radio network planning problem for 4G systems. Among other things, the produced results highlight two key issues: (a) the way towards high capacity radio networks is to replace macrocells with a number of microcells that is more than one order of magnitude higher, (b) optimizing the radio network deployment can reduce the cost/Mbps/km2 by factors of 4 to 20, compared to equivalent macrocellular reference scenarios.