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The growing subscriptions for cellular communication services have been remarkable globally. This development has placed immense pressure on the network providers to optimally position the radio transmitters to deliver quality services to mobile subscribers. The conventional cellular network planning techniques are prone to errors and incapable of...
Citations
... An effective cell modeling with particle swarm evolution (PSO) is proposed to improve the energy efficiency (EE) of the network [5]. A genetic algorithm (GA) based cell planning is proposed in [6] to find the number of BS required considering diverse sectorization schemes. The drawback of high-order sectorization includes an effective rise in the number of handovers, which results in network overhead. ...
... Vertices of the hexagonal serving area of a BS is evaluated from the far associated user i′ to the BS j as (6) ΔPL h = −20 log 10 h 2 dB for land type 3 ...
The role of information and communication technology infrastructure is very crucial and perhaps most important during and post disaster (DPD) scenarios where thousands of lives are at risk. Communication services are expected to operate effectively in such demanding situations with restricted resources while fulfilling their core functionalities. The absence of coordinated cell planning taking the vulnerability of the geographical zone into account is a drawback that inhibits system operations and rescue efforts of public protection and disaster relief (PPDR) units. In this paper, the major issues of cell planning are encountered, and new algorithms for optimum LTE cell planning based on the hybrid dragonfly algorithm with differential evolution (DADE) are proposed under user coverage, user association, and capacity constraints. Thereafter, the feasibility of deployment and operation of an operator-independent emergency system (ES) integrated with balloon-based lightweight LTE eNodeB is analyzed to mitigate the DPD communication challenges. Then evaluate the optimal location for the deployment of ESs to cater to the users under the aforementioned constraint. Finally, optimum cell planning considering the vulnerability of the zone is discussed. The comparative comprehensive analysis of the results shows that the proposed algorithm offers superior convergence characteristics as well as time complexity as compared to the other state-of-the-art algorithms. Comparative results of normalized sum utility depict that the proposed algorithm outperforms the grey wolf optimizer (GWO), salp swarm algorithm (SSA), differential evolution (DE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) based hybrid algorithms, respectively, by 0.5%, 4.3%, 6.5%, 8.6%, and 11.8%.
... Machine learning techniques offer several advantages in path loss predictive modelling. They can handle complex and non-linear relationships between variables, adapt to changing environments, and make predictions based on a wide range of input features [40][41][42][43][44][45][46][47][48][49][50]. ...
... Inspired by the principles of evolution, GAs employ a bio-inspired optimization technique to find optimal solutions to a given problem. The algorithm operates by evolving a population of potential solutions through successive generations, mimicking genetic reproduction and natural selection [46,51]. One of the key advantages of GAs is their ability to handle large, high-dimensional search spaces. ...
... By employing fitness functions, which evaluate the quality of each solution based on its ability to solve the problem at hand, GAs promote the survival and propagation of more successful individuals, while eliminating suboptimal solutions. Over time, the population evolves and adapts to the changing environment, honing in on the best possible solution [46,51]. By mimicking the principles of evolution and employing genetic operators, GAs efficiently explore large search spaces, making them suitable for highdimensional problems. ...
... Machine learning techniques offer several advantages in path loss predictive modelling. They can handle complex and non-linear relationships between variables, adapt to changing environments, and make predictions based on a wide range of input features [40][41][42][43][44][45][46][47][48][49][50]. ...
... Inspired by the principles of evolution, GAs employ a bio-inspired optimization technique to find optimal solutions to a given problem. The algorithm operates by evolving a population of potential solutions through successive generations, mimicking genetic reproduction and natural selection [46,51]. One of the key advantages of GAs is their ability to handle large, high-dimensional search spaces. ...
... By employing fitness functions, which evaluate the quality of each solution based on its ability to solve the problem at hand, GAs promote the survival and propagation of more successful individuals, while eliminating suboptimal solutions. Over time, the population evolves and adapts to the changing environment, honing in on the best possible solution [46,51]. By mimicking the principles of evolution and employing genetic operators, GAs efficiently explore large search spaces, making them suitable for highdimensional problems. ...
In the field of wireless communication and network planning, accurate path loss predictive modelling plays a vital role in understanding the behavior of signal propagation in diverse environments. Traditional empirical models have been widely
used for path loss estimation, but they often lack the flexibility to adapt to complex scenarios. On the other hand, machine learning techniques have shown great potential in various domains, including wireless communication. This paper aims
to present a hybrid empirical and machine learning approach for efficient path loss predictive modelling. By combining the strengths of empirical models and machine learning algorithms, we can enhance the accuracy and adaptability of path
loss predictions. The following sections provide an overview of path loss modelling, explore traditional empirical techniques, discuss the application of machine learning approaches, and outline the methodology for the hybrid approach, along with evaluation and analysis. Finally, we conclude with a summary of findings and suggest future directions for research in this field.
... Frequency Dependent Path Loss Models are essential for the planning and optimization of wireless networks. By accurately predicting the signal strength and coverage area, these models help network designers determine the optimal placement of base stations and antennas to ensure efficient and reliable communication [38][39][40][41][42][43][44][45][46][47][48][49][50]. Moreover, they assist in the design of wireless communication systems in areas with varying frequencies, such as hospitals, airports, and urban centers, where multiple services are provided simultaneously. ...
... Originally developed by Japanese researchers Okumura and Hata, this equation takes into account various factors such as frequency, distance, base station height, and antenna height, to estimate the signal strength at a particular location. The model accounts for both line-of-sight and non-line-of-sight transmission, making it a valuable tool for designing and optimizing wireless communication systems in urban areas [40][41][42][43][44][45][46]. The equation itself is a weighted sum of several components, including free space loss, diffraction loss, and penetration loss. ...
... Advanced path loss models, such as those based on ray tracing [45 and radio wave simulations, offer highly accurate path loss prediction capabilities in complex scenarios. These models account for detailed information on terrain, building materials, object interactions, and diffraction effects, enabling more precise wireless system design and network planning [45][46][47][48][49]. Advanced path loss models are an essential tool in the field of wireless communication as they provide accurate predictions of signal propagation. ...
Modern wireless systems for mobile communication use electromagnetic waves to transmit information over the air, enabling seamless connectivity for a wide range of devices. However, one of the key challenges in wireless communication paths is loss in the strength of propagated signals. Path loss refers to the reduction in signal strength as it propagates through the wireless channel. Path loss models are mathematical representations that capture the attenuation of signal power due to various factors such as distance, frequency, obstacles, and environmental conditions. Understanding and modeling path loss is crucial for designing and optimizing wireless communication systems, as it directly impacts the coverage area, link quality, and overall performance of the network. By accurately modeling path loss, engineers can also optimize various aspects of a wireless communication system, such as antenna placement; transmit power control, and interference mitigation, ultimately improving the broad-spectrum performance and reliability of the network. This paper investigates the concept of path loss in wireless communication networks and provides a comprehensive overview of its various models and their use in designing and implementation of networks. Furthermore, it reviews existing path loss models, and explains their advantages and disadvantages. Finally, it discusses the current trends future research directions related to path loss and its models. The findings in this study can help them better design and implement robust wireless communication networks with improved signal quality and capacity.
... Rodriguez et al. [23] analyzed the effect of vehicles' average utilization on the siting in a study on facility location and equipment emplacement led by. Nwelih et al. [24] introduced a weighted fitness function that combines coverage, capacity, and transmit power parameters in this field. However, in most of the articles, only the influence of the selection of coordinates for a single type of BS was considered, neglecting the complexity of the practical situation where multiple types of BSs work in combination [25,26]. ...
Nowadays, communication networks are becoming increasingly complex. This paper aims to demonstrate an effective method to achieve the intelligent planning for network base stations (BSs). The various parameters such as BS coordinates (x, y), the collaboration of multiple types of BS, and the density of BS construction are taken as design parameters for BS placement. We construct the objective function using the lowest total cost and the total minimum workload of BS to 90%. To solve the problem of siting planning with large data volume and mixed placement of multiple BS, we propose a new practical three-step model for BS siting planning: (Ⅰ) roughly selecting the alternative coordinates for the BS using the DBSCAN algorithm; (Ⅱ) correcting and further refining the alternative BS coordinates using the K-means algorithm; (Ⅲ) determining the optimal BS construction solution to meet the requirements using simulated annealing algorithm (SAA). The real data of a 2500×2500 area have been used for the simulation test. The simulation result shows that BS placement covers 90.03% of the workload, confirming that the proposed method can handle site planning for large orders of magnitude of data and use a mix of BS to achieve the best economics for the demand. This paper provides basic support for future research on network site optimization.
