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The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.

Since the birth of communications, mankind has always depended on the presence of a communications channel, even before the invention of modern technologies and techniques that replaced more traditional communication channels. The modern communication channel exists both in wired and wireless forms, and a plethora of applications and technologies are able to use both communication media effectively. The wireless form of communication techniques has enjoyed a wide acceptance due to its support for mobility. Wireless devices and applications are used for different purposes in various sectors including health, education, e-commerce, transportation, smart agriculture, disaster, social networks, and many others. Wireless systems infrastructure has also been deployed to support broadcasting, public safety communications, and cellular mobile telephony. In particular, the most widely deployed technology is the cellular mobile communications networks. Broadband Internet provision is of particular interest in meeting the sustainable development goals (SDGs), particularly, in the developing economies. Successful deployment of a wireless communications network would require optimum planning and proper design of the entire system, with more focus on its physical layer interface. The physical layer interface is the one that allows to interconnect users and the wired network, defining the quality of service (QoS) delivered by the providers and also the quality of experience (QoE) as perceived by the users, being that such features allow assessing how sophisticated and robust the physical layer interface really is. While designing the physical layer of wireless systems, many different parameters are brought forward for consideration, including QoS, coverage area, transmitted frequency, transmitted power, received power, and system budget. Thus, to determine how effective the values of these parameters can be when performing network planning in a particular topography, a suitable and well optimized radio frequency (RF) path loss propagation model must be applied.

Path loss represents the reduction in signal strength of the transmitted signal caused by large scale fading along the communication path. Hence, having accurate path loss models becomes essential during the deployment of wireless systems. Many different propagation models are available for different types of terrain, and their performances are location dependent and site specific. However, in order to obtain a dependable path loss model for a given area, a general model needs to be optimized and/or tuned. The optimized model can then be used by engineers to determine the correct values of parameters such as base station (BS) location, transmitter and receiver antenna height, down tilt angle, transmitted power, and frequency [

Path loss propagation models are grouped into four (i.e., empirical models, physical/analytical models, semiempirical models, and deterministic models) [

Their inability in getting detailed information about the propagating environment leads to the adoption of intelligent algorithms to predict path loss, including the artificial neural networks (ANNs) and fuzzy systems (FSs). These methods learn and adapt to any changes in the environment, thus providing a better prediction when compared with the traditional models. Nature-inspired computational methodologies, also known as computational intelligent (CI), such as the genetic algorithm (GA), particle swarm optimization (PSO), and ant colony (AC), provide a solution to the complex propagation environments where the traditional models failed [

Researchers have conducted various studies on the use of CI techniques for path loss predictions due to the need to implement robust wireless systems offering high performance. A comprehensive search was conducted to retrieve existing review papers providing a literature review of CI techniques applied to path loss prediction but only a single review article was found. The survey was conducted [

In this paper, we provide a clear perspective on this topic with a broad and in-depth review on the recent use of CI for path loss prediction. The computational methods considered in our review include artificial neural networks, fuzzy systems, swarm intelligence, and other forms of computer intelligence technique investigated by researchers. The motivation behind this research is to allow interested researchers to make use of this literature survey as a starting point for their own research, whereas expert readers can use the study to propose novel approaches for path loss prediction.

Thus, the contributions of this review paper can be summarized as follows:

A taxonomy and descriptive literature review of computational intelligence model applications, covering the most recent peer-reviewed articles from top journals, conference proceedings, and edited books

Assessment and explorative analysis of the diverse issues and approaches adopted in the different works analyzed, highlighting their main advantages and flaws

Revealing open research issues and opportunities for future research in this area

The remaining sections of the paper are structured as follows. Section

The research steps followed to review the prevailing works in the domain of CI for path loss prediction are presented in this section. Also, we detailed the selection process of the selected studies, which was done through a set of inclusion and exclusion criteria.

Our systematic review followed the guidelines provided in [

Databases used as data sources.

Academic database | URL address |
---|---|

IEEE Explore | |

Springer | |

Google Scholar | |

Taylor & Francis | |

ISI Web of Science | |

Science Direct | |

Scopus | |

ACM Digital Library |

The study selection procedure was designed with caution to ensure that a comprehensive literature review is achieved. Articles were gathered from different academic databases that are relevant to this study; in particular, 957 articles were fetched and examined. This set was reduced to 321 based on a title check and 98 based on abstract. 98 articles were revised comprehensively to identify a final list of 46 articles based on the full contents of the papers; the full process is illustrated in Figure

Study selection procedure.

In order to select the most relevant articles for the study, both inclusion and exclusion criteria were defined. To arrive at the final list, the main literature elements were selected by reading through the title, abstract, and full content of the selected papers. Thus, the inclusion and exclusion criteria applied in this present review are presented in Table

Selection criteria of the study.

Inclusion criteria | Exclusion criteria |
---|---|

The literature survey focuses on path loss prediction only | The literature survey does not consider other forms of prediction |

The review process focuses on the application of CI techniques to path loss prediction only | The review does not concentrate on the application of CI techniques on other forms of research besides path loss prediction |

The review concentrated on published peer-reviewed papers only | The review did not consider nonpeer reviewed articles such as descriptions, technical reports, and workshops |

The study considers the literature published in reputable journals and conferences | The study does not consider the literature published in the books, abstracts, keynotes, or editorials |

The study considered articles that are written in English only | The study did not consider articles that were written in other languages apart from English |

Figure

Total number of articles acquired from the queried search databases.

The CI techniques can be broadly classified into five major categories [

Taxonomy of computational intelligence techniques [

This aspect of computational techniques was introduced and defined by means of a membership function [

A fuzzy set makes its determination based on truth factors which take in the probabilities input in order to provide a definite output. In a fuzzy set

A way of defining a fuzzy set

Gaussian membership function (where

Parameter

Trapezoidal membership function (where

A special case of a trapezoidal function (for

Singleton (where _{0} is a parameter):

A fuzzy inference system (FIS) model is based on fuzzy set theory, fuzzy “

The description of the structure above is done with a first-order sugeno because the output is a crisp value. A sugeno-based ANFIS has a rule of the form [

Block diagram of a fuzzy inference system.

The ANFIS structure [

Rule 1:

Rule 2:

A node in this layer is adaptable and is given as follows:

The input to the _{i} is a changeable language relating to this node, and the MF of _{i} is _{i} (_{i}_{i}_{i}} is the antecedent variables set. Equation (

The membership function decides the mapping of each point in the input space by assigning a membership value in an interval of 0 and 1. The input (antecedent) variables are initially generated by the trial and error method. These variables are therefore adjusted through the learning capability of the NN which enables the errors reduction to be easier and at the same time optimizing the output (consequent) variables [

This layer is made up of the stable nodes which solve the firing power

The output of each node in this layer is constant which is given by the following:

The changeable output of this layer is given by the following:_{i}, _{i} and _{i}} is the consequent variables set and they are computed using the least squares estimates method.

The addition of all the input signals from layer 4 is the output of this layer and is given by the following:

The ANFIS optimization combines both the least square errors estimate and backpropagation algorithms which establishes the output and input parameters, respectively, until the training is completed. It is a statistical approach employed in determining a line of best fit through the minimization of the sum of squares of a mathematical function.

Equation (

Various nature-inspired algorithms have been proposed to improve the prediction of path losses and to aid in successful design and implementation of wireless systems. For instance, Dalkili et al. [

In [

Gupta et al. [

Summary of fuzzy logic computational process.

Authors | Aim | Methods |
---|---|---|

Dalkili et al. [ | To have a new algorithm-based ANFIS for tuning the path loss model | ANFIS |

Supachai et al. [ | To propose a multilayer fuzzy logic system (MLFS) for path loss prediction | Multilayer fuzzy logic system (MLFS) |

Gupta et al. [ | To propose a better method to predict path loss | |

Sanu et al. [ | To proffer the use of a BPSK modulated signal to obtain the path loss | Fuzzy system + linear regression |

Sumit et al. [ | To introduce a fuzzy approach on the prediction of path loss | Mamdani fuzzy inference |

Bhupuak and Tooprakai [ | The use of K-means clustering and fuzzy logic for the minimization of prediction path loss error | K-means and fuzzy logic |

Supachai and Pisit [ | The use of new upper- and lower-bound models for the line-of-sight prediction of path loss in microwave systems | Fuzzy linear regression |

Salman et al. [ | Applied neuro-fuzzy model for the prediction of path loss | ANFIS |

Gupta et al. [ | Path loss prediction for current point of base station in a cellular mobile communications | Fuzzy logic |

Surajudeen-Bakinde et al. [ | Test ANFIS for path loss prediction | ANFIS |

Danladi and Vasira [ | Uses fuzzy logic and spline interpolation to modify the Hata model | Fuzzy logic |

Shoewu et al. [ | To develop a new propagation path loss model for different terrains in Lagos in the 900 MHz and 1800 MHz frequency bands | Fuzzy logic |

Danladi and Vasira [

The artificial neural network (ANN) was derived from the human brain, consisting of the large parallel interconnection of a large number of neurons, resembling the human nerve system. Its popularity can be traced to the late 1800s, when scientific attempts to study the human brain were made. In 1890, William James published the very first scientific article detailing brain activity patterns. The first mathematical computational model of neurons was created by Walter and McCulloch, and it is still used in artificial neural networks as of [

ANN is an artificial intelligence technique which can effectively be used for the development of path loss models, providing a solution for prediction problems. A neural network has the competence to learn, and it has no need of an explicit knowledge of the input and output process relationship [

Popular neural network architecture.

The fundamental element of ANN is the neuron. Let

The activation function is denoted by

_{j} ≤ 1. The derivative of equation (

Considering a neuron _{j} (

For a neuron

For a neuron

In [

In [

Summary of artificial neural networks contributions.

Authors | Aim | Methods |
---|---|---|

Popescu et al. [ | To study the application of neural networks to the prediction of propagation path loss in urban and suburban environments | Feed forward neural networks |

Sotiroudis et al. [ | To propose an alternative neural network algorithm for the prediction of propagation path loss in urban environments | ANN |

Oustlin et al. [ | To analyze ANN models used for macrocell path loss estimation | ANN |

Kalakh et al. [ | To present an ultrawide band propagation channel modeling with neural networks in a mine environment | ANN |

Zaarour et al. [ | To use MLP and RBF artificial neural networks to study ultrawide band communication channels | ANN: multilayer perception (MLP) and radial basis function (RBF) |

Sotiroudis et al. [ | To produce an alternative procedure for predicting propagation path loss in urban environments | Artificial neural network and application of adaptive evolutionary algorithms |

Ozdemir et al. [ | To use the Levenberg–Marquardt algorithm for studying the propagation loss of FM radio stations | Levenberg–Marquardt algorithm ANN |

Dela Cruz and Caluyo [ | To develop a statistical path loss model by measuring indoor losses using a fixed portable indoor antenna | ANN |

Nadir and Idrees Ahmad [ | To address the applicability of the Okumura-Hata model in GSM frequency band of 890–960 MHz | ANN |

Delos Angeles and Dadios [ | To predict path loss for TV transmission using alternative neural networks, and ascertain the proposed model viability | ANN |

Benmus et al. [ | To predict the propagation path loss with an empirical model at the capital city of Libya | ANN |

Ofure et al. [ | To use a three-stage approach in the determination of GSM Rx level from atmospheric parameters | ANN |

Eichie et al. [ | To develop an ANN-based path loss estimation model for rural and urban areas | ANN |

Moazenni [ | To study the relation between the path loss propagation delay and the atmosphere parameter with a neural model | ANN |

Wu et al. [ | To propose a new artificial neural network prediction model for railway environments | ANN |

Authors in [

Wu et al. [

Evolutionary computing has also experienced significant attention by researchers within the path loss prediction area, where several works report the use of evolutionary algorithms. For example, reference [

Summary of the evolutionary algorithms’ computational process.

Authors | Aim | Methods |
---|---|---|

Sotiroudis et al. [ | To make selection of propagation path loss in urban environments an alternative to procedure prediction | Artificial neural network + differential algorithms. |

Fernandes and Soares[ | To evaluate path loss in microcellular systems at the 900 MHz frequency band | Genetic algorithm |

Cavalcanti et al. [ | To carry out a comparative test using the free space and Ericsson 9999 models along with their optimized version | Genetic algorithm. |

The swarm intelligence expression was introduced in the cellular robotic systems context by Beni and Wang in 1989. Swarm intelligence is the collective behavior of decentralized, self-organized systems, either natural or artificial, typically comprising a population of simple agents or bodies interacting locally with each another and with their environment [

A particle swarm optimization (PSO) algorithm is a population-based quest procedure where the individuals, known as particles, are clustered into a swarm. PSO is a stochastic optimization method that is modeled on the fundamental social behavior of bird flocks, which is then used to solve nonlinear problems [

For the global best PSO, the neighborhood for each particle is the entire swarm. For the star neighborhood topology, the social component of the particle velocity update reflects information obtained from all the particles in the swarm. In this case, the social information is the best position found by the swarm, referred to as

The global best position,

He et al. in [

In the same vein, Reference [

The hybrid PSO and ANFIS were also used in [

Summary of particle swarm optimization computational process.

Authors | Aims | Methods |
---|---|---|

He et al. [ | To design an RBF-based neural network adaptive particle swarm optimization algorithm | PSO + neural network |

Tahat and Taha [ | To propose the application of a statistical tuning technique based on particle swarm optimization (PSO) to adjust the COST 231 Walfisch-Ikegami for path loss prediction | PSO |

Olukunle et al. [ | To propose the development of an optimized model for urban outdoor coverage at 2300 MHz frequency for LTE network systems in Port Harcourt urban terrain roads (Rumuokoro, Eneka and Ikwerre) | PSO |

Chiu et al. [ | To evaluate the performance of a particle swarm optimization (PSO) model and a genetic algorithm (GA) for path loss estimation in an urban area | PSO + GA |

Al Salameh and Al-Zu’bi [ | To propose the path loss prediction for mobile phone stations in an outdoor environment in the 900 MHz and 1800 MHz bands | PSO |

Omae et al. [ | To conduct a study and report the path loss prediction of a Wi-Fi signal propagation along a passageway using particle swarm optimization (PSO) trained adaptive neural fuzzy inference system (ANFIS), ANFIS, and PSO trained with a random input | PSO + ANFIS |

Garah et al. [ | To propose the application of particle swarm optimization for the tuning of the COST 231 model parameters for the improvement of its accuracy for path loss prediction | PSO |

Xiang and Wang [ | To report the application of PSO-BP-Kriging for 5G signal coverage detection | Enhanced particle swarm optimization algorithm |

The reviewed works evaluate the performance of applied computational intelligence techniques on path loss prediction by means of statistical measurement metrics. The performance of any model developed for propagation path loss prediction is usually evaluated using several performance metrics, as illustrated in Figure

Performance metrics extracted from the survey.

In Table

Comparison of some review literatures.

Authors | Year | Methods | Performance |
---|---|---|---|

Oustlin et al. [ | 2010 | ANN | ME 0 dB |

Salman et al. [ | 2017 | Fuzzy | RMSE 5.23 dB |

Ozdemir et al. [ | 2014 | ANN | RMSE 9.57 dB |

Olukunle et al. [ | 2017 | SWARM | RMSE 3.030 dB |

Danladi and Vasira [ | 2018 | Fuzzy | MAE 1.55 dB & 0.4 dB |

Al Salameh and Al-Zu’bi [ | 2014 | SWARM | RMSE 5 dB |

Fernandes and Soares [ | 2014 | Evolutionary | ME 3 dB |

Surajudeen-Bakinde et al. [ | 2018 | Fuzzy | RMSE 4.47 dB |

Popoola et al. [ | 2019 | ANN | RMSE 0.81 dB |

Popoola et al. [ | 2019 | ANN | R 0.95 |

This section illustrates the results obtained based on the number of works published in each year ranging from 2004 to 2019, as shown in Figure

Trend of publications.

Percentage of papers in each category.

Number of articles in each category per year (period 2004–2019).

Despite the improvement recorded by the various CI algorithms, there are open research problems. The research problems are discussed in this section, and new directions are pointed out to provide research opportunities for developing research in propagation path loss prediction.

Deep learning is the new generation of the ANN which is currently attracting tremendous attention from the research community. However, surprisingly the deep learning remains unexploited in propagation path loss prediction notwithstanding it is effectiveness, efficiency, and robustness in solving real world problems. Empirical evidence from the literature has proven that the deep learning architectures such as the deep belief network, convolutional neural network, attentive deep neural network, stack autoencoder, generative adversarial network, deep reinforcement learning, deep long short-term memory, and deep recurrent neural network among many others can handle machine learning problems such as prediction, clustering, and classification better than the shallow ANN currently in use for propagation path loss prediction especially when the amount of the data size is large. As such, this survey suggests researchers to explore these architectures of the new generation ANN in the domain of propagation path loss.

Observations from the survey pointed out that the propagation path loss prediction is mainly conducted for a single terrain. Therefore, the model developed based on any CI algorithm for a particular terrain is limited for prediction of path loss within the terrain. The result obtained from such model cannot be generalized to other terrains. The multiterrain propagation path loss prediction model remains an open issue. We suggest researchers to apply deep learning architecture because of its ability to handle large scale data to investigate the possibility of propagation path loss prediction for multiple terrains.

It has been observed from the survey that physical algorithms that derived their inspiration from the mechanism and processes of physical systems remain unexploited in propagation path loss prediction. Example of such algorithms includes simulated annealing, harmony search, memetic algorithm, and cultural algorithm. This is despite the fact that these classes of algorithms have been applied in other domain to solve real world problems and found to be effective and efficient. Therefore, we enjoin the research community to explore the physical algorithms in the area of propagation path loss prediction and compare the performance with the classical algorithms from evolutionary and swarm computation.

It was observed from the survey that the deep learning architecture that its hyperparameters were optimized by the global optimization algorithm from the family of evolutional and swarm computation remains unexploited in propagation path loss prediction. Empirical works have shown that the deep learning architecture hyperparameters optimized by the global optimization algorithm are more effective than the conventional architecture. It will be interesting to investigate the performance of deep learning architecture that its hyperparameters are optimized by the global optimization algorithm in the domain of propagation path loss.

It is clearly shown from the survey that researchers in this domain heavily relied on the shallow ANN for propagation path loss prediction. The shallow ANN has limitation in handling large scale data because the performance of the shallow ANN diminishes as the amount of data increases. In addition, unlike the new generation ANN-deep learning, the shallow ANN require manual data engineering to perform feature extraction on the data before feeding into the shallow ANN for propagation path loss prediction. To eliminate this tedious procedure and waste of time and resources, we suggest the application of the deep learning architecture.

The application of immume algorithms in propagation path loss prediction remains unexploited. The category of algorithms derived their inspiration from the complex adaptive biological immune system. Example of such algorithms includes negative selection, clonal selection, artificial immune systems, and immune network models. It was found from the literature search that immune algorithms are yet to gain attention from researchers for propagation path loss prediction in different environments. On the other hand, studies conducted on immune system reveal that it has more applications in data communication networks, such areas as pattern recognition, data clustering, security, anomaly, and web mining, among many others [

Not all the CI techniques have been thoroughly studied, as shown in Figure

The combination of CI and existing empirical or deterministic path loss propagation models may provide trade-off between ease of application and accuracy. The empirical path loss models are dependent on the local terrain and have high prediction error when applied to another terrain. The deterministic path loss prediction models are dependent on the modeling of the terrain, buildings, and the position of the antennas, which require updated and comprehensive terrain databases that are usually not cost effective. Therefore, the implementation of combination of empirical/deterministic models and CI techniques would successfully aid at better predicting with little or no errors. This is another open research area that needs to be looked upon.

This study presents a systematic literature review on the application of computational intelligence techniques for the prediction of path losses for different environments and spanning different operating frequencies. The literature review covered different classes of CI techniques as follows: fuzzy sets, artificial neural networks (ANNs), evolutionary algorithms, swarm intelligence (SI), and artificial immune systems (AISs). The current status on the application of CI techniques for path loss prediction, research trends in the past few years, and open research problems and future research areas and expectations are outlined in the paper. It was discovered that the efforts made by researchers to propose a better model for the prediction of path loss in different terrains has at times led to the testing and simultaneous application of two different CI techniques in a specific environment. The comprehensive review presented in this paper can thus become a first point of reference for new researchers interested in radio propagation and channel modeling. Also, expert researchers in channel modeling or radio propagation can use this review to gain further insight when suggesting a novel approach for path loss prediction.

No data were used to support the findings of this study.

The authors declare that they have no conflicts of interest.

This work was funded by the Federal Ministry of Education, Federal Government of Nigeria, Tertiary Education Trust Fund (TETFUND), Institutional Based Research (IBR) Fund, 2018.