Abstract
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.
Highlights
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. e 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. e wireless form of communication techniques has enjoyed a wide acceptance due to its support for mobility
We provide a clear perspective on this topic with a broad and in-depth review on the recent use of computational intelligence (CI) for path loss prediction. e computational methods considered in our review include artificial neural networks, fuzzy systems, swarm intelligence, and other forms of computer intelligence technique investigated by researchers. e 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
ACM digital library reasoning as shown in Figure 4. e fuzzy inference systems (FISs) approximate functions are based on a rule base, a database, and a reasoning mechanism. e adaptive neuro-fuzzy inference system (ANFIS) is a class of adaptive networks that are functionally equivalent to FIS
Summary
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. e 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. e wireless form of communication techniques has enjoyed a wide acceptance due to its support for mobility. Nature-inspired computational methodologies, 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 [5] These CI methods integrate neural networks, fuzzy logic, and other natural inspired algorithms to optimize the path loss, reducing the errors and improving the prediction accuracy. 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. IEEE Springer Google Taylor & ISI web of Science explore scholar francis science direct
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