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Radio environment maps (REMs) are beginning to be an integral part of modern mobile radiocommunication systems and networks, especially for ad-hoc, cognitive, and dynamic spectrum access networks. The REMs will use emerging military systems of tactical communications. The REM is a kind of database used at the stage of planning and management of the...
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... The basis of this solution is the innovative use of the nonlinear least-square estimator. On the other hand, the authors of [28] conducted an in-depth analysis of statistical properties of signal propagation in WSN based on a numerical solution of the wave equation. The conclusions from this analysis, supported by the simulation results, constitute a set of important guidelines for defining the methods of determining the parameters of many path-loss models. ...
... Validation and calibration of path loss models require extensive field measurements to assess the accuracy of predicted path loss values. These measurements involve gathering data on signal strength, distance, transmitter location, and environmental factors using specialized equipment and techniques [6,50,55,[60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]. Path loss propagation measurements are typically performed by transmitting a known signal from a fixed location and measuring the received signal strength at various locations within the coverage area. ...
... Specifically, we investigated the selected models in terms of their influencing estimation parameters, and also compare their total loss effects in different signal propagation environments. Ways and techniques to improve existing path loss models Existing Path loss models are often limited by their inability to accurately account for real-world factors, leading to discrepancies between predicted and actual signal strength [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. In order to improve existing path loss models, several techniques and approaches can be adopted. ...
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.
... In [26], an ATOLL network planning tool has been engaged to calibrate Hata automatically. [30] to calibrate standard path models for efficient planning of mobile ad-hoc networks by considering atmospheric refractivity and terrain diffraction as key components. The researchers' aim was achieved using line-of-sight and non-line-of-sight signal propagation scenarios. ...
... This led to the introduction of the Hata path loss model, SUI path loss model, COST 231 Hata path loss model, and Ikegami path loss model, all of which were developed in countries such as Japan and the US. The application of the models mentioned above for path loss estimation outside the locations or environments wherein they were not developed has always yielded significant errors [25], [30], [31]. ...
Recent technological development has facilitated the deployment of different Mobile Broadband Cellular Network Systems (MBCNS), such as Long Term Evolution (LTE) and 5G New Radio (NR), globally. This development aims at satisfying the ever-data-hungry multimedia applications to guarantee good quality of service for mobile subscribers. One way mobile subscribers can continuously access and enjoy the services provided by the MBCNS is to put in place a well-planned signal coverage area, wherein accurate path loss estimation is prioritized. Particularly, with the regular technological advancements and evolution of mobile communication systems, particularly fourth and fifth, the development of accurate précised path loss models has become more critical for robust planning and optimization purposes. In practice, the conventional log-distance path loss model is suitable for path loss predictive modeling and estimation in plain signal propagation environments. However, the model is not suitable for irregular terrains. In order to adapt this model for application in irregular terrains, this paper proposes a modified log-distance path loss model with an adaptive polynomial term. The modified long-distance path loss model provides efficient irregular terrain signal loss estimation parameters. In order to boost the prediction accuracy of the proposed model, two regression optimization methods, non-linear least square and weighted least square, were employed with the Levenberg-Marquardt (LM) algorithm to determine its relevant parameters. In terms of the coefficient of correlation and percentage error, the modified log-distance path loss model with the optimized parameters showed 40-60% improvement in accuracy over the standard log-distance model for the path loss prediction across the six different locations investigated. Furthermore, the validation of the proposed model has been provided in order to ascertain the level of its prediction accuracies in other locations. Overall, the modified log-distance model showed remarkable accuracy and efficiency when deployed in a related wireless propagation environment.
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.
Modern wireless communication systems use various technological solutions to increase the efficiency of created radio networks. This efficiency also applies to radio resources. Currently, the utilization of a radio environment map (REM) is one of the directions allowing to improve radio resource management. The REM is increasingly used in emerging mobile ad-hoc networks (MANETs), in particular military tactical networks. In this case, the use of new technologies such as software-defined radio and network, cognitive radio, radio sensing, and building electromagnetic situational awareness made it possible to implement REM in tactical MANETs. Propagation attenuation maps (PAMs) are crucial REM elements that allow for determining the ranges of radio network nodes. In this paper, we present a novel algorithm for PAM based on a parabolic equation method (PEM). The PEM allows determining the signal attenuation along the assumed propagation direction. In this case, we consider terrain topography to obtain a more realistic analysis. Then, we average the adjacent attenuation profiles defined for the selected directions in places where attenuation has not been calculated. To this aim, linear regression is applied. Finally, we define several metrics that allow for the accuracy assessment of determining the PAM as a function of its dimensions.
