Path Loss Model Research Articles

Optimizing Performance of Worst Case User in Ultra-dense Networks Utilizing Deep Q-learning

Abstract: This paper defines, analyzes, and improves the performance of the worst-case user in ultradense networks Background: In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance. Objectives: Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks. Methods: In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper. Results: The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions. Conclusion: In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.

Performance Analysis and Optimization of Worst Case User in CoMP Ultra Dense Networks

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

Channel measurement and characterisation for 5G multi‐scenarios at 26 and 38 GHz

AbstractMillimetre‐wave (mmWave) frequencies play a vital role in fifth‐generation (5G) wireless systems and beyond due to the vast available bandwidth of several GHz. This paper presents channel characteristics and their channel models for mmWave based on extensive channel measurements at 26 and 38 GHz conducted in 5G scenarios, such as the rooftop, the high‐speed railway, and the Industrial Internet of Things scenarios. The channel sounder receiver (Rx) uses an omnidirectional or phased array antenna to meet the requirements of channel measurement, such as recording absolute delay and obtaining 3‐dimensional angular information. We use the classical close‐in model and floating‐intercept model to model path loss. Meanwhile, channel statistics in the delay domain are derived from the measured power delay profiles. Note that the different scenarios are measured with the same channel sounder, making the measurement results of different scenarios comparable. It is shown that the shadow fading parameter σ is 0.7 dB as fewer large scatterers exist in the rooftop scenario. Moreover, due to the significant dependence of delay spread on the geometric relationship between the transmitter (Tx), Rx, and surrounding environment, the statistical data in the delay domain varies significantly in different scenarios. The channel characteristics and models will guide future air‐interface, beamforming, and transceiver designs for 5G and beyond.

A novel relay placement method for smart energy IoT systems in offices with genetic algorithm

<p>Smart energy in large offices and organizations is an important research area of the Internet of Things (IoT). The energy efficiency of buildings is vital for the environment and global sustainability. To achieve satisfactory performance for this goal, WiFi access point (AP) indoor coverage is of high importance. As it costs a lot to add more WiFi to have good coverage in all parts of the office’s buildings, we consider relay node (RN) instead of adding more APs. So in this paper, we propose a novel RN placement in order to improve the indoor coverage of offices considering the signal attenuation using a path-loss model as the main measure for determining positions. The main problem is the placement of these RNs and the required number of them by considering existing APs. At first, we obtain the radio propagation model parameters by considering the data that are collected from the AP. Then based on these parameters, the proposed solution uses a genetic algorithm (GA) for RN placement optimization problem. The experimental results display the effectiveness of the proposed solution for the RN placement problem.</p>

Path loss modeling based on neural networks and ensemble method for future wireless networks

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.

Channel characterization and modeling for wireless communication link planning and design in the tropics

<p>Channel model provides basis in the link budgetary for planning and design of low margin systems of communication links. This paper proposed a simple and efficient channel model for wireless channel path loss characterization and modeling. Free space channel was characterized and modelled to account for both static and dynamic changes of signal that propagates through it. Correction factors in the model account for the losses due to refraction effects, as a result of abnormal refractivity condition frequent in tropical regions. The model was validated via measurement data from experiment conducted in a tropical region and a high level of agreement was observed. The proposed model can be deployed during link planning and budgetary for ultra high frequency (UHF) wireless radio services.</p>

Intelligent Base Station Placement in Urban Areas With Machine Learning

This paper presents a time-reliable machine learning model for accurate and rapid base station placement in urban areas. Several real-world city data are used for training the machine learning model to predict parameters such as path loss values. It is developed using supervised training and multiple regressions with the use of artificial intelligence. Parameters such as coverage dimensioning, bandwidth of frequencies, building's density and height, propagation model tuning and path loss values are used to predict the locations of base stations. Once the predicted base station position is obtained, the average percentage error is calculated against the real-world data of base station that shows an accuracy of 84%. This proves that our model is a reliable tool to predict future base station locations. This model will benefit the telecommunication industries to reduce time and cost for rapid base station planning in urban areas that see frequent changes to their landscapes.

Communication via quad/hexa-copters during disasters

PurposeThe purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.Design/methodology/approachIn this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.FindingsIt was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.Originality/valueThe research is original. None of the parts of this research paper has been published anywhere.

LoRa Communication Using TVWS Frequencies: Range and Data Rate

Low power wide area network (LPWAN) is a wireless communication technology that offers large coverage, low data rates, and low power consumption, making it a suitable choice for the growing Internet of Things and machine-to-machine communication applications. Long range (LoRa), an LPWAN technology, has recently been used in the industrial, scientific and medical (ISM) band for various low-power wireless applications. The coverage and data rate supported by these devices in the ISM band is well-studied in the literature. In this paper, we study the usage of TV white spaces (TVWS) for LoRa transmissions to address the growing spectrum demand. Additionally, the range and data rate of TVWS-based LoRa, for different transmission parameter values using different path-loss models and for various scenarios such as free space, outdoor and indoor are investigated. A path-loss model for TVWS-based LoRa is also proposed and explored, and the evaluations show that TVWS offers a longer range. This range and data rate study would be useful for efficient network planning and system design for TVWS-based LoRa LPWANs.

An Investigation on Effects of In-Leaf and Out-of-Leaf Conditions on Propagated Radio Broadcast FM Signal

The daily increasing desire for the right information at any place, anytime, and anywhere by people has made broadcast media indispensable media for disseminating information to the public. Propagation models are deployed in planning and designing wireless communication systems. Different environments do require a unique propagation model. In this paper, least squares regression analysis was utilized to create the path loss models for the in-leaf and out-of-leaf conditions of a teak (Tectona grandis) plantation. The developed model was found to be more suitable compared to the existing Weissberger’s and COST235 models because it gives the least difference in root mean square error of 3.9 dB in the two scenarios compared to COST 234 and Weissberger, which stand at 11.2 dB and 10.8 dB, respectively, and the developed model was closer to the assessed path loss obtained from the measurement carried out. The results of the study establish a standard model that can be deployed in the effective planning and design of wireless communication links for very high bands within the radial distance in the tropical rain forest of 30m to 45m foliage depth. This study confirms the need for distinctive models for radio signals at different locations under different conditions.

Measurement-Based Close-In Path Loss Modeling With Diffraction for Rural Long-Distance Communications

In this letter, we investigate rural large-scale path loss models based on the measurements in a central area of South Korea (rural area) in spring. In particular, we develop new close-in (CI) path loss models incorporating a diffraction component. The transmitter used in the measurement system is located on a hill and utilizes omnidirectional antennas operating at 1400 and 2250 MHz frequencies. The receiver is also equipped with omnidirectional antennas and measures at positions totaling 3,858 (1,262 positions for LOS and 2,596 positions for NLOS) and 4,957 (1,427 positions for LOS and 3,530 positions for NLOS) for 1400 and 2250 MHz, respectively. This research demonstrates that the newly developed CI path loss models incorporating a diffraction component significantly reduce standard deviations (STD) and are independent of frequency, especially for LOS beyond the first meter of propagation, making them suitable for use with frequencies up to a millimeter-wave.

Performance of Indoor 5G 3GPP Systems

Background: The 5G cellular networks are expected to provide high data rates for indoor environments by utilizing the mmWave bands. Due to a lack of general path loss models of these bands, various works have been carried out to find the most appropriate model that can provide accurate values of path loss over distance for different indoor scenarios. This paper focuses on some well-known path loss models, e.g., mixed and OpenOffice models introduced by 3GPP; the dualstripe model was presented by Alcatel-Lucent, picoChip Designs, and Vodafone at the 3GPP meeting; single and dual ABG models were designed by leading academic and industrial labs. Methods: Two network scenarios, e.g., single-cell and 3-cell networks, are utilized in simulation to analyze the effects of path loss models on network performance. Results: The simulation results indicate that the single-slop and dual-slop models can derive a similar performance when only a cell is deployed. However, in a 3-cell network system with intercell interference, the achieved performance in these systems is significantly different. Conclusion: The dual-slop model is better than the single–slope model in terms of path loss modeling since it can capture more characteristics of wireless transmission links, particularly the Light-of- Sight (LoS) and Non-LoS (NLoS) effects.

Vehicular Visible Light Communication With Low Beam Transmitters in the Presence of Vertical Oscillation

The availability of light-emitting diodes (LEDs) in vehicle exteriors makes possible the use of visible light communication (VLC) as a vehicle-to-vehicle (V2V) wireless connectivity solution. Although high beam headlights offer higher illumination levels and further communication distances compared to low beam headlights, regulations restrict high beam utilization in many driving cases. These restrictions make low beam headlights a more suitable candidate for vehicular VLC systems. In this paper, we consider two heavy vehicles (e.g., trucks) on a road following each other. Low beam headlights of the follower truck act as wireless transmitters and the lead truck is equipped with a photodetector at its back. We first propose an accurate path loss model based on experimental measurements. The proposed model takes into account the presence of lateral shift between the trucks as well as the fact that headlamps and photodetector can be placed at different heights. Furthermore, we model the truck vertical oscillation experimentally at different speeds. Utilizing these models, we derive a bit error rate (BER) expression for VLC-based truck-to-truck communication system and investigate the effect of several system and channel parameters on the performance. In an attempt to mitigate the degrading effects of the vertical oscillation, we further investigate the proper selection of photodetector location in order to minimize the error rate performance.

Path loss model based on exponential water cycle algorithm for wireless sensor network

SummaryWireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient . The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.

"Design of Wearable Antenna Sensors and Path Loss Modeling for Internet of Wearable Things Applications"

"Design of Wearable Antenna Sensors and Path Loss Modeling for Internet of Wearable Things Applications"

Adaptive Path Loss Model for BLE Indoor Positioning System

Indoor positioning systems (IPSs) allow the location and tracking of mobile devices in indoor environments where the Global Positioning System (GPS) does not provide satisfactory results. In model-based IPSs, it is common to use signal propagation models to estimate distances between anchor nodes and mobile devices using the Received Signal Strength Indicator (RSSI). However, using fixed parameters in the path loss model to characterize the signal in large-scale scenarios results in the degradation of the positioning accuracy. In this paper, we propose the ADAM (Adaptive Model) Positioning System, a model-based IPS that chooses the best anchor nodes to benefit the positioning computation and uses different parameters for the log-distance model to represent the signal in different regions and conditions of the scenario. Then, we estimate a single, more precise position using a data fusion technique. Our proposal does not require training nor prior knowledge of the best parameters for each region. We evaluated the performance of our proposed system in a real-world, large-scale environment using Bluetooth-based mobile devices. Our results clearly show that ADAM can locate mobile devices with an average error of 2.93 m in relation to the real position, which is 23% better than literature-based models using fixed parameters for the entire environment.

Performance Analysis of a Millimeter Wave Communication System in Urban Micro, Urban Macro, and Rural Macro Environments

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.

Evolutionary Ensemble Learning Pathloss Prediction for 4G and 5G Flying Base Stations With UAVs

The usage of unmanned aerial vehicles (UAVs) as flying base stations (FBSs) for expanding coverage and assisting the terrestrial cellular networks constitutes a promising technology for 5G and beyond. A crucial parameter affecting cellular network design is path loss prediction. An alternative to the accurate, though time-consuming, propagation prediction with deterministic Ray Tracing models could be Machine Learning (ML) based predictions. Ensemble Learning techniques are used in order to optimally combine the predictions of standalone models. That is, they combine the best-performing individual models into a better-performing meta-model. Our proposed method of the evolutionary tuned stacked ensemble optimizes the ensemble as a whole, instead of optimizing its individual base learners. To the best of our knowledge, this is the first time that an evolutionary technique is applied in order to mutually tune an ensemble’s base learners for a path loss modeling problem in electromagnetics. Moreover, we present a model that works in more than one frequency. As opposed to the standard implementation of ensemble learning, our method offers a significant performance boost with low complexity.

Energy‐optimized cluster head selection based on enhanced remora optimization algorithm in underwater wireless sensor network

SummaryWith the technological advancements, wireless sensor network (WSN) has played an impeccable role in monitoring the underwater applications. Underwater WSN (UWSN) is supported by WSN but subjected to data dissemination in an acoustic medium. Due to challenging conditions in underwater scenario, the limited battery resources of these sensor nodes stem to a crucial research problem that needs to address the energy‐efficient routing in UWSN. In this research work, we intend to propose an energy‐optimized cluster head (CH) selection based on enhanced remora optimization algorithm (ECERO) in UWSN. Since CH devours the maximum energy among the nodes, we perform selection of CH based on EROA while considering energy, Euclidean distance from sink, node density, network's average energy, acoustic path loss model and lastly, the adaptive quantity of CHs in the network. Further, to reduce the load on CH node, we introduce the concept of sleep scheduling among the closely located cluster nodes. The proposed work improves the performance of recently proposed EOCSR algorithm by great magnitude which claims to mitigate hot‐spot problem, but EOCSR still suffers from the same due to relaying a large magnitude of data.

PERFORMANCE ANALYSIS AND DEVELOPMENT OF PATH LOSS MODEL FOR TELEVISION SIGNALS IN IMO STATE, NIGERIA

It is impossible to overstate the importance of propagation models in wireless network planning, frequency assignment, and television parameter evaluation. The fact that no two locations are identical in terms of climatic conditions, building patterns, terrain, etc. makes using path loss predicting models for any area extremely challenging. Therefore, it is impossible to develop a single path loss model that applies to all environmental settings. The main aim of this study is to develop a path loss model for NTA channel 12 Owerri and evaluate its performance based on received signal strength values along five selected routes in Imo State, Nigeria.A suitable path loss model was developed by critically analyzing the measured path loss values of each base station, which were retrieved from the signal strength data received. The values of the developed path loss model were compared to those of other empirical path loss models developed by other researchers as well as the measured path loss values. The results show that the proposed path loss model is well suited for predicting the path loss of NTA channel 12 Owerri signals in the study environment, while the other conventional empirical models taken into consideration in this study overestimated the path loss of NTA channel 12 Owerri signals with Root Mean Square Error and Mean Error of 63.65 and above. Additionally, the findings indicate that NTA Owerri performs poorly at a distance of 18 kilometers from the base transmitting station. The overall findings are helpful for designing prospective television network channels in the study location and other similar environments.