Network Path Loss Research Articles

Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

Analysis and optimization of 4G / LTE network pathloss using Particles Swarm Optimization algorithm

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.

Design and Construction of 2.4 Ghz Omnidirectional Antenna as Wireless LAN Transmitter (Case Study at Bukit Indah Campus, Malikussaleh University)

Communication with a wireless system is one of the mainstay communications so that communication integration is achieved. For this reason, Malikussaleh University provides a wifi network on each campus, one of which is the Bukit Indah campus. So that the achievement of wifi access points on mobile devices needs to be improved so that it is achieved properly. Therefore it is necessary to optimize the placement of access points on the Malikussaleh University wifi network. Modeling an omnidirectional antenna that has a range of 360 ° is one solution. The method applied is to design and build an omnidirectional antenna on the Bukit Indah Campus of Malikussaleh University. Based on simulations on Mobile Radio, the largest network pathloss is 116.2 dB and the smallest is 102.0 dB. The results of omidirectional antenna testing conducted 5 times obtained the largest download speed worth 3.18 Mbps with 314ms ping at a distance of 30 meters and the smallest download speed worth 0.55Mbps with 195ms ping at a distance of 150 meters. The farther the distance of the antenna the lower the ping and packet loss, from the test results obtained an average ping of 267.8ms and the largest packet loss percentage of 0.4%. The ideal distance for this antenna is 30 to 110 meters and can even get a longer distance provided there are no obstacles. With this distance it is ideal for an assembled antenna with makeshift materials that we often encounter in everyday life.

PLANNING OF 5G NETWORK PATH LOSS IN GEOMETRY BASED STOCHASTIC CONCEPT BY USING LINEAR REGRESSION METHODS

This research is a continuation of several previous studies that made 5G network planning using the Free Space Reference Path Loss model. In this study, a 5G network path loss planning was made using the Geometry Based Stochastic model. A forecasting system is created that connects the path loss with the distance between the transmitter and the receiver antenna using the linear regression method. It is important to look at 5G network planning on a different side. The result shows that the path loss value in the light of sight condition is better than the non-light of sight condition with the lowest value of 94.4271 dB at the frequency of 28 GHz and 99.5856 dB at the 73 GHz frequency. Linear Regression analysis shows that the best path loss calculation is the frequency 28 GHz of LOS conditions with MSE is 0.001 and the standard deviation error is 0.0319.

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

SummaryPath loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

A Survey of Routing Issues and Associated Protocols in Underwater Wireless Sensor Networks

Underwater wireless sensor networks are a newly emerging wireless technology in which small size sensors with limited energy and limited memory and bandwidth are deployed in deep sea water and various monitoring operations like tactical surveillance, environmental monitoring, and data collection are performed through these tiny sensors. Underwater wireless sensor networks are used for the exploration of underwater resources, oceanographic data collection, flood or disaster prevention, tactical surveillance systems, and unmanned underwater vehicles. Sensor nodes consist of a small memory, a central processing unit, and an antenna. Underwater networks are much different from terrestrial sensor networks as radio waves cannot be used in underwater wireless sensor networks. Acoustic channels are used for communication in deep sea water. Acoustic signals have many limitations, such as limited bandwidth, higher end-to-end delay, network path loss, higher propagation delay, and dynamic topology. Usually, these limitations result in higher energy consumption with a smaller number of packets delivered. The main aim nowadays is to operate sensor nodes having a smaller battery for a longer time in the network. This survey has discussed the state-of-the-art localization based and localization-free routing protocols. Routing associated issues in the area of underwater wireless sensor networks have also been discussed.

다중 셀 MIMO 하향채널의 셀 에지 SINR

In this paper, we consider 19 cells with the two tiers for polar-rectangular coordinates (PRCs) and provide the cell edge performance of cellular networks based on distance from cell center i.e., BS (base station). When FFR is applied(or adopted) to cell edge, it is expected that BS cooperation, or a coordinated multipoint (CoMP) multiple access strategy will further improve the system performance. We proposed a new method to evaluate the sum rate capacity of the MIMO DC of multicell system. We improve the performance of cell edge users for intercell interference cancelation in cooperative downlink multicell systems. Simulation results show that the proposed scheme outperforms the reference schemes, in terms of cell edge SINR (signal-to-interference-noise ratio) with a minimal impact on the network path loss exponent. We show 13 dB improvements in cell-edge SINR by using reuse of three relative to reuse of one. BS cooperation has been proposed to mitigate the cell edge effect.

Research on the Characteristics of Data Transmission of Wireless Network

The paper discusses the application of the wireless data transmission in the industrial control system and analyses the characteristics of the data transmission. It gives the different characteristics of data transmission in the four experiments with the Zigbee Protocol. By comparing the result of the wireless network-path loss model and experiments, it shows that the path loss, multiple-point jamming, communication barrier primarily affect the stability of data transmission.