Absir-mi -In this paper we consider the application of some adaptive algorithms for the antenna array beam steering. Three spatial channel models ( Lee's Model, Gaussian Angle of Arrival Model and Discrete Uniform Distribution Angle of Arrival Model) and 12 -element antenna array are simulated. The antenna array beam steering is performed by using the adaptive algorithms: Least Mean Sgziare -LMS, Least Mean Fourth -LMF, Recursive Least Square -RLS and Sample Matrix Inversion -SMZ. The output antenna array SNIR is analyzed and plotted relating to the angular width and the iteration number the adaptive algorithms.
Designing an integrated telecommunications and computer network using a simulation model requires defining all the network and communication elements that make up the network and network communication. The accuracy, quality, and usability of the simulation results depend on the accuracy and the way of defining the network traffic as a timed event between the source and the destination in the network. In this paper, the method for a more accurate definition of network traffic required for the development of a simulation model using the software tool OPNET is proposed. Network traffic can be represented using a multigraph associated with an appropriate matrix of network traffic over time. Time definition of network traffic is enabled by applying the method of sampling multigraphs with the mathematical derivation of the corresponding statistical distribution function. Predicted or existing communication in the network describes using the derived function of network traffic distribution and precisely defines the OPNET simulation model.
Considering that networks based on New Radio (NR) technology are oriented to provide services of desired quality (QoS), it becomes questionable how to model and predict targeted QoS values, especially if the physical channel is dynamically changing. In order to overcome mobility issues, we aim to support the evaluation of second-order statistics of signal, namely level-crossing rate (LCR) and average fade duration (AFD) that is missing in general channel 5G models. Presenting results from our symbolic encapsulation point 5G (SEP5G) additional tool, we fill this gap and motivate further extensions on current general channel 5G. As a matter of contribution, we clearly propose: (i) anadditional tool for encapsulating different mobile 5G modeling approaches; (ii) extended, wideband, LCR, and AFD evaluation for optimal radio resource allocation modeling; and (iii) lower computational complexity and simulation time regarding analytical expression simulations in related scenario-specific 5G channel models. Using our deterministic channel model for selected scenarios and comparing it with stochastic models, we show steps towards higherlevel finite state Markov chain (FSMC) modeling, where mentioned QoS parameters become more feasible, placing symbolic encapsulation at the center of cross-layer design. Furthermore, we generate values within a specified 5G passband, indicating how it can be used for provisioningoptimal radio resource allocation.
This research focuses on an improved automatic target recognition algorithm for solving the classification challenge of ground-moving targets from pulsed-Doppler radar. First, it was studied how decision-making intervals affect the proposed algorithm. Second, the altering of the data augmentation process was investigated. Third, a consideration of the three time-frequency signal representations and finally the use of different deep learning models for the classification issues were examined. It is proven that the proposed algorithm can efficiently recognize all targets enclosed in the publicly available RadEch dataset, with 4 s of radar echoes. When the decision-making time is only 1 s, a classification probability of 99.9% was obtained, which is an improvement related to the other research studies in this area. Furthermore, when the decision-making time is reduced 16 times the classification accuracy is reduced by only 1.3%. Moreover, the proposed algorithm was successful on another dataset enclosing ground-moving targets from comparable pulsed-Doppler radar.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.