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In wireless networks, drone base stations (DBSs) offer significant benefits in terms of Quality of Service (QoS) improvement due to their line-of-sight (LoS) transmission capabilities and adaptability. However, LoS links can suffer degradation in complex propagation environments, especially in urban areas with dense structures like buildings. As a promising technology to enhance the wireless communication networks, reconfigurable intelligent surfaces (RIS) have emerged in various Internet of Things (IoT) applications by adjusting the amplitude and phase of reflected signals, thereby improving signal strength and network efficiency. This study aims to propose a novel approach to enhance communication coverage and throughput for mobile ground users by intelligently leveraging signal reflection from DBSs using ground-based RIS. We employ Deep Reinforcement Learning (DRL) to optimize both the DBS location and RIS phase-shifts. Numerical results demonstrate significant improvements in system performance, including communication quality and network throughput, validating the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project's scheduling aligns with the enterprise's sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as helpful technologies to enhance UAV communication networks. However, due to the high mobility of UAVs, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. The simulation results showed that the proposed GRU model can effectively and accurately estimate the link quality of ground users in the RIS-assisted UAV-enabled wireless communication network. [ABSTRACT FROM AUTHOR]

This paper proposes a common-mode noise suppression filter scheme for use in the servers and computer systems of high-speed buses such as SATA Express, HDMI 2.0, USB 3.2, and PCI Express 5.0. The filter uses a novel series-mushroom-defected corrugated reference plane (SMDCRP) structure. The measured results are similar to the full-wave simulation results. In the frequency domain, the measured insertion loss of the SMDCRP structure filter in differential mode (DM) can be kept below −4.838 dB from DC to 32 GHz and can maintain signal integrity characteristics. The common-mode (CM) suppression performance can suppress more than −10 dB from 8.81 GHz to 32.65 GHz. Fractional bandwidth can be increased to 115%, and CM noise can be ameliorated by 55.2%. In the time domain, using eye diagram verification, the filter shows complete differential signal transmission capability and supports a transmission rate of 32 Gb/s for high-speed buses. The SMDCRP structure filter reduces the electromagnetic interference (EMI) problem and meets the quality requirements for the controllers and sensors used in the server and computer systems of high-speed buses. [ABSTRACT FROM AUTHOR]

This article describes a broadband common‐mode filter for suppressing common‐mode (CM) noise in high‐speed differential signals. The filter used a novel defected corrugated reference plane (DCRP) structure. It used structure changes to impact the characteristic impedances in the CM current return path, which caused many approach transmission zeros. The main purpose generated a larger CM noise stopband effect. The experimental results showed that the frequency domain can reduce the CM noise over 9.29 dB from 3.67 to 17.03 GHz. The fractional bandwidth was 129%. The CM noise can be improved by 68.2%. The insertion loss in differential mode (DM) was kept at less than −1.611 dB from DC to 20 GHz with good signal integrity. The eye diagram can support a 16 Gb/s transmission rate in the time domain. The DCRP structure filter supports high‐speed computer buses up to PCI Express 4.0, SATA Express, HDMI 1.4, and USB 3.2, which meet current computer products' needs. [ABSTRACT FROM AUTHOR]

This paper explores applications of the coordinated multi-point (CoMP) architecture operation of enhanced node B (eNB) in wireless communication networks featuring device-to-device (D2D) signaling. This is applied to cellular phone coverage for rapid mass transit systems, such as the Taiwan high speed rail transport system, and indoor public environments. The paper is based on formulas pertaining to the link between budget design and guidelines, as well as principles and theories of engineering practice, allowing designers to analyze and fully control the uplink and downlink signals and output power of fiber repeaters linking cellular phones to base stations. Finally, we employ easily installed cellular-over-fiber optic solutions for a small cell enhancement (SCE) system with novel architecture based on a leakage coaxial cable system using LTE-A technology. As a result, we successfully applied enhanced coverage designs for distributed antenna systems. These can be used to create self-organizing networks (SoN) for an Internet of Things. [ABSTRACT FROM AUTHOR]

A novel wideband common-mode (CM) suppression filter is proposed for high-speed transmission. The filter is embedded in 3 10 mm × 10 mm layers of a printed circuit board (PCB) that combines a mushroom structure and a defected corrugated reference plane structure (MDCRP). Using the novel MDCRP structure generates more resonance frequencies in the CM current return path. This generates a wider CM noise suppression performance. We used a simulation method to obtain the best geometric parameters for the MDCRP structure. The experimental results proved that the full-wave simulation results were consistent with the actual measurement results. This novel filter shows good signal integrity according to actual measurements, and the insertion loss can be kept to less than −2.306 dB from DC to 21 GHz in differential mode (DM). The CM noise can be suppressed by over −10 dB from 5.09 GHz to 20.62 GHz. The fractional bandwidth is 120.8%, and the CM noise improves by 64.5%. An eye diagram proves that the filter can support a 20 Gb/s transmission rate with complete differential signal transmission capability. The MDCRP structure filter can support HDMI 2.0, PCI Express 4.0, USB 3.2, and SATA Express. Therefore, the filter meets current computer and server system products' design needs. [ABSTRACT FROM AUTHOR]

The author of this paper explored Vivaldi [1] [2] line theory and technology and used it as a basis to propose a Vivaldi antenna array to replace a single Vivaldi antenna. This was to achieve a dual-polarized antenna with high directivity and high isolation in the MIMO anechoic chamber, and one that is minimally affected by the environment. The operating frequency of this antenna array covers a frequency range of 0.7–6.0 GHz and is composed of four Vivaldi units with relatively high isolation between them to reduce measurement errors caused by coupling. In each unit, two identical Vivaldi antennas are connected in parallel to form the same polarization unit, and a microstrip power divider was used and the impedance matching of circular holes was performed to design this connected antenna with an ultra-wide operating frequency and the same polarization. The authors then interconnected two polarization units orthogonally at 90 degree cross to form the antenna described in this study, which has high directivity, high isolation, an ultra-wide frequency and dual polarization. During the design process, an FR4 printed circuit board (PCB) was used to effectively reduce the cross-sectional area of the antenna and reduce reflection and interference on the basis of ensuring an ultra-wide operating frequency. Additionally, the two orthogonal units of each polarized antenna unit had to work separately, and the electric field data collected from different polarization directions were sequentially transmitted to the receiver for postprocessing to satisfy the measurement requirements of the MIMO OTA anechoic chamber. In this study, SEMCAD electromagnetic simulation software was used to adjust and complete the analysis of antenna characteristics and obtain a favorable operating frequency and voltage standing wave ratio, as well as excellent isolation and radiation characteristics.

A precise localization system is a key enabling technology for Internet of Things (IoT) applications and location-based services. Fingerprint-based localization methods are well-known and widely used solutions. These methods, however, are time-consuming and laborious for radio map construction during an offline site survey in large-scale applications. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network-based radio map construction method for real-time device localization. The proposed synthetic radio map construction method for fingerprinting outdoor localization (SRCLoc) combined the hybrid support vector machine and deep gated recurrent unit algorithms sequentially. The SRCLoc reduced the workload of site surveying required to build the fingerprint database by up to 85.7%. The results show that the average positioning error of SRCLoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs. [ABSTRACT FROM AUTHOR]

An accurate propagation channel model is crucial for evaluating the performance of a communication system. A propagation channel can be described by a Markov model with a finite number of states, each of which is considered to be quasi-stationary over a short period. This work proposes a two-layer multistate Markov model. Instead of a large Markov transition matrix used in a conventional single-layer Markov model, two small Markov transition matrices are employed by a two-layer Markov model to reduce the computational complexity of the model without increasing the memory requirements. The proposed approach characterizes the multiplicative processes of a propagation channel as shadowing and fast fading. Each type of fading is considered as several channel states and each of the states corresponds to a specific mixed Rayleigh-lognormal distribution. Numerical results reveal that the statistical properties of the simulated data are quite close to those obtained from the measurements; indeed, the proposed two-layer Markov model is more accurate and less complex, and requires less memory than the single-layer Markov model. Furthermore, the proposed two-layer Markov model enables the fading statistics and error probability performance of a quadrature phase-shift keying modulation scheme in a typical urban Taipei environment to be more accurately predicted. Besides, it can easily be applied to similar environmental scenarios. Index Terms--Markov model, propagation channel modeling.

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air‐to‐ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal‐to‐noise‐ratio, throughput values, the categories of line‐of‐sight, and non‐line‐of‐sight links. Furthermore, K‐means used as a clustering method for class identification, long short‐term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi‐layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks. [ABSTRACT FROM AUTHOR]

A spatial signature is the response vector of a base-station antenna array to a mobile unit at a certain location. Mobile subscribers at different locations exhibit different spatial signatures. The exploitation of spatial diversity (or the difference of spatial signatures) is the basic idea behind the so-called space-division multiple-access (SDMA) scheme, which can be used to significantly increase the channel capacity and quality of a wireless communication system. Although SDMA schemes have been studied by a number of researchers [1]-[6], most of these studies are based on theoretical analyses and computer simulations with ideal assumptions. Not much experimental study [7], [8] has been reported on spatial signature variation due to nonideal perturbations in a real wireless communication environment. The purpose of this paper is to present, for the first time, extensive experimental results of spatial signature variation using a smart antenna testbed. The results to be presented include the spatial signature variation with time, frequency, small displacement, multipath angle spread and beamforming performance. The experimental results show the rich spatial diversity and potential benefits of using an antenna array for wireless communication applications. Index Terms - Adaptive arrays, mobile communication.

In wireless communications, smart antenna systems (or antenna arrays) can be used to suppress multipath fading with antenna diversity and to increase system capacity by supporting multiple co-channel users in reception and transmission. This paper presents experimental results of diversity gain, interference cancellation, and mitigation of multipath fading obtained by using a smart antenna system in typical wireless scenarios. Also given are experimental results for the signal-to-interference ratio (SIR) of two moving users, comparing different beamforming algorithms in typical wireless scenarios. All of the experiments were performed using the 900-MHz smart antenna testbed at The University of Texas at Austin. Index Terms - Antenna arrays, mobile antennas, multipath channels.

This paper presents results from simultaneous Land S-band slant-path fade measurements through a pecan, a cottonwood, and a pine tree employing a tower-mounted transmitter and dual-frequency receiver. A single circularly polarized antenna was used at each end of the link. The objective was to provide information for personal communications satellite up-link power control design on the correlation of tree shadowing between frequencies near 1620 and 2500 MHz. Fade time series obtained simultaneously did not exhibit any significant correlation, but their means were weakly correlated ([r.sup.2] from 0.31 to 0.58). Fades were measured along a 10-m lateral distance with 5-cm spacing. Instantaneous fade differences between L- and S-band exhibited a normal distribution with means usually near 0 dB and standard deviations from 5.2 to 7.5 dB. The cottonwood tree was an exception with 5.4 dB higher average fading at S-than at L-band. More than 90% of the spatial variations occurred with periods larger than 1 [similar to] 2 wavelengths. Simultaneous swept measurements over 160-MHz spans showed that the standard deviation of the power levels as function of frequency increased from about 1 dB at locations with mean fades less than 4 dB to near 6 dB at locations with mean fades of 20 dB. For a 5-dB fade, the central 90% of fade slopes were within a band of 0.7 dB/MHz at L- and 1.9 dB/MHz at S-band.

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users' resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity. [ABSTRACT FROM AUTHOR]

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air‐to‐ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal‐to‐noise‐ratio, throughput values, the categories of line‐of‐sight, and non‐line‐of‐sight links. Furthermore, K‐means used as a clustering method for class identification, long short‐term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi‐layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a popular system architecture for monitoring application such as building, industrial or environment. IoT system produces amount of data that is difficult for operator to process. Decision support system is an information that assists the system administrator to decide a decision when facing a problem. Moreover, the common wireless communication technology to build the IoT system is Wi-Fi, ZigBee and Bluetooth that have weakness in the coverage area. The weak signals were usually found when implement in smart building application. In this research, we applied Narrow Band Internet of Things (NB-IoT) to create a building information management system as well as used Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) to build the decision support system for building information management. The proposed system was applied in a building which has basement, first floor, and second floor. Each floor was installed end node which consist of sensors, esp-32 and M3510 (NB). Those are three kinds of nodes function in our proposes system, (1) nodes for building, (2) nodes for equipment, and (3) nodes for human activity. The sensors array for node building are placed on windows, doors and glass wall. The human activity nodes recorded from sensor on front door, Passive Infrared sensors and sensor on back door. For equipment management, sensors were placed to monitor pump and water level. The Decision System in this research was built by using the SVM and KNN. Both of SVM and KNN analyzed and decided the decisions based on data from end node. Based on experiment, the proposed NB-IoT design was able to solve the coverage area problem by replacing the Wi-Fi, ZigBee and Bluetooth. The sensor measurements were perfectly transmitted through NB-IoT and completely recorded in server. The proposed system was work perfectly to monitor, record and classify the normal and abnormal condition when received the alert information from conventional monitoring system. The accuracy of proposed SVM and KNN methods are 96.9% and 94.1%, respectively. The SVM performance is higher than KNN. [ABSTRACT FROM AUTHOR]

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory. In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments. [ABSTRACT FROM AUTHOR]

Indoor localization is used in many applications like security, healthcare, location based services, and social networking. Fingerprinting-based methods are widely used for indoor localization. But received signal strength (RSS) variation due to device diversity and change of conditions in the localization environment (e.g., distribution of furniture, people presence and movement, and opening and closing of doors) induce a significant localization error. To overcome this, we propose a hybrid indoor localization system using radio frequency (RF) and pyroelectric infrared (PIR) sensors. Our localization system has two stages. In the first stage, the zone of the target person is identified by PIR sensors. In the second stage, we apply K-nearest neighbor (K-NN) algorithm to the fingerprints within the zone identified and estimate position. Zone based processing of fingerprints will exclude deviated fingerprints because of RSS variation. We proposed two localization methods: Proposed_1 and Proposed_2 which use signal strength difference (SSD) and RSS, respectively. Simulation results show that the 0.8-meter accuracy of Proposed_1 achieves 84% and Proposed_2 achieves 65%, while traditional fingerprinting and SSD are 46% and 28%, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT In this work, we propose a novel solution for a switched beamformer called the adjustable phased array system (APAS). By programming electronic switches, the implemented four-antenna APAS circuit generates 12 phase-shift angles that are multiples of [ABSTRACT FROM AUTHOR]

An accurate propagation channel model is crucial for evaluating the performance of a communication system. A propagation channel can be described by a Markov model with a finite number of states, each of which IS considered to be quasi-stationary over a short period. This work proposes a two-layer multistate Markov model. Instead of a large Markov transition matrix used in a conventional single-layer Markov model, two small Markov transition matrices are employed by a two-layer Markov model to reduce the computational complexity of the model without increasing the memory requirements. The proposed approach characterizes the multiplicative processes of a propagation channel as shadowing and fast fading. Each type of fading is considered as several channel states and each of the states corresponds to a specific mixed Rayleigh-lognormal distribution. Numerical results reveal that the statistical properties of the simulate data are quite close to those obtained from the measurements; indeed, the proposed two-layer Markov model is more accurate and less complex, and requires less memory than the single-layer Markov model. Furthermore, the proposed two-layer Markov model enables the fading statistics and error probability performance of a quadrature phase-shift keying modulation scheme in a typical urban Thipei environment to be more accurately predicted. Besides, it can easily be applied to similar environmental scenarios. [ABSTRACT FROM AUTHOR]

The concept behind path diversity is that a user who can access several satellites simultaneously will be able to communicate more effectively than a user who could only access one. The success of this method depends on the environment, the satellite constellation, and diversity combining technology. This paper explores the path diversity effects of non-geostationary orbit (NGO) satellite personal communication services, for different degrees of user mobility, under various scenarios, using the constellation of the global positioning system (GPS). Measurements are taken near downtown Taipei. Three types of mobilities (fixed-point, pedestrian, and vehicular) are examined, and the switch diversity and maximum ratio combining method are applied to determine the path diversity gain and calculate bit error probability. The error probability performance of applying diversity schemes in coherent binary phase shift keying (BPSK) and non-coherent differential phase shift keying (DPSK) modulations over Rician fading channels are also analysed and evaluated by using the characteristic function method. The results show that fading can be significantly reduced and diversity greatly increased. A significant diversity gain and improvement in bit error rate (BER) can be expected in all cases by simply applying switch diversity scheme. Besides, for the maximum ratio combining method, the results imply that summing two satellite signals suffices to increase diversity and improve the bit error rate performance. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) systems are being considered as candidates for 5G wireless systems due to their promise of improved spectral efficiency. NOMA schemes are being combined with popular multicarrier schemes such as orthogonal frequency division multiplexing (OFDM) to take advantage of the benefits of multicarrier signals. A variant of the power domain NOMA is Layer Division Multiplexing (LDM). The most commonly deployed power domain LDM scheme involves successive interference cancellation (SIC) based decoding at the receiver. Fast convolution based filtered-OFDM (FC-F-OFDM) systems are becoming popular among 5G wireless access technologies due to their ability to process 5G physical layer signals efficiently. In this work, firstly, a cognitive multicarrier non-orthogonal multiplexed system based on the concept of LDM is discussed, which uses FC-F-OFDM and conventional OFDM as its component layers. Secondly, cyclostationary FREquency SHift (FRESH) filter based SIC decoding is used at the receiver side, which also utilizes artificial neural network (ANN) processing. Computer simulations indicate that the system provides good bit error rate (BER) performance under frequency selective Rayleigh fading channels. [ABSTRACT FROM AUTHOR]