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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 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]

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]

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]

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]

In order to communicate with target unmanned aerial vehicles (UAVs), ground control stations (GCSs) typically adopt adaptive beamforming with high antenna gain and co-channel interference rejection. Multiple interfering signals arriving from different directions arise from other UAVs and other GCSs, and the beamformer installed in the home GCS will usually attempt to null unwanted signals from all these directions of arrival (DoAs) without analyzing the distribution of the angles of arrival. Consequently, the beamformer will fail to allocate nulls in some directions, and the signal-to-interference-plus-noise (SINR) performance of the home GCS is impaired. In this paper, a new approach to null allocation is proposed, based on machine learning using k-means clustering. The design first involves the collection of information about the DoAs and the corresponding received signal strengths of all the interfering signals into a two-dimensional dataset. Secondly, this dataset is broken down into clusters by using k-means clustering, and the cluster centroids are calculated. In each cluster, the interfering signal that has the shortest Euclidean distance to the centroid is identified as the approximated centroid. Only the approximated centroids are selected as input to the beamformer, with the aim that each complete cluster of interference sources can be nulled by allocating one null per cluster. To optimize the number of clusters k used in the null allocation process, the design adopts the particle swarm optimization technique to adaptively update the value of k to maximize the SINR at the home GCS. Simulation results show that our design yields a maximum SINR improvement of about 12 dB when compared to cases where no null allocation is considered. Moreover, our design also outperforms null steering in the UAV scenarios. Advantageously, this enhanced performance is obtained without the need for additional power amplification or hardware modification to the beamformer. [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]

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]

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]

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]

Without centralized frequency reuse coordination, unlicensed Wi-Fi spectrum users suffer from strong co-channel interference. The conventional Wi-Fi spectrum co-existence mechanism, carrier-sense multiple access with collision avoidance, temporarilly pauses the packet transmission for co-channel interference but still raises ambient noise level. In this paper, we propose the channel selection strategies for unlicensed Wi-Fi users in both the static case and the moving case. For static users, the strategy is to use channel switching implemented on a commercial off-the-shelf access point. This strategy senses the situation of current channel usage and searches for an idle channel if the currently used channel is occupied by other acess points. The designed strategy then manipulates the access point to switch to the idle channel. Experimental results show that the designed channel switching strategy outperforms the carrier-sense multiple access with collision avoidance with about 88% increment of the data throughput measured. However, the moving users may not accept the service outage due to the hardware channel switching. Therefore, a novel design of prediction based channel selection strategy without channel switching using deep learning is proposed, including the deep learning based channel prediction strategy and the time-to-live based algorithm. At each point on the trajectory of the moving user, the presented prediction strategy firstly predicts the three channels that have the strongest interference power with the deep neural network. The presented algorithm then rules out these interference-rich channels. Finally, a channel with lower interference can be found and selected for the moving users. Performance evaluation results show that along the trajectory, in compared with the conventional selection of non-overlapping channel, the presented channel selection strategy decreases up to about 36 dB in the interference power level and enhances up to about 37 dB in signal-to-interference-plus-noise ratio. • For Wi-Fi, we prove that the channel selection outperforms spectrum co-existence in tackling co-channel interference. • We suggest that static and moving Wi-Fi users need different channel selection strategies. • For static Wi-Fi users, our proposed strategy provides 88% data throughput improvement from spectrum co-existence. • For moving Wi-Fi users, our deep learning based strategy provides the best indoor signal-to-interference-plus-noise ratio. • We recommend the ways of migrating our strategies into real-world device manufacturing. [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]

Based on the basic principle of spread spectrum systems, this paper proposes a hybrid automatic-repeat-request (HARQ) scheme with interference mitigation for multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The proposed hybrid media access control (MAC) and physical layer (PHY) design orthogonally spreads signals in the transmission domain to separate the desired signals from interferences. It not only improves the signal-to-noise ratio (SNR) but also suppresses intercarrier interference (ICI) and interantenna interference (IAI). Almost without increasing complexity, mathematical analyses and numerical simulations show that the proposed scheme outperforms the conventional Chase combining HARQ. [ABSTRACT FROM AUTHOR]