Journal of Communication and Information Systems

Does hexagonal lattice improve the performance of QAM-FBMC?

2023-05-15T14:43:39-03:00

Quadrature amplitude modulation filter-bank multicarrier (QAM-FBMC) is a promising technology for future wireless communications systems. However, the intrinsic interference observed at the receiver can be a source of problems. In order to overcome the intrinsic interference, in this paper, we propose a QAM-FBMC system with hexagonal lattice structure, which we call HQAM-FBMC. We also propose a new prototype filter design, specifically for the HQAM-FBMC system, based on discrete prolate spheroidal sequences (DPSS). The proposed filters have also been optimized to reduce the intrinsic interference of the system. Moreover, we compare and analyze the performance of the QAM-FBMC system with the performance of the proposed HQAM-FBMC using the optimized filter through the bit error rate (BER).

Ultra-Wideband, Directive and Circular Polarization Lens Antennas for Future Communications

2023-04-11T06:05:44-03:00

This paper presents a potential featured antenna for future wireless communication networks. The use of a hemispherical dielectric lens combined with an equiangular spiral printed antenna is proposed to achieve a radiator design with high gain, operating in a frequency range between 8.0 GHz to 15.6 GHz in circular polarization. Numerical analyses performed by the ANSYS HFSS software, and experimental measurements validated the functionality of the proposed structure, reaching gains greater than 14.2 dBi.

A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images

2023-04-05T14:06:59-03:00

Glaucoma is an asymptomatic chronic eye disease that, if not treated in the early stages, can lead to blindness. Therefore, detection in the early stages is essential to preserve the patient’s quality of life. Thus, it is crucial to have a non-invasive method capable of detecting this disease through images in the fundus examination. In the literature, datasets are available with fundus images; however, only a few have glaucoma images and labels. Learning from an imbalanced dataset challenges machine learning, which limits supervised learning algorithms. We compared approaches to extract and classify three public datasets with 2390 images: ACRIMA, REFUGE, and RIM-ONE DL. First, we evaluated extracted features non-structural from HOG, LBP, Zernike, and Gabor filters and features obtained from transfer learning. Then, we classified them with Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). Finally, each classifier was evaluated individually and in a voting classifier (VOT). We extracted and classified features from transfer learning models in the same process. Also, they were classified using traditional machine learning. Due to class imbalance, we undersampled the majority class normal by applying the following methods: random choice, near miss, and cluster centroid. We also evaluated our model using a cross-dataset approach. Therefore, we efficiently identified glaucoma in different fundus images using network VGG19 and a voting classifier. In addition, balancing classes reduced false negatives and improved model quality. Our approach achieved an average F1-score equals to 94.69%, accuracy rate of 94.77%, precision of 96.10%, recall of 93.45%, and specificity of 96.08%.

Direct-Conversion Spectrum Sensor Impaired by Symmetric α-Stable and α-Sub-Gaussian Noises

2023-02-23T09:00:26-03:00

Spectrum sensing in underwater cognitive acoustic networks or in underwater acoustic sensor networks can be impaired by impulsive noise generated by snapping shrimps. In mathematical analysis or simulations, the amplitude variations of this noise are commonly modeled by the symmetric alpha-stable (SαS) distribution. As an alternative, the alpha-sub-Gaussian (SαG) distribution can model both temporal correlation and amplitude variations. This article assesses the performance of underwater spectrum sensing with a direct-conversion receiver (DCR) under impulsive noise modeled by the SαS and SαG distributions. Several recent test statistics are compared, demonstrating that they have different degrees of robustness against impulsive noise and that the DCR is significantly less sensitive to this noise, compared to the conventional receiver model that does not take into account the influences of hardware characteristics into the performance of spectrum sensing.

Deep Reinforcement Learning Based Resource Allocation Approach for Wireless Networks Considering Network Slicing Paradigm

2023-02-06T11:48:11-03:00

In this paper, we present an approach for resource scheduling in wireless networks based on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) Reinforcement Learning (RL) algorithm. More specifically, we propose a joint power and Scheduling Block (SB) allocation algorithm for networks with NS. The reinforcement learning algorithm applied to the resource allocation problem is formulated using state transitions regarding the system dynamics. We also present an algorithm, namely Network Slicing based on Reinforcement Learning (NSRL) that combines the proposed reinforcement learning based resource allocation with an approach based on reservation and sharing of resources among the slices where each RL agent acts in one slice. Simulations are carried out considering User Equipments (UEs) within a small cell coverage area - (Small Cells) with different Modulation and Coding Schemes (MCS) standardized by the 3rd Generation Partnership Project (3GPP) based on a simplified NS scenario with fifth generation wireless network (5G) characteristics. In the simulations, two slices are considered for the UEs: one considering Ultra-reliable and Low Latency Communications (URLLC) and other related to enhanced Mobile Broadband (eMBB) services. Simulation results show that the NSRL algorithm efficiently allocates power and SBs, outperforming other algorithms in the literature.

UWB Radar Applied to Lane Occupation and Vehicle Classification

2023-02-07T14:46:14-03:00

This article describes the use of a commercial UWB radar for vehicle classification and lane occupation detection using real-world data acquired in an urban environment. We compare two radar image processing schemes: one based on deep learning using raw data produced by the radar, and a second method employing traditional machine learning algorithms using features extracted from raw data. We verify experimentally that both schemes lead to reasonably accurate estimates without the need of large training sets.

A New Ultraminiaturized Low-profile and Stable FSS with 2.5D Structure for 900 MHz ISM Band

2023-02-01T07:44:25-03:00

This paper presents a new 2.5D ultraminiaturized frequency selective surface (FSS) structure to operate in the 900 MHz ISM band (902 to 928 MHz), for application in the Wi-Fi HaLow. The proposed FSS simulation and design are performed using ANSYS HFSS software and equivalent circuit model (ECM). The development of the proposed 2.5D FSS is based on meander-line-based conducting patches and has required the simulation and design of typical (2D) and 2.5D structures. An equivalent circuit model was proposed and presented very accurate results. For comparison purpose, a prototype is fabricated and measured. Agreement is observed between simulation and measurement results.

Multilayer Framework for Resource Orchestration in Next Generation Networks

2023-01-16T11:37:27-03:00

Due to the significant increase in data traffic and the large number of Internet Protocol (IP) devices, operators and researchers are seeking solutions to address the greater demand. One of the most attractive of these is Heterogeneous Cloud Radio Access Networks (H-CRAN), which has the capacity to solve problems of the current generation and add several improvements, such as centralized processing and greater energy efficiency. However, resource orchestration such as radio, mapping between radio and BaseBand Unit (BBU) and load balance in BBU pool are still of the utmost importance. This paper proposes a multilayer approach that enables Peak Remote Radio Head (PRRH)-underutilized reconfiguration model and optimized mapping between PRRH and BBU, with the aim of achieving high availability, energy savings and a reduction in high-speed processing. Obtained results were compared with other approaches in the literature and showed that our model offers a more efficient means of mitigating the problems addressed in this paper.

Index Encoding and Antenna Selection in Multiuser Precoder Index Modulation MIMO Communication

2022-11-11T09:27:28-03:00

Index modulation (IM) offers energy efficient solutions to communication systems by altering the on/off status of entities of the system. This work presents a multiuser (MU) IM-based system operating in a multiple-input multiple-output (MIMO) channel, named Multiuser Precoder Index Modulation (MU-PIM-MIMO), in which the choice of the IM-precoder matrices, responsible for assigning zero or nonzero values to the information vector, is a source of information. System model is specified for Zero-Forcing and Block Diagonalization channel precoders, as well as additional mechanisms, such as user notification and channel estimation. Numerical results show that MU-PIM-MIMO systems can offer attractive tradeoff between detection performance and spectral efficiency. Metrics for selection of the most favorable information bearing positions (IBP) patterns of the information vector, based on the maximization of the signal-to-noise ratio and on the maximization of the achievable rate, are developed in order to offer further improvements in system performance. Additionally, a scenario where the number of transmit antenna elements exceeds the number of radiofrequency chains at the base station is considered, and optimal and computational efficient ways to select the IBP patterns and the active transmit antennas are proposed. Simulation results evidence the effectiveness of the strategies.

On Generating Monte Carlo Simulations of Underwater Acoustic Communication Systems with Application to Transmit Beamforming

2022-11-03T16:33:29-03:00

Underwater Acoustic (UWA) communication systems still rely heavily on at-sea trials. This work presents an operational framework that significantly reduces the need for practical experiments. The key idea is to generate channel impulse responses (CIRs) drawn from probability density functions constructed based on trusted information and to employ Monte Carlo simulations to develop new UWA communication systems. Hence, the proposed operational framework depends only on cheaper-to-acquire physical measurements to produce CIRs. It comprises a model-based CIR replay tool and a stochastic-based UWA channel simulator. The former can be any model-based CIR replay tool, and the latter is proposed in this work and validated using data from four different practical experiments. We also carried out experiments for a transmit beamforming with signals digitally modulated in binary phase-shift keying, which were transmitted by an array and by a single source with equivalent power. For the array, the ideal transmit direction comes from the lowest bit error rate (BER) obtained with computer simulations. This paper compares the performance of the transmit array to the single source transmission and the results of a practical experimental transmission with a Monte Carlo simulation employing the proposed technique. We show that both achieved close results regarding BER and mean squared error. The conclusion is that the proposed operational framework, once adjusted to the specific transmission site, can be used to design new UWA communication systems, eliminating the burden of at-sea trials for tests of new transceivers. Finally, we conducted real-life transmit beamforming experiments to verify the BER gain obtained in practice using the steering angle obtained from simulations.