Modern Wireless Communication Systems Research Articles

Communication spectrum prediction method based on convolutional gated recurrent unit network

In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Performance Analysis and Simulation of IRS-Aided Wireless Networks Communication

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus.

A Compact Bandpass Filter with Active Switchable Passband Status

In order to meet the increasing demand for adjustable devices in the modern wireless communication system, a compact bandpass filter with active switchable passband is proposed. The filter is based on the half mode substrate integrated waveguide (HMSIW), and uses its cut-off characteristics to form the lower stopband. The resonant characteristics of a quarter-wavelength shorted stub generates a transmission zero (TZ) at higher frequency, which forms the upper stopband. The active control of DC power supply determines the biased states of PIN diodes to change the electrical size of the filtering parts. When the diodes are reverse biased, the filter works in C band with the passband of 3.9-5.2 GHz; when the diodes are forward biased, the passband shifts to S band of 2.7-3.6 GHz. The 28.6% relative bandwidth in both frequency bands remains constant. The simple design realizes the active switching between bands, providing a promising idea for active adjustable devices.

Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.

Applying Characteristic Impedance Compensation Cut-Outs to Full Radio Frequency Chains in Multi-Layer Printed Circuit Board Designs.

Modern wireless communication systems are of utmost importance to various sectors such as healthcare, education, the household, and the advancement of emerging technologies like the internet of things, autonomous vehicles, and the enhancement of 5G. Further development and improvement of these systems drives the need for small dimension, high integration and density, and cost-effective electronic devices. Achieving optimal performance in wireless electronic devices involves overcoming engineering challenges related to microstrip line signal integrity. This research addresses the impact of surface mount technology (SMT) component pads on signal integrity, proposing a novel high-frequency microstrip line structure for mitigating impedance discontinuities. The study introduces stepped microstrip lines and explores characteristic impedance compensation techniques. A six-layer printed circuit board (PCB) structure is presented, and the effects of compensation on signal integrity are analyzed using time-domain reflectometry and scattering parameter measurements. The results demonstrate the effectiveness of compensation methods in aligning characteristic impedance with desired values, thereby ensuring improved impedance matching and transmission coefficients. The average over-the-length impedance for the proposed structure with compensation applied was measured to be 52.7 Ω, which is only 1.3 Ω (2.5%) more than that of the reference microstrip. Applying reference plane cut-outs leads to a maximum compensated absolute value of more than 30 Ω to reach the target impedance with a 10% tolerance. This research contributes valuable insights for advancing wireless communication systems and maintaining robustness in high-frequency microstrip transmission lines.

Circularly Polarized Metamaterial Patch Antenna Circuitry for Modern MIMO Applications

This research is an attempt to highlight that the presented antenna design is proposed for modern wireless communication systems, including 5G networks. The proposed antenna design is structured to realize a reconfigurable antenna terminal to suit smart wireless systems. Therefore, the proposed antenna is structured from twelve-unit cells of metamaterial (MTM) inclusions. For this, effectively, the proposed antenna profile is reduced to 0.21λ0, where λo is the free space wavelength at 2.45GHz, which occupies 40 × 30 mm2 equivalently. This is accomplished by using T-resonator inductors to conduct 3rd iteration Hilbert-shaped MTM inclusions. The proposed antenna is printed on a substrate to cover the frequency bands from 2.6GHz to 4.4GHz. To optimize the antenna performances, a numerical-parametric analysis based on CST MWS commercial software package formulations is invoked for this study. The suggested antenna performance is numerically evaluated for validation using the HFSS software suite. The antenna has great performance while being sufficiently small to be used with integrated electronics.

Design and Simulation of Elliptical Shaped Microstrip Patch Antenna Using HFSS

Abstract: The elliptical patch antenna presents a comprehensive study on the design, fabrication, and performance analysis of an operating at 2.4 GHz, employing FR4 substrate material. The elliptical shape is chosen for its unique radiation characteristics and compact form factor, making it suitable for integration into modern wireless communication systems. The design process involves careful consideration of key parameters, including patch dimensions, feedline structure, and ground plane geometry. Advanced simulation tools, such as electromagnetic simulators, are employed to optimize the antenna's performance, achieving desirable characteristics such as low return loss and enhanced bandwidth. Utilizing FR4 as the substrate material offers several advantages, including costeffectiveness, ease of procurement, and compatibility with standard PCB manufacturing processes. The dielectric properties of FR4 are thoroughly analyzed and incorporated into the antenna design process to ensure accurate simulation results. The proposed elliptical patch antenna has promising applications in various wireless communication systems, including Wi-Fi, Bluetooth, and IoT devices, operating in the 2.4 GHz frequency band. Its compact size and robust performance make it a compelling choice for space-constrained and high-performance wireless communication applications. This contributes valuable insights into the design and optimization of patch antennas, particularly those using FR4 substrates, and serves as a foundation for the development of efficient and cost-effective wireless communication solutions in the 2.4 GHz spectrum.

Implementation of a Wideband Microwave Filter Design with Dual Electromagnetic Interference (EMI) Mitigation for Modern Wireless Communication Systems with Low Insertion Loss and High Selectivity.

By leveraging the advantages of the uniform transmission line, this manuscript presents a broadband high-selectivity filter range starting from 2.5 GHz to 16.8 GHz, utilizing a simple uniform transmission line structure loaded with three-quarter-wavelength stubs. The proposed UWB filter is studied using the ABCD network parameter method. After that, a shorted T-shaped stub-loaded resonator is coupled with the transmission line of the UWB filter to obtain dual-notch features at 4.4 GHz (for long distance wireless ISPs (WISPs), 4G/5G operator for LTE backhaul) and 7.5 GHz (for X-band downlink communication). The overall footprint is specified as 22.5 mm × 12 mm or 1.12 λg × 0.6 λg, where λg represents the wavelength at the central frequency. The operating principle of such a filter is explained, and its controllable broadband response, as well as controllable stopband frequencies, are optimized to show some of the attractive features of the new scheme, such as a super wideband response of about a 148.18% fractional bandwidth; an out-of-band performance up to 25 GHz; five single-resonator transmission poles filtering behaviour at different frequencies, with highly reduced radiation losses greater than 10 dB; a simple topology; a flat group delay; a low insertion loss of 0.4 dB; and high selectivity. Additionally, the filter is fabricated and evaluated, and the results show a good match for experimental validation purposes.

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

The most important issues for improving the performance of modern wireless communication systems are interference cancellation, efficient use of energy, improved spectral efficiency and increased system security. Beamforming Array Antenna (BAA) is one of the efficient methods used for this purpose. Full band BAA, on the other hand, will suffer from a large number of controllable elements, a long convergence time and the complexity of the beamforming network. Since no attempt had previously been made to use Partial Update (PU) for BAA, the main novelty and contribution of this paper was to use PU instead of full band adaptive algorithms. PU algorithms will connect to a subset of the array elements rather than all of them. As a result, a common number of working antennas for the system's entire cells can be reduced to achieve overall energy efficiency and high cost-effectiveness. In this paper, we propose a new architectural model that employs PU adaptive algorithms to control and minimize the number of phase shifters, thereby reducing the number of base station antennas. We will concentrate on PU LMS (Least Mean Square) algorithms such as sequential-LMS, M-max LMS, periodic-LMS, and stochastic-LMS. According to simulation results using a Uniform Linear Array (ULA) and three communications channels, the M-max-LMS, periodic LMS, and stochastic LMS algorithms perform similarly to the full band LMS algorithm in terms of square error, tracking weight coefficients, and estimation input signal, with a quick convergence time, low level of error signal at steady state and keeping null steering's interference-suppression capability intact.

Design of High-efficiency Continuous Class F Doherty Power Amplifier

The high efficiency of the saturation state and rollback state is an important part of the front end of modern wireless communication systems. Improving the efficiency of RF power amplifiers is of great significance for energy saving and amplifier performance. Based on the traditional Doherty power amplifier, the harmonic suppression network is introduced before the fundamental frequency matching, so that the second harmonic impedance and third harmonic impedance can achieve continuous class F impedance matching conditions, and then the saturation efficiency and regression efficiency can be improved without affecting the fundamental wave matching. The results of the simulation show that the saturation efficiency of the Doherty amplifier reaches 73.94%~77.22%. The drain efficiency of back-off 6 dB reaches 61.80%~62.92%. The average saturation drain efficiency is 75.58%, and the average back-off 6 dB drain efficiency is 62.36%.

Editorial on “Design, Analysis, and Measurement of Antennas”

The area of modern wireless communication systems has experienced rapid growth, leading to a rising demand for multifunctional devices capable of providing various wireless services [...]

A novel broadband windmill‐shaped metasurface antenna with stable radiation patterns

AbstractA novel windmill‐shaped metasurface (MTS) structure is firstly presented and applied to realise broadband antennas with stable radiation patterns. Different from conventional square MTS structure, the proposed windmill‐shaped MTS structure is formed by etching several triangular sawtooth slots instead of rectangular slots on the radiating square patch. Through the characteristic mode analysis, it can be found that the proposed MTS structure exhibits a much stable broadside radiation within a large bandwidth of 45.9%. Besides, the tapered edges resulted from the sawtooth slots can be regarded as smoother transitions, which would bring a better impedance matching. Based on above analysis, two antennas with different aperture sizes using the proposed windmill‐shaped MTS are well designed accordingly. Both antennas demonstrate the capability to achieve large impedance bandwidths over 40% and stable radiations (2‐dB gain bandwidth >33%). Compared with the conventional MTS‐based antenna, these proposed antennas improve the bandwidth and radiation performances effectively, showing great promises for modern wireless communication systems.

A Compact Negative Group Delay Microstrip Diplexer with Low Losses for 5G Applications

Microstrip Diplexers play an important role in modern wireless communication systems. In this paper, a novel compact microstrip diplexer based on spiral cells is presented. The proposed resonator primarily consists of two spiral thin lines connected to a pair of coupled lines. This novel resonator is analyzed mathematically to find its behavior and tune the dimensions of the final layout easily. Using the analyzed resonator, two bandpass filters (BPFs) are designed. Then, a novel high-performance microstrip diplexer is obtained by designing and integrating these two BPFs. The center frequencies of the first and second channels of the proposed diplexer are 1.86 GHz and 4.62 GHz, respectively. The proposed diplexer boasts a remarkably small size of 0.004 λg2 and features flat channels with low insertion losses of only 0.048 dB and 0.065 dB for the first and second channels, respectively. The maximum group delays of S21 and S31 are 0.31 ns, 0.86 ns, respectively, which are good values for a modern communication system. Meanwhile, inside its passbands for some frequency ranges, its group delays are negative. Thus, using this diplexer can decrease the signal dispersion. The 1st and 2nd passbands are wide with 47.3% and 47.1% fractional bandwidths (FBW), respectively. Therefore, this diplexer can be easily and successfully used in designing high-performance RF communication systems.

Design of a Crossed Dielectric Resonator-Loaded, Dual-Band Dual-Polarized Differential Patch Antenna with Improved Port Isolation and Gain

To meet the urgent requirement for more channel capacity in modern wireless communication systems, antennas with more operation bands are demanded. However, large amounts of antennas suffer from low radiation gains and low port isolation levels. In view of this, a differentially fed, dual-wideband, dual-polarized patch antenna is proposed in this paper. Compared with conventional crossed-feeding structures, the proposed crossed dielectric resonator (CDR) can provide extra resonances with improved isolation levels and radiation gain. Further, four shorting pins are introduced to the radiating patch to help improve the impedance-matching performance. In addition, the proposed antenna also has a very compact size of 0.46λ × 0.46λ × 0.12λ. Finally, a prototype of the proposed antenna is fabricated to validate the design concept. The measured results show that the proposed antenna generates dual wide bands of 1.86–2.52 GHz and 3.26–3.72 GHz for |S11| < −10 dB. High radiation gains of 8.9 ± 0.9 dBi and 10.8 ± 1.2 dBi are also obtained, as well as high port isolation levels of better than 38.4 dB and 36.2 dB at the two bands. The excellent performance of the proposed antenna makes it a promising candidate for 4G/5G wireless communication systems.

Analysis and optimization for non-orthogonal pilot sequence sets in massive MIMO systems

In modern wireless communication systems, orthogonal pilot signals has been generally employed in estimation of the channel state information. However, orthogonal pilot signals are inadequate for supporting the rapidly increasing requirements of communication throughput for 5G-and-beyond wireless environments, owing to the pilot contamination and short coherence times in high-mobility situations. To address these concerns, we present a new strategy for making use of non-orthogonal pilot sequences in channel estimation for multi-cell massive multiple-input multiple-output systems. First, we extend prior pilot assignment strategies based on the orthogonality of pilots to the general case of non-orthogonal pilot signals. Based on the proposed non-orthogonal pilot assignment strategy, we establish the minimal pilot length that fulfills a requirement for the channel estimate error, under a given degree of non-orthogonality. Then, we demonstrate validity of the pilot assignment strategy with the minimal length, which maximizes the entire network throughput. Simulation results show that our proposed method gives a significantly enhanced performance in terms of the net throughput compared to that with orthogonal pilot sequences. The performance gain becomes particularly significant with a higher density of users or shorter coherence time intervals.

The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario

4X4 MIMO slot antenna spanner shaped low mutual coupling for Wi-Fi 6 and 5G communications

Multiple-input multiple-output (MIMO) antennas are essential for modern wireless communication systems that require high data rates, reliability, and quality. However, designing MIMO antennas poses several challenges, such as achieving high isolation, low mutual coupling, low envelope correlation coefficient (ECC), high gain, and high efficiency in a compact size. In this paper, we propose a novel MIMO antenna design that can overcome these challenges and support various wireless applications, such as WiFi-6, 5G (n77/n78/n79), and satellite communications. The proposed design consists of four spanner-shaped radiating elements that are etched on an FR-4 substrate with a tapered feeding line. The spanner-shaped antenna is derived from an octagonal ellipse that is cut by another octagonal ellipse to form the shape of the antenna. The proposed design achieves dual operating bands of 3.3–3.8 GHz and 4.5–8.4 GHz with low mutual coupling (<−10 dB), low ECC (less than0.05), high gain (up to 4 dBi), and high efficiency (>80%). The proposed design is compared with other MIMO antenna designs in the literature and shows its advantages in terms of size, performance, and applicability.

Interpretable AI-Based Large-Scale 3D Pathloss Prediction Model for Enabling Emerging Self-Driving Networks

In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization. The state-of-the-art empirical propagation models are based on measurements in specific environments and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time-consuming and expensive. We propose a Machine Learning (ML)-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others, even with sparse training data, by providing a 65% increase in prediction accuracy as compared to empirical models and 13x decrease in prediction time as compared to ray-tracing. To address the interpretability challenge that thwarts the adoption of most ML-based models, we perform extensive secondary analysis using SHapley Additive exPlanations (SHAP) method, yielding many practically useful insights that can be leveraged for intelligently tuning the network configuration, selective enrichment of training data in real networks and for building lighter ML-based propagation model to enable low-latency use-cases.

Artificial Intelligence Techniques for Mobile Station Location Estimation

Modern wireless communication systems require positioning functions, which provide are automatic location estimation of stations within a network. However, when new networks are implemented, much higher accuracy is required when determining geographical coordinates of a mobile station to develop of services related to the station location. To solve the problem of mobile station positioning, its geographical coordinates are calculated, coordinates of the closest base stations being known. The paper proposes to use a genetic neuro-fuzzy controller for improving the effectiveness of positioning a mobile station. Positioning methods providing usage of artificial intelligence methods are based on measurements of levels for signals from the closets access points or base stations, their coordinates are known. The proposed localization method is based on values of received signal strength indicator – RSSI. At the same time, the RSSI method has a disadvantage – low accuracy, which is proposed to be increased by applying methods of artificial intelligence – fuzzy logic, neural networks, genetic algorithms. Therefore, the objective of this paper is to elaborate an optimized method for determining location of a mobile station. In compliance with the suggested method, RSSI values and ToA values enter the genetic neuro-fuzzy controller, after corresponding processing, the distance from the mobile station to the base station appears at its output.