Wireless Communication Technology Research Articles

6G is The Future of Connectivity

6G is the next generation of mobile communication technology, promising advancements in speed, latency, communication, and applications. Promises to be a major leap forward in wireless communication technology compared to 5G. AI will be utilized in 6G networks to manage networks intelligently, predict maintenance needs, and optimize resource allocation.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

Optimization of UWB indoor positioning based on hardware accelerated Fuzzy ISODATA

With the development of wireless communication technology, Ultra-Wideband (UWB) has become an important solution for indoor positioning. In complex indoor environments, the influence of non-line-of-sight (NLOS) factors leads to increased positioning errors. To improve the positioning accuracy, fuzzy iterative self-organizing data analysis clustering algorithm (ISODATA) is introduced to process a large amount of UWB data to reduce the influence of NLOS factors, and to stabilize positioning error within 2 cm, enhances the accuracy of the positioning system. To further improve the running efficiency of the algorithm, FPGA is used to accelerate the key computational part of the algorithm, compared with running on the MATLAB platform, which improves the speed about 100 times, enhances the algorithm’s computational speed dramatically.

Energy efficiency optimization for 6G multi-IRS multi-cell NOMA vehicle-to-infrastructure communication networks

Intelligent Reflecting Surfaces (IRS), software-controlled metasurfaces, have emerged as an upcoming sixth-generation (6G) wireless communication technology. IRS intelligently manipulates and optimizes signal propagation using a large-scale array of intelligent elements, enhancing signal coverage, increasing capacity, mitigating path loss, and combating multipath fading This work provides a new energy-efficiency model for multi-IRS-assisted multi-cell non-orthogonal multiple access (NOMA) vehicular to infrastructure communication networks. The objective is the joint optimization of the total power budget at the roadside unit (RSU), NOMA power allocation for the user equipment, and designing phase shifts for IRS in each cell to maximize the achievable energy efficiency of the system. Due to non-convexity, the original non-convex problem is first decoupled and transformed using block coordinate descent and successive convex approximation methods. Then, an efficient solution is achieved using Gradient-based and interior-point methods. We also consider two benchmark schemes: (1) NOMA power optimization at RSU with random phase shift design at IRS and (2) orthogonal multiple access power allocation with optimal phase shift design at IRS. Numerical results show the superiority of the proposed solution compared to the benchmark schemes. The proposed solution outperforms the benchmarks, demonstrating a 59.57% and 151.21% improvement over the NOMA and orthogonal schemes, respectively, at pct=2 dBm. Additionally, it shows up to a 10.43% better performance than OMA at 10 IRS elements.

AoI-aware transmission control in real-time mmwave energy harvesting systems: a risk-sensitive reinforcement learning approach

The evolution of enabling technologies in wireless communications has paved the way for supporting novel applications with more demanding QoS requirements, but at the cost of increasing the complexity of optimizing the digital communication chain. In particular, Millimeter Wave (mmWave) communications provide an abundance of bandwidth, and energy harvesting supplies the network with a continual source of energy to facilitate self-sustainability; however, harnessing these technologies is challenging due to the stochastic dynamics of the mmWave channel as well as the random sporadic nature of the harvested energy. In this paper, we aim at the dynamic optimization of update transmissions in mmWave energy harvesting systems in terms of Age of Information (AoI). AoI has recently been introduced to quantify information freshness and is a more stringent QoS metric compared to conventional delay and throughput. However, most prior art has only addressed average-based AoI metrics, which can be insufficient to capture the occurrence of rare but high-impact freshness violation events in time-critical scenarios. We formulate a control problem that aims to minimize the long-term entropic risk measure of AoI samples by configuring the “sense & transmit” of updates. Due to the high complexity of the exponential cost function, we reformulate the problem with an approximated mean-variance risk measure as the new objective. Under unknown system statistics, we propose a two-timescale model-free risk-sensitive reinforcement learning algorithm to compute a control policy that adapts to the trio of channel, energy, and AoI states. We evaluate the efficiency of the proposed scheme through extensive simulations.

Effective capacity analysis of configurable intelligent surface-assisted NOMA communications systems

This paper investigates the integration of configurable intelligent surfaces (CIS) into relay radio networks, focusing on communication system enhancement. Towards this end, we propose CIS-assisted non-orthogonal multiple access (NOMA) communication systems to improve direct connections between a base station and two destination nodes. Our primary objective is to assess the net-work’s overall capacity, considering critical factors like signal-to-noise ratio, the number and placement of CIS components, quality of service exponent, and power distribution coefficients. Analytical equations developed in this research closely align with simulation results, validating our theoretical analysis. This study underscores the growing significance of CISs in modern communication systems, introducing adaptability and optimization to wireless networks. By exploring CIS-assisted NOMA communication systems, we contribute to dis-cussions about the evolving landscape of wireless communication technologies, poised to revolutionize information transmission and reception in the digital age.

Energy saving performance analysis for future fifth generation millimetre-wave cellular networks

The deployment of fifth generation (5G) millimetre-wave (mmWave) base stations (BSs) will consume more energy over time due to the limited time available, despite the increasing interest in developing 5G mmWave wireless communication technology. Constructing 5G mmWave cellular network infrastructure can improve energy efficiency, which is a challenge to implement in heterogeneous networks. This paper presents analytical frameworks for monitoring the effectiveness of 5G mmWave cellular networks. Based on the state management of BS, a system model for 2-tier heterogeneous networks is developed, and particle swarm optimization (PSO) is then used to compute the total energy consumption of the heterogeneous networks. Energy consumption was compared and analysed by leveraging state switching and the aggregate delay for three methods: fundamental separation, conventional separation, and a proposed energy-saving method that introduced a sleep state. Simulation shows that the proposed energy-saving method, which is a combination of conventional separation approaches, has the lowest total energy consumption and offers a 9% reduction compared to other related works. The results validate the accuracy of the power usage used in the 5G mmWave cellular network of the proposed method.

Clarity in Wireless Quantum Communication: Exploring Explainable AI for Signal Modulation Classification

The advent of artificial intelligence (AI) and edge computing technologies has ushered in a new era for automatic modulation classification (AMC), with a particular focus on deep learning (DL)-based approaches. These AI-based AMC systems, deployed at edge devices, have shown remarkable advancements in recognizing and classifying a wide range of wireless signals. However, a significant challenge in this domain lies in the lack of adequate explanations for the models employed, which, in turn, hampers model accuracy and training efficiency. Consequently, the practical applications and further enhancements of these models are constrained. To address this limitation, researchers have started to explore interpretable methods for the technical analysis of DL-based AMC systems. In this paper, we delve into the subject, leveraging recent developments in interpretable methods, to provide a comprehensive review of applicable techniques and to outline the existing research challenges in the field of interpretable AMC. Moreover, we introduce a novel interpretable AI-based AMC framework designed to augment the interpretability of AMC outcomes. This framework achieves this by elucidating the contribution of wireless signal features to the training process of DL networks. In addition, we explore the potential impact of quantum computing on AMC systems. Quantum computing, with its ability to process complex computations at unprecedented speeds, offers a promising avenue for enhancing the efficiency and accuracy of AMC. By integrating quantum algorithms with AI-based AMC frameworks, it is possible to significantly improve the performance of wireless signal classification, especially in environments with high signal variability and noise. Our experimental results demonstrate that the proposed approach possesses the capacity to unravel the classification mechanisms hidden within opaque auto-modulated classification networks. Furthermore, it exhibits the potential to facilitate the training of networks on edge devices with reduced energy consumption and enhanced classification accuracy, thereby addressing critical challenges in the field of wireless signal classification. Additionally, the integration of quantum computing techniques holds the promise of further advancing the capabilities of AMC systems, paving the way for more robust and efficient wireless communication technologies.

MobileAmcT: A Lightweight Mobile Automatic Modulation Classification Transformer in Drone Communication Systems

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Balancing Technological Innovation and Environmental Sustainability: A Lifecycle Analysis of 6G Wireless Communication Technology

Wireless communication has revolutionized the evolution of humankind. The rapid growth and development of mobile communication has created an ecosystem better than what has been before. However, issues such as ample energy consumption and resulting carbon emissions, a lack of proper disposal mechanisms for large amounts of electronic waste, and the recycling of electronic materials interrupt growth. When the world is waiting for the implementation of 6G mobile communication technology, it is mandatory to resolve these issues for the sustainability of 6G technology. In this review, we present the superiority of 6G over previous generations accompanied by issues that cause extensive damage to the environment. To mitigate this adverse effect, we present a lifecycle analysis of 6G wireless communication technology from production to disposal, focusing on issues surrounding electronic waste, energy consumption, and environmental impact. This study explains the intricacies of electronic parts, toxic compounds, and the dangers of incorrect disposal techniques. It also investigates energy consumption issues specific to 6G technology, such as manufacturing processes and network infrastructures that require considerable energy. We also present a quantitative evaluation of the 6G lifecycle in detail. In addition, we present a comprehensive strategy and insights to make 6G sustainable. Furthermore, we suggest an ecological policy for all stakeholders for the sustainability of 6G. We also present political and commercial implications for 6G. As the process of 6G development continues, we show the impact of network fragmentation on standardization, which helps improve sustainability. Finally, we conclude that while the existing research has made significant advances in 6G, there is a need for correct disposal techniques to refine the key government policies for managing e-waste. New cooling technologies and renewable energy sources must be adopted to reduce the current greenhouse emission of 200 g of CO2 and energy consumption of 2.5 kWh per GB for 6G networks.

Enhanced Algorithm for Reduction of Blackhole’s Re-Judgment Delay for Realtime Traffic in Mobile Adhoc Network

Mobile Ad-hoc Network (MANET) is a prominent technology in wireless communication in which mobile nodes operate in a distributed manner without having central control devices; This lack of control over network devices and over changing topology aspects of the network allows malicious nodes gain opportunities to join the network. The blackhole attack is one of the security risks that increases the network overhead. In this attack, the malicious node advertises itself as having the shortest path to the destination and falsely replies to the route requests, and drops all receiving packets. Therefore, the paper proposes a trust-level based re-judgment algorithm. Experiments were set up in the NS3 networking simulation tool. Simulations results showed that the proposed trust-level based re-judgment algorithm improves network throughput and packet delivery rate by 82.8% and 146.6 Kbps respectively. Also, reduces the end-to-end delay by an average of 24.17 milliseconds

Electrophysical Characteristics of Acrylonitrile Butadiene Styrene Composites Filled with Magnetite and Carbon Fiber Fillers.

With the rapid development of wireless communication technologies and the miniaturization trend in the electronics industry, the reduction of electromagnetic interference has become an important issue. To solve this problem, a lot of attention has been focused on polymer composites with combined functional fillers. In this paper, we report a method for creating an acrylonitrile butadiene styrene (ABS) plastic composite with a low amount of conductive carbon and magnetic fillers preparation. Also, we investigate the mechanical, thermophysical, and electrodynamic characteristics of the resulting composites. Increasing the combined filler amount in the ABS composite from 1 to 5 wt % leads to a composite conductivity growth of almost 50 times. It is necessary to underline the temperature decrease of 5 wt % mass loss and, accordingly, the composite heat resistance reduction with an increase in the combined filler from 1 to 5 wt %, while the thermal conductivity remains almost constant. It was established that electrodynamic and physical-mechanical characteristics depend on the agglomeration of fillers. This work is expected to reveal the potential of combining commercially available fillers to construct effective materials with good electromagnetic interference (EMI) protection using mass production methods (extrusion and injection molding).

Wearable Sensors Based on Miniaturized High-Performance Hybrid Nanogenerator for Medical Health Monitoring.

Wearable sensors are important components, converting mechanical vibration energy into electrical signals or other forms of output, which are widely used in healthcare, disaster warning, and transportation. However, the reliance on batteries limits the portability of wearable sensors and hinders their application in the field of Internet of Things. To solve this problem, we designed a miniaturized high-performance hybrid nanogenerator (MHP-HNG), which combined the functions of triboelectric sensing and electromagnetic power generation as well as the advantages of miniaturization. By optimizing the design of TENG and EMG, the wearable sensor achieved a voltage output of 14.14 V and a power output of 49 mW. Based on the wireless optical communication and wireless communication technologies, the wearable sensor achieved the integration of sensing, communication, and self-powered function, which is expected to realize health monitoring, emergency warning, and rehabilitation assistance, and further extend the potential application value in the medical field.

Flash sintering of tunable dielectric Ba0.6Sr0.4TiO3 electroceramics

Ba(1-x)SrxTiO3 (BST) is a crucial dielectric material in tunable devices for modern wireless communication technologies, owing to high dielectric tunability and low dielectric loss. However, the conventional processing of BST ceramics requires a high sintering temperature of about 1350 ºC. In this work, we demonstrate the feasibility of using Flash sintering (FS) and corresponding maps to process Ba0.6Sr0.4TiO3 ceramics, resulting in a 350 ºC decrease in sintering temperature and 7 h reduction in sintering process time. The Flash-sintered BST exhibits a finer grain microstructure, lower dielectric loss, and significantly higher K-factor (figure of merit for tunability) when compared to conventionally sintered BST, unequivocally meeting the application criteria for tunable materials. Our findings highlight the effectiveness of FS as an alternative method for the rapid sintering of BST, while also opening up possibilities for further research into more efficient sintering processes for electroceramics.

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Porous-multilayered Ti3C2Tx MXene hybrid carbon foams for tunable and efficient electromagnetic wave absorption

The rapid development of modern wireless communication technology makes electromagnetic wave pollution increasingly serious, necessitating the development of high-performance electromagnetic wave-absorbing materials. This study introduces a Ti3C2Tx MXene hybrid carbon foam (HCF) that exhibits a tunable and efficient electromagnetic wave absorption performance. The HCF features a porous, multilayered structure, which enhances its multi-reflection properties, and a heterogeneous interface between the carbon foam and MXene boosting its dielectric loss. By regulating the content of MXene, the ratio of polarization loss to conduction loss can be regulated to achieve better impedance matching. As a result, the HCF demonstrated superb electromagnetic wave absorption performance with RLmin = −72.25 dB and EABmax = 6.55 GHz. Furthermore, the minimum absorption frequency domain could be tuned within 8–14 GHz by varying the MXene content. The performance in radar wave absorption and electromagnetic-thermal conversion (ramp rate 85.81 °C/s from RT to 200 °C) proves that HCF is an efficient and reliable electromagnetic wave absorber, suitable for radar technology, microwave heating, and communication systems applications.

Supporting Immersive Video Streaming via V2X Communication

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method’s effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.

Inner External DQN LoRa SF Allocation Scheme for Complex Environments

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

Frequency reconfigurable microstrip patch antenna for multiband applications

Wireless communication technology is well-established, and several antennas have been developed and produced specifically for this purpose. However, antenna performance and communication system development need to be enhanced in order to adapt to the present era. The performance of the antenna is significantly influenced by its design. Thus, this work produced a novel wideband antenna design via the use of a frequency reconfigurable approach. In the recommended study, microstrip patch antennas (MPAs) were used in wideband applications to switch frequencies using shunt-series microelectromechanical systems (MEMS). The suggested antenna, which has two switches built into it, is tested in ON-ON, OFF-ON, and OFF-OFF switching scenarios. Radiation pattern, voltage standing wave ratio (VSWR), gain, bandwidth, and return loss are among the antenna performance metrics used to assess the suggested antenna's performance in each switching situation. The simulation findings suggest that the optimal antenna design for usage in wireless communication systems is one that works well with a shunt-series MEMS switch.