Search Results (106)

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region. Particularly, this paper considers the resolution constraint in the formulated optimization problem, which is designed to guarantee that interference between users can be neglected. A low-complexity optimization algorithm is proposed to realize the joint optimization of power and antenna number. Specifically, the near-field resolution constraint is first simplified to a polynomial inequality using the Fresnel approximation. Then the fractional objective of maximizing energy efficiency is transformed into a convex optimization subproblem via the Dinkelbach algorithm, and the power allocation is solved for a fixed number of antennas. Finally, the number of antennas is integrally optimized with monotonicity analysis. The simulation results show that the proposed method can significantly improve the system energy efficiency and reduce the antenna overhead under different resolution thresholds, user angles, and distance configurations, which provides a practical reference for the design of green and low-carbon near-field communication systems. Full article

With the advancement of sixth-generation (6G) wireless communication technology, new demands have been placed on maritime communications. In maritime environments, factors such as evaporation ducts and sea waves significantly impact signal transmission. Moreover, in multi-user communication scenarios, interactions between different users introduce additional complexities. This paper proposes a novel channel model for maritime unmanned aerial vehicle (UAV) to multi-unmanned surface vehicle (USV) communications, which incorporates massive multiple-input–multiple-output (MIMO) antennas at both the transmitter (Tx) and receiver (Rx), while also accounting for the effects of evaporation ducts and sea waves on the channel. For the USV-single-user maritime model, the temporal auto-correlation function (ACF) and spatial cross-correlation function (CCF) are analyzed. For the UAV-to-multi-user channel model, key channel characteristics such as channel matrix collinearity (CMC) and channel capacity are examined. Finally, the accuracy and effectiveness of the proposed model are validated through a comparison between the measured and simulated data under a single-link environment. Meanwhile, a comparison between the CMC obtained from the proposed model and that derived from Ray-Tracing further verifies the model’s accuracy in multi-link environments. This model provides essential theoretical guidance for future 6G maritime communication systems. Full article

Terahertz (THz) antennas are among the critical components required for enabling the transition to sixth-generation (6G) wireless networks. Although research on THz antennas for 6G communication systems has garnered significant attention, a standardized antenna design has yet to be established. This study introduces the modeling of a full-metal transverse slotted waveguide antenna (TSWA) for 6G and beyond. The proposed antenna operates across the upper regions of the V-band and the entire W-band. Designed and simulated using widely adopted full-wave analysis tools, the antenna achieves a peak gain of 17 dBi and a total efficiency exceeding 90% within the band. Additionally, it exhibits pattern-reconfigurable capabilities, enabling main lobe beam steering between 5° and 68° with low side lobe levels. Simulations are conducted to assess the power handling capability (PHC) of the antenna, including both the peak (PPHC) and average (APHC) values. The results indicate that the antenna can handle 17 W of APHC within the W-band and 3.4 W across the 60–160 GHz range. Furthermore, corona discharge and multipaction analyses are performed to evaluate the antenna’s power handling performance under extreme operating conditions. These features make the proposed TSWA a strong candidate for high-performance space applications, 6G communication systems, and beyond. Full article

The commercial deployment of fifth generation (5G) mobile communication networks has begun, bringing with it novel offerings, improved user activities, and a variety of opportunities for different types of organizations. However, there still exist several challenges to implementing 5G technology. Sixth generation (6G) wireless communication technology development has begun on a worldwide scale in response to these challenges. Even though there have been many discussions on this topic in the past, many questions remain unanswered in the present literature. The article provides a comprehensive overview of 6G, including the common understanding of the concept, as well as its technical requirements and potential applications. A comprehensive analysis of the 6G network design, potential uses, and key elements are covered. This research article delineates future study topics and unresolved challenges to stimulate an ongoing global discourse. This analysis and content of this study supports the use of different applications and services that will benefit the community in the near future using the 6G technology. Subsequently, recommendations for each problem are provided, offering solutions to unresolved difficulties where functionalities are anticipated to improve, hence enhancing the overall user experience. Full article

Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions. Full article

The global deployment of 5G wireless networks has introduced significant advancements in data rates, latency, and energy efficiency. However, the rising demand for immersive applications (e.g., virtual and augmented reality) necessitates even higher data rates and lower latency, driving research toward sixth-generation (6G) wireless networks. This study addresses a major challenge in post-5G communication: mitigating signal blockage in high-frequency millimeter-wave (mmWave) bands. This paper proposes a novel framework for blockage prediction using AI-based classification techniques to enhance signal reliability and optimize connectivity. The proposed framework is evaluated comprehensively using performance metrics such as accuracy, precision, recall, and F1-score. Notably, the NN Model 4 achieves a classification accuracy of 99.8%. Comprehensive visualizations—such as learning curves, confusion matrices, ROC curves, and precision-recall plots—highlight the model’s performance. This study contributes to the development of AI-driven techniques that enhance reliability and efficiency in future wireless communication systems. Full article

With the growing demand for wireless communication, the sixth-generation (6G) wireless network will be more complex. The digital twin channel (DTC) is envisioned as a promising enabler for 6G, as it can create an online replica of the physical channel characteristics in the digital world, thereby supporting precise and adaptive communication decisions for 6G. In this article, we systematically review and summarize the existing efforts in realizing the DTC, providing a comprehensive analysis of ray tracing (RT), artificial intelligence (AI), and large model approaches. Based on this analysis, we further explore the potential of integrating large models with RT methods. By leveraging the strong generalization, multi-task processing capabilities, and multi-modal fusion capabilities of large models while incorporating physical priors from RT as expert knowledge to guide their training, there is a strong possibility of fulfilling the fast online inference and precise mapping requirements of the DTC. Therefore, we propose a novel DeepRT-enabled DTC (DRT-DTC) framework, which combines physical laws with large models like DeepSeek, offering a new vision for realizing the DTC. Two case studies are presented to demonstrate the possibility of this approach, which validate the effectiveness of physical law-based AI methods and large models in generating the DTC. Full article

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost effectiveness, and ease of deployment. The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and sixth-generation mobile communication technology (6G) and Mobile Edge Computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices, and changed the image sensing and understanding to novel modes (such as machine-to-machine or human-to-machine interactions). However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. To address these issues, this paper proposes a Scalable Semantic Adaptive Communication (SSAC) for task requirement. Firstly, we design an Attention Mechanism-based Joint Source Channel Coding (AMJSCC) in order to fully exploit the correlation among semantic features, channel conditions, and tasks. Then, a Prediction Scalable Semantic Generator (PSSG) is constructed to implement scalable semantics, allowing for flexible adjustments to achieve channel adaptation. The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system. Full article

Maritime communication networks are critical for supporting the increasing demands of oceanic and coastal activities, including shipping, fishing, and offshore operations. However, traditional systems face significant challenges in providing reliable, high-throughput connectivity due to dynamic sea environments, mobility, and non-line-of-sight (NLoS) conditions. Reconfigurable intelligent surfaces (RISs) have been proposed as a promising solution to overcome these limitations by enabling programmable control of electromagnetic wave propagation in next-generation mobile communication networks, such as beyond fifth generation and sixth generation ones (B5G/6G). This paper presents a deep learning-based (DL) scheme for beam selection in RIS-aided maritime next-generation networks. The proposed approach leverages deep learning to optimize beam selection dynamically, enhancing signal quality, coverage, and network efficiency in complex maritime environments. By integrating RIS configurations with data-driven insights, the proposed framework adapts to changing channel conditions and potential vessel mobility while minimizing latency and computational overhead. Simulation results demonstrate significant improvements in both machine learning (ML) metrics, such as beam selection accuracy, and overall communication reliability compared to traditional methods. More specifically, the proposed scheme reaches around 99% Top-$K$ Accuracy levels while jointly improving energy efficiency (ee) and spectral efficiency (SE) by approx. 2 times compared to state-of-the-art approaches. This study provides a robust foundation for employing DL in RIS-aided maritime networks, contributing to the advancement of intelligent, high-performance wireless communication systems for advanced maritime applications, such as autonomous mooring, the autonomous approach, and just-in-time arrival (JIT). Full article

Ambient backscatter communication (AmBC) has been regarded as an energy- and spectrum-efficient backscatter scheme for the massive Internet of Things (IoT). However, most existing AmBC systems are non-adaptive end-to-end systems, which cannot fully accommodate the forthcoming massive communications of the sixth-generation (6G) wireless communication systems. Adaptive backscatter communication has emerged as a research hotspot in AmBC in recent years. In this paper, we propose a novel adaptive backscatter technique on passive backscatter devices (BDs) in massive IoT scenarios. We first design a low-power adaptive strategy for the AmBC system where the backscatter receiver (BR) assigns a decision threshold to the passive BDs for the local adaptive backscatter mode chosen. Then, we propose the decision threshold design method by solving a joint sum rate maximization problem where the reflection coefficients (RCs) and transmit time allocation (TA) of different backscatter modes are also jointly optimized. Finally, simulations are provided to verify the efficiency of the proposed adaptive backscatter technique in terms of sum rate and outage probability performances. The results show that our proposed adaptive multi-source AmBC system can achieve a 34.8% average sum rate performance improvement compared with traditional AmBC systems under a common setup, and it performs better than other existing adaptive backscatter systems. Moreover, the numeric results confirm the accuracy and tightness of our derivation of outage probabilities. Full article

Sixth-generation (6G) wireless networks have the potential to transform global connectivity by supporting ultra-high data rates, ultra-reliable low latency communication (uRLLC), and intelligent, adaptive networking. To realize this vision, 6G must incorporate groundbreaking technologies that enhance network efficiency, spectral utilization, and dynamic adaptability. Among them, unmanned aerial vehicles (UAVs), terahertz (THz) communication, and intelligent reconfigurable surfaces (IRSs) are three major enablers in redefining the architecture and performance of next-generation wireless systems. This survey provides a comprehensive review of these transformative technologies, exploring their potential, design challenges, and integration into future 6G ecosystems. UAV-based communication provides flexible, on-demand communication in remote, harsh areas and is a vital solution for disasters, self-driving, and industrial automation. THz communication taking place in the 0.1–10 THz band reveals ultra-high bandwidth capable of a data rate of multi-gigabits per second and can avoid spectrum bottlenecks in conventional bands. IRS technology based on programmable metasurface allows real-time wavefront control, maximizing signal propagation and spectral/energy efficiency in complex settings. The work provides architectural evolution, active current research trends, and practical issues in applying these technologies, including their potential contribution to the creation of intelligent, ultra-connected 6G networks. In addition, it presents open research questions, possible answers, and future directions and provides information for academia, industry, and policymakers. Full article

The new generation of wireless communication technologies is already in development. Sixth Generation (6G) mobile communications are designed to push the limits for more bandwidth, more connected devices with minimal power requirements, and better signal quality. Previous technologies used in Fifth Generation (5G) are inadequate to handle the new requirements alone. One of the proposed solutions is the use of Reconfigurable Intelligent Surfaces (RISs). These surfaces, when combined with Artificial Intelligence (AI), may be a very powerful means of achieving this. In this paper, we review studies that focus on the use of RISs controlled by AI in determining the concept of Smart Radio Environment (SRE) for use in 6G wireless networks. We examine applications that span from Supervised to Federated Learning (FL) as enabled by the rise in Edge Computing. As the new generation of mobile devices is expected to have enhanced capabilities to perform computing and AI locally, thus reducing the need to transfer the data to a central hub, more opportunities are created for the extensive use of FL. In this context, we focus on research in FL as used in RIS-aided SRE. Full article