A Roadmap for NF-ISAC in 6G: A Comprehensive Overview and Tutorial.

Near-field (NF) integrated sensing and communication (ISAC) has the potential to revolutionize future wireless networks. It enables simultaneous communication and sensing operations on the same radio frequency (RF) resources using a shared hardware platform, maximizing resource utilization. NF-ISAC systems can improve communication and sensing performance compared to traditional far-field (FF) ISAC systems by exploiting the unique propagation characteristics of NF spherical waves with an additional distance dimension. Despite its potential, NF-ISAC research is still in its early stages, and a comprehensive survey of the technology is lacking. This paper systematically explores NF-ISAC technology, providing an in-depth analysis of both NF and FF systems, their applicability in various scenarios, and different channel models. It highlights the advantages and philosophies of ISAC, examining both narrow-band and wide-band NF-ISAC systems. Case studies and simulations offer deeper insights into NF-ISAC design philosophies. Additionally, the paper reviews the existing NF-ISAC literature, methodologies, potentials, and conclusions, and discusses future research areas, challenges, and applications.

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques.

Unmanned aerial vehicles (UAVs) have garnered attention for their potential to improve wireless communication networks by establishing line-of-sight (LoS) connections. However, urban environments pose challenges such as tall buildings and trees, impacting communication pathways. Intelligent reflection surfaces (IRSs) offer a solution by creating virtual LoS routes through signal reflection, enhancing reliability and coverage. This paper presents a three-dimensional dynamic channel model for UAV-assisted communication systems with IRSs. Additionally, it proposes a novel channel-tracking approach using deep learning and artificial intelligence techniques, comprising preliminary estimation with a deep neural network and continuous monitoring with a Stacked Bidirectional Long and Short-Term Memory (Bi-LSTM) model. Simulation results demonstrate faster convergence and superior performance compared to benchmarks, highlighting the effectiveness of integrating IRSs into UAV-enabled communication for enhanced reliability and efficiency.

Analysis and modeling of radio propagation fading characteristics in tunnel construction.

With the development of underground rail transit, the concept of intelligent tunnel construction has been proposed and promoted. High-quality networking during tunnel construction is a prerequisite for this, making it highly urgent to establish networking during tunnel construction. When studying intra-tunnel networking, it is necessary to consider the propagation characteristics of radio waves in the tunnel. In this paper, according to the actual needs of engineering in tunnel construction and the characteristics of tunnel scenes, an improved ray tracing method is proposed, which considers the type and installation position of antennas, transceiver frequency band and power in channel modeling, and proposes a field strength calculation method under different coordinate systems according to the characteristics of straight and curved segments during tunnel construction. In addition, the propagation characteristics of radio waves in dynamic tunnel construction scenarios are quantitatively analyzed. In this paper, by establishing antenna diagram, two-dimensional and three-dimensional models of tunnels, the computer simulation method is applied to compare with the improved algorithm, and the results have good consistency, in addition, the improved algorithm does not require a lot of modeling work in the early stage, and has high applicability and portability. Not only that, this paper also makes actual measurements of the subway under construction in Zhengzhou, China in different scenarios, and verifies the effectiveness of the method.

Customized Millimeter Wave Channel Model for Enhancement of Next-Generation UAV-Aided Internet of Things Networks.

The success of next-generation Internet of Things (IoT) applications could be boosted with state-of-the-art communication technologies, including the operation of millimeter-wave (mmWave) bands and the implementation of three-dimensional (3D) networks. With some access points (APs) mounted on unmanned aerial vehicles (UAVs), the probability of line-of-sight (LoS) connectivity to IoT nodes could be augmented to address the high path loss at mmWave bands. Nevertheless, system optimization is essential to maintaining reliable communication in 3D IoT networks, particularly in dense urban areas with elevated buildings. This research adopts the implementation of a geometry-based stochastic channel model. The model customizes the standard clustered delay line (CDL) channel profile based on the environmental geometry of the site to obtain realistic performance and optimize system design. Simulation validation is conducted based on the actual maps of highly dense urban areas to demonstrate that the proposed approach is comprehensive. The results reveal that the use of standard channel models in the analysis introduces errors in the channel quality indicator (CQI) that can exceed 50% due to the effect of the environmental geometry on the channel profile. The results also quantify accuracy improvements in the wireless channel and network performance in terms of the CQI and downlink (DL) throughput.

A Survey of Vehicular VLC Methodologies.

Visible Light Communication (VLC) has recently emerged as an alternative to RF-based wireless communications. VLC for vehicles has demonstrated its potential for Intelligent Transportation Systems (ITSs) to exchange information between vehicles and infrastructure to achieve ITS core goals, such as improving road safety, passenger comfort, and traffic flow. This paper seeks to provide a detailed survey of vehicular VLC systems. This paper presents an overview of current developments in vehicular VLC systems and their benefits and limitations for experienced researchers and newcomers.

Path loss modeling based on neural networks and ensemble method for future wireless networks.

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.

SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard.

Compared with railway communication service requirements on the mainline, requirements in hotspots such as stations and yards are more complicated in terms of service types as well as bandwidth, of which railway-dedicated mobile communication systems such as 5G-R facilitated with dedicated frequency support cannot meet the entire communication requirements. Therefore, other radio-communication technologies need to be adopted as a supplement, among which the mmWave communication system is a promising technology, especially for large bandwidth communication between train and trackside. However, there is a lack of evaluation of the 28 GHz mmWave channel characteristics for the railway marshaling yard scenario. In this paper, the railway marshaling yard mmWave propagation scenario is deeply analyzed and classified into three typical categories, based on which, a measurement campaign is conducted using an SDR channel sounding system equipped with a 28 GHz mmWave phased-array antenna. A self-developed software under the LabVIEW platform is used to derive the channel parameters. Conclusions on the relationship between the parameters of MPC numbers, time-spread, and received power and position, as well as the impact of typical obstructions such as the Catenary, adjacent locomotives, and buildings are drawn. The statistical results and conclusions of this paper are helpful for facilitating the design and performance evaluation of future mmWave communication systems for railway marshaling yards and can also be further extended and applied to the research of mmWave utilization in 6G and other future communication technologies for more scenarios.

A Survey of Path Loss Prediction and Channel Models for Unmanned Aerial Systems for System-Level Simulations.

Unmanned aerial systems (UAS) have recently gained popularity, and they are envisioned as an integral parts of the current and future wireless and mobile-radio networks. Despite the exhaustive research on air-to-ground channels, there are insufficient studies, experimental campaigns and general channel models related to air-to-space (A2S) and air-to-air (A2A) wireless links. This paper presents a comprehensive review of the available channel models and path-loss prediction for A2S and A2A communications. Specific case studies attempting to extend current models' parameters and provide important knowledge of the channel behavior in combination with UAV flight characteristics are also provided. A time-series rain-attenuation synthesizer is also presented that describes quite accurately the impact of the troposphere at frequencies above 10 GHz. This specific model can be also applied to both A2S and A2A wireless links. Finally, scientific challenges and gaps that can be used for future research on the upcoming 6G networks are highlighted.

Intelligent Drone Positioning via BIC Optimization for Maximizing LPWAN Coverage and Capacity in Suburban Amazon Environments.

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.

Channel Modeling and Quantization Design for 3D NAND Flash Memory.

As the technology scales down, two-dimensional (2D) NAND flash memory has reached its bottleneck. Three-dimensional (3D) NAND flash memory was proposed to further increase the storage capacity by vertically stacking multiple layers. However, the new architecture of 3D flash memory leads to new sources of errors, which severely affects the reliability of the system. In this paper, for the first time, we derive the channel probability density function of 3D NAND flash memory by taking major sources of errors. Based on the derived channel probability density function, the mutual information (MI) for 3D flash memory with multiple layers is derived and used as a metric to design the quantization. Specifically, we propose a dynamic programming algorithm to jointly optimize read-voltage thresholds for all layers by maximizing the MI (MMI). To further reduce the complexity, we develop an MI derivative (MID)-based method to obtain read-voltage thresholds for hard-decision decoding (HDD) of error correction codes (ECCs). Simulation results show that the performance with jointly optimized read-voltage thresholds can closely approach that with read-voltage thresholds optimized for each layer, with much less read latency. Moreover, the MID-based MMI quantizer almost achieves the optimal performance for HDD of ECCs.

Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers.

Satellite-based link analysis is valuable for efficient and secure quantum communication, despite seasonal limits and restrictions on transmission times. A semi-empirical quantum key distribution model for satellite-based systems was proposed that simplifies simulations of communication links. Unlike other theoretical models, our approach was based on the experimentally-determined atmospheric extinction coefficient typical for mid-latitude ground stations. The parameter was measured for both clear and foggy conditions, and it was validated using published experimental data from the Micius satellite. Using this model, we simulated secure QKD between the Micius satellite and ground stations with 300 mm and 600 mm aperture telescopes.

MmWave Physical Layer Network Modeling and Planning for Fixed Wireless Access Applications.

The large bandwidths that are available at millimeter-wave frequencies enable fixed wireless access (FWA) applications, in which fixed point-to-point wireless links are used to provide internet connectivity. In FWA networks, a wireless mesh is created and data are routed from the customer premises equipment (CPE) towards the point of presence (POP), which is the interface with the wired internet infrastructure. The performance of the wireless links depends on the radio propagation characteristics, as well as the wireless technology that is used. The radio propagation characteristics depend on the environment and on the considered frequency. In this work, we analyzed the network characteristics of FWA networks using radio propagation models for different wireless technologies using millimeter-wave (mmWave) frequencies of 28 GHz, 60 GHz, and 140 GHz. Different scenarios and environments were considered, and the influence of rain, vegetation, and the number of subscribers was investigated. A network planning algorithm is presented that defines a route for each CPE towards the POP based on a predefined location of customer devices and considering the available capacity of the wireless links. Rain does not have a considerable effect on the system capacity. Even though the higher frequencies exhibit a larger path loss, resulting in a lower power of the received signal, the larger bandwidths enable a higher channel capacity.

Body-Centric Terahertz Networks: Prospects and Challenges.

Following recent advancements in Terahertz (THz) technology, THz communications are currently being celebrated as key enablers for various applications in future generations of communication networks. While typical communication use cases are over medium-range air interfaces, the inherently small beamwidths and transceiver footprints at THz frequencies support nano-communication paradigms. In particular, the use of the THz band for in-body and on-body communications has been gaining attention recently. By exploiting the accurate THz sensing and imaging capabilities, body-centric THz biomedical applications can transcend the limitations of molecular, acoustic, and radio-frequency solutions. In this paper, we study the use of the THz band for body-centric networks, by surveying works on THz device technologies, channel and noise modeling, modulation schemes, and networking topologies. We also promote THz sensing and imaging applications in the healthcare sector, especially for detecting zootonic viruses such as Coronavirus. We present several open research problems for body-centric THz networks.

Deep-Learning-Based Adaptive Symbol Decision for Visual MIMO System with Variable Channel Modeling.

A channel modeling method and deep-learning-based symbol decision method are proposed to improve the performance of a visual MIMO system for communication between a variable-color LED array and camera. Although image processing algorithms using color clustering are available to correct distorted color information in a channel, color-similarity-based approaches are limited by real-world distortions; to overcome such limitations, symbol decision is defined as a multiclass classification problem. Further, to learn a robust classifier against channel distortion, a deep neural network learning technique is applied to adaptively determine symbols from channel distortion. The network designed herein comprises the channel identification and symbol decision modules; the channel identification module extracts a channel identification vector for symbol determination from an input image using a two-dimensional deep convolutional neural network (CNN); the symbol decision module then generates a feature map by combining the channel identification vector and information on adjacent symbols to determine the symbol via learning correlations between adjacent symbols using a one-dimensional CNN. The two modules are connected together and learned simultaneously in an end-to-end manner. We also propose a new channel modeling method that intuitively reflects real-world distortion factors rather than the conventional additive white Gaussian noise channel to efficiently train deep-learning networks. Lastly, in the proposed channel distortion environment, the proposed method shows performance improvement by an average of about 41.8% (up to about 54.8%) compared to the existing Euclidean distance method, and about 6.3% (up to about 9.2%) on average compared to the SVM method.

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach.

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels.

Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.

An Enhanced VLC Channel Model for Underground Mining Environments Considering a 3D Dust Particle Distribution Model.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization.

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.

Influence of Source Parameters on the Polarization Properties of Beams for Practical Free-Space Quantum Key Distribution.

Polarization encoding has been extensively used in quantum key distribution (QKD) implementations along free-space links. However, the calculation model to characterize channel transmittance and quantum bit error rate (QBER) for free-space QKD has not been systematically studied. As a result, it is often assumed that misalignment error is equal to a fixed value, which is not theoretically rigorous. In this paper, we investigate the depolarization and rotation of the signal beams resulting from spatially-dependent polarization effects of the use of curved optics in an off-axis configuration, where decoherence can be characterized by the Huygens-Fresnel principle and the cross-spectral density matrix (CSDM). The transmittance and misalignment error in a practical free-space QKD can thus be estimated using the method. Furthermore, the numerical simulations clearly show that the polarization effect caused by turbulence can be effectively mitigated when maintaining good beam coherence properties.

An Optimized Approach to Channel Modeling and Impact of Deteriorating Factors on Wireless Communication in Underground Mines.

The environment of underground coal mines has challenging properties that makes this zone inadaptable for a stable communication system. Additionally, various deteriorating physical parameters strongly affect the performance of wireless networks, which leads to limited network coverage and poor quality of data communication. This study investigates the communication capability in underground coal mines by optimizing the wireless link to develop a stable network for an underground hazardous environment. A hybrid channel-modeling scheme is proposed to characterize the environment of underground mines for wireless communication by classifying the area of a mine into the main gallery and sub-galleries. The complex segments of mine are evaluated by categorizing the wireless links for the line-of-sight (LOS) zones and hybrid modeling is employed to examine the characteristics of electromagnetic signal propagation. For hybrid channel modeling, the multimode waveguide model and geometrical optic (GO) model are used for developing an optimal framework that improves the accessibility of the network in the critical time-varying environment of mines. Moreover, the influence of various deteriorating factors is analyzed using 2.4 GHz to 5 GHz frequency band to study its relationship with the vital constraints of an underground mine. The critical factors such as path loss, roughness loss, delay spread, and shadow fading are examined under detailed analysis with variation in link structure for the mine.