Channel Model Research Articles

6G optical-RF wireless integration: a review on heterogeneous cellular network channel modeling, measurements, and challenges

6G optical-RF wireless integration: a review on heterogeneous cellular network channel modeling, measurements, and challenges

Carrier frequency synchronization for WLAN systems based on MIMO-OFDM-IM

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) MIMO-OFDM-IM is a modulation technique that has garnered significant interest in recent times owing to its attraction of smooth transition to greener communications, enhancing efficiency in terms of energy, spectrum and compatibility to previous existing standards without the need for drastic changes in the physical layer. It is also proved that even though OFDM-IM provides better immunity to carrier frequency offset compared to traditional OFDM, the sensitivity to frequency error is still a major issue and it has to be resolved on priority basis to fully exploit the potential offered by the IM technique. In this work, we propose a novel non-data-aided algorithm that efficiently estimates and eliminates the offset present in the received signal. The algorithm has been analyzed for the MIMO-OFDM-IMbased wireless local area network system using the high throughput task group (TGn) MIMO channel models in the presence of AWGN. In addition, it is compared with the other popular blind algorithms based on both pilots and inactive data tones. The simulation plots depict a clear improvement of the new estimator over the existing methods not only in the low signal-to-noise ratio (SNR) regions but also in the high SNR region.

Blockchain-enabled downward traceability to combat gray market

Cross-market brand owners have long been troubled by the gray market issues. This study examines a dual distribution channel model involving one brand owner, two retailers, and two independent markets. In this model, low-end retailers privately transfer products from the low-end to high-end markets, creating a gray market. The brand addresses this behavior by adopting blockchain. We examine the impact of blockchain on gray market invasion, firm decisions, consumer surplus, and social welfare through downward traceability. The results show that gray market invasion consistently benefits low-end market retailers while disadvantaging high-end market retailers. It is profitable for the brand to allow gray market invasion when the willingness to pay in the low-end market is substantial. Blockchain adoption can effectively inhibit gray market invasion, benefiting high-end market retailers while harming low-end market retailers and the brand, depending on the cost and penalty fee involved with blockchain implementation. When the cost of blockchain adoption is relatively low, brand adopting blockchain may achieve a win-win-win market situation. However, although consumer surplus and social welfare can benefit from a gray market invasion, adopting blockchain reduces this impact, especially when cost of blockchain adoption and penalty fee are both relatively high.

Real-FlexE: Real-time Flexible channel Emulator

In digital communications, accurately modeling and replicating the channel effects between a transmitter and receiver is essential to assess their impact on transmitted data. Access to a real-time channel emulator is therefore crucial in the development of telecommunication systems. This paper presents a configurable, flexible, and high-performance FPGA-based channel emulator that supports multiple channel models, including Doppler, AWGN, and multi-path Fading. We explore bit-width precision in the components and present trade-offs between quality and resource utilization. Our exploration demonstrates ∼25%–∼45% savings in various resources while maintaining the accuracy of the channel effects and operating at a high throughput of 500 Msps.

Underwater visible light communication: recent advancements and channel modeling

Underwater visible light communication: recent advancements and channel modeling

An illumination-guided dual attention vision transformer for low-light image enhancement

Existing Retinex-based low-light image enhancement methods often overlook corruptions hidden in darkness or pattern collapse caused by the lit-up process. Recent deep learning approaches suggest the use of U-shaped networks with Vision in Transformer (VIT) to address these issues. However, most VIT-based methods focus on channel modeling to reduce expensive computational costs, but in which the restored images suffer from spatial illumination inconsistencies, artifacts, and blurriness. To end for this, we propose a novel one-stage Retinex-based Illumination-Guided Dual transformer model (IGDFormer) to lit up low-light images. The model consists of an estimator and a restorer. The estimator generates a light-up feature map and a lit-up image through pure Convolutional Neural Networks (CNNs). The restorer denoises the lit-up image with a U-shaped network equipped with an Illumination-Guided Dual Attention Block (IGDAB). Specifically, IGDAB consists of cascaded channel attention and window attention that achieves cross-channel/spatial modeling. Channel attention alleviates inductive bias through the CNN-Transformer collaborative layer, and window attention introduces spatial domains knowledge by partitioning and shifting. In addition, the light-up features act as values guide the interaction modeling of non-local illumination intensities in both the channel and spatial domains. Extensive experiments were conducted on 5 low-light image enhancement benchmarks and 1 dark object detection benchmark, which demonstrate that the efficacy of our IGDFormer and its superiority in restoring spatial details compared to other state-of-the-art methods. The code is available at https://github.com/YanJieWen/IGDFormer-light-up-dark.

Model-based reinforcement learning approach for federated learning resource allocation and parameter optimization

In this paper, we investigate the performance of a model-based approach for solving resource allocation and parameter adjustment problems in federated learning (FL) within a wireless network. Given the existence of models for energy, communication channels, and accuracy, such models can be leveraged to achieve improved performance. Additionally, machine learning techniques can be employed to identify known parts of the model and also exploit training data for unknown parts of the model, enabling the creation of complex policies. Model-based reinforcement learning (RL) methods have the potential to offer such solutions, particularly in resource allocation and parameter optimization settings where the model can be partially derived mathematically. Our results demonstrate that the use of such a method in FL scenarios leads to improvements in both performance and the number of iterations required to identify the desired policy. Our simulations demonstrate the significance of allocating appropriate resources for FL applications through proper consideration of inherent tradeoffs, as performance will not improve beyond a certain saturation point. Additionally, our proposed FL model takes intelligently into account the presence of slow users to propose efficient policies for users that may have access to more abundant resources.

Tracking Charge Carrier Paths in Freestanding GaN/AlN Nanowires on Si(111).

Functional and abundant substrate materials are relevant for applying all sophisticated semiconductor-based device components such as nanowire arrays. In the case of GaN nanowires grown by metalorganic vapor phase epitaxy, Si(111) substrates are widely used, together with an AlN interlayer to suppress the well-known Ga-based melt-back-etching. However, the AlN interlayer can degrade the interfacial conductivity of the Si(111) substrate. To reveal the possible impact of this interlayer on the overall electrical performance, an advanced analysis of the electrical behavior with suitable spatial resolution is essential. For the electrical investigation of the nanowire-to-substrate junction, we used a four-point probe measurement setup with sufficiently high spatial resolution. The charge separation behavior of the junction is also demonstrated by an electron beam-induced current mode, while the n-GaN nanowire (NW) core exhibits good electrical conductivity. The charge carrier-selective transport at the NW-to-substrate junction can be attributed to different, local material compositions by two main effects: the reduction of Ga adatoms by shadowing of the lower part of the NW structure by the top part during growth, i.e. the protection of the pedestal footprint from Ga adsorption. Our combination of investigation methods provides direct insight into the nanowire-to-substrate junction and leads to a model of the conductivity channels at the nanowire base. This knowledge is crucial for all future GaN bottom-up grown nanowire structure devices on conductive Si(111) substrates.

Multi-Cluster Approaches to Radio Sensor Array Channel Modeling and Simulation.

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

Evaluation of the Influence of the Dispersion and Diffraction Properties of the Ionosphere on the Bandwidth of the Trans-Ionospheric Channel

Theoretical substantiation and development of a hardware and software complex for estimating the band of dispersion distortion and the coherence band of fading in a satellite (trans-ionospheric) radio channel based on the results of GPS monitoring of the ionosphere. The basis for solving this problem is the development of a structural-physical model of the radio channel, which allows simultaneously taking into account the phase dispersion and its diffraction in small-scale inhomogeneities of the ionosphere. Analytical dependences of the band of dispersion distortions and coherence of frequency-selective fading on the average value and small-scale fluctuations of the total electron content of the ionosphere are obtained. It is shown that under conditions of ionospheric disturbances, the coherence band of fading can be much smaller than the dispersion band. In accordance with the obtained dependencies, a structure for constructing a hardware and software complex for estimating the dispersion and coherence bands of a satellite radio channel based on the improvement of the GPS-monitoring method of the total electronic content of the ionosphere with small-scale inhomogeneities has been developed

Joint optimization of spatial correlation for space-constrained MIMO underwater optical wireless communication system in weak turbulence

For the space-constrained underwater installations, spatial correlation is an essential factor affecting the reliability of the underwater optical wireless communication (UOWC) system with multiple-input multiple-output (MIMO) technology. In this paper, a joint optimization method for the layout and transmit power allocation of the light-emitting diode (LED)-based UOWC system is proposed and demonstrated under the weak turbulent channel. To analysis the transmission performance of the MIMO UOWC system, a composite channel model incorporating absorption, scattering and weak turbulence effects is established, and the channel spatial correlation characteristics with various system parameters is analyzed based on this model. Among these parameters, we take a 9 × 9 MIMO UOWC system as an example to simulate the communication performance of the optimization method under different turbulence intensities. The simulation results show the optimized system exhibits a channel capacity improvement of 13bps/Hz compared to the original system at the signal-to-noise ratio (SNR) of 20 dB and the same turbulence intensity. Moreover, the SNR of jointly optimized system can achieve a gain of 5 dB at the same bit error rate (BER). By extending the joint optimization algorithm to a 16 × 16 MIMO UOWC system, the optimized SNR can also obtain a gain of 7.5 dB which effectively demonstrates the universality of the proposed joint optimization algorithm.

Measurement-Based Tapped Delay Line Channel Modeling for Fixed-Wing Unmanned Aerial Vehicle Air-to-Ground Communications at S-Band

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning.

A review on free space optical communication links: 5G applications

Abstract Free space optical (FSO) communications has garnered significant attention as a potential substitute for radio frequency (RF) wireless communication in short and long range transmissions. Free-space optical (FSO) communication is a method where an optical beam is sent across an unguided channel, gaining favour in high-speed wireless networks due to its large optical bandwidth, fast data transmission, lack of licencing requirements, secure nature, and easy setup. Despite several inherent advantages that FSO has over existing radio frequency (RF) communication technologies, its link performance is severely hampered by atmospheric fog and turbulence, which not only reduce optical irradiance but also cause the modulated optical beam to deviate from its intended path, impacting the quality of the received signal. This review paper discusses FSO technology, channel impairments under varied atmospheric attenuation conditions, advantages, and applications. Various channel models in terms of probability density functions are described, which characterise the statistical nature of the irradiance fluctuations of optical transmission through scattering and atmospheric turbulence. Key objectives that need to be addressed in the near future, particularly in fifth-generation (5G) and beyond, includes enhancing capacity, boosting data rates, minimising latency, and enhancing service quality. Last but not the least, several uses of FSO that facilitates 5G and future technologies have also been covered in this study.

A Three-Dimensional Fully Polarized Millimeter-Wave Hybrid Propagation Channel Model for Urban Microcellular Environments

A Three-Dimensional Fully Polarized Millimeter-Wave Hybrid Propagation Channel Model for Urban Microcellular Environments

Deconstruction of the Franciscan Complex Central Terrane Mélange and re-evaluation of Franciscan mélanges and architecture of the northwestern San Francisco Bay Area, California, USA

Various mélange types occur within the Franciscan accretionary Complex of western California. The largest mélange body, called the Central Belt Mélange (or similar names) served earlier as the type example for the orogen-long, subduction channel model, yet in the northwestern San Francisco Bay Area the name does not accurately reflect the geology. The mélange designation was commonly applied where resistant exotic and native blocks of rock are scattered across a relatively smooth terrain. Detailed mapping shows that many blocks experienced post-accretion transport. Areally large rock masses previously designated as Central Belt Mélange consist of multiple units and less than 30% of the tectonostratigraphy is mélange. Weakly metamorphosed sandstone–mudrock broken to dismembered formational units and similarly deformed sandstone–mudrock submarine fan facies dominate the tectonostratigraphy. Subordinate mélanges of the northwestern San Francisco Bay Area are of tectonic or sedimentary origin. The sedimentary bodies represent submarine mass flow deposits. Tectonic mélanges mark Mesozoic subduction-zone faults or Cenozoic strike-slip faults. Discriminating between mélange types and their origins, and reconstructing tectonostratigraphic columns for major fault blocks, clarifies the primary accretionary complex architecture and reveals significant along-strike variations in the Franciscan subduction accretionary Complex.

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.

DendroTweaks: An interactive approach for unraveling dendritic dynamics.

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks - a toolbox designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks is tailored to interactive fine-tuning of subcellular properties, such as kinetics and distributions of ion channels, as well as the dynamics and allocation of synaptic inputs. It offers an automated approach for standardization and refinement of voltage-gated ion channel models to make them more comprehensible and reusable. The toolbox allows users to run various experimental protocols and record data from multiple dendritic and somatic locations, thereby enhancing model validation. Finally, it aims to deepen our understanding of which dendritic properties are essential for neuronal input-output transformation. Using this knowledge, one can simplify models through a built-in morphology reduction algorithm and export them for further use in faster, more interpretable networks. With DendroTweaks, users can gain better control and understanding of their models, advancing research on dendritic input-output transformations and their role in network computations.

LS-based multipath channel measurement method using software defined radio platform

Multipath channel measurement is the foundation of multipath channel modeling and analysis. Recently, the software defined radio (SDR) has provided new ideas for the design of multipath channel measurement systems due to universality and reconfigurability. Therefore, this paper introduces a multipath channel measurement method applicable to various SDR platforms. At first, to enhance the resolution of multipath measurements, a signal processing approach combining the least squares (LS) estimation algorithm within the SDR framework is proposed. Furthermore, to mitigate LS estimation errors stemming from high correlation among baseband signals, a scrambled measurement signal is introduced and its effectiveness is demonstrated through eigenvalue decomposition. Next, the systematic errors of In-phase and Quadrature(IQ) imbalance and bandwidth limitation are further analyzed and addressed within the SDR framework. Finally, the proposed measurement method is implemented based on a practical universal software radio peripheral (USRP) device and validated under simulated and real channels. The proposed measurement method mainly focuses on the baseband signal processing within the software component of SDR, making it applicable to most general USRP devices. The experiment results demonstrate that multipath channel measurement results with high accuracy can be obtained.

Photo‐activation of Tolane‐based Synthetic Ion Channel for Transmembrane Chloride Transport

While natural channels respond to external stimuli to regulate ion concentration across cell membranes, creating a synthetic version remains challenging. Here, we present a photo‐responsive uncaging technique within an artificial ion channel system, which activates the ion transport process from a transport‐inactive o‐nitrobenzyl‐based caged system. From the comparative ion transport screening, 1b emerged as the most active transporter. Interestingly, its bis(o‐nitrobenzyl) derivative, i.e., protransporter 1b' was inefficient in transporting ions. Detailed transport studies indicated that compound 1b is an anion selective transporter with a prominent selectivity towards chloride ions by following the antiport mechanism. Compound 1b' did not form an ion channel, but after the o‐nitrobenzyl groups were photocleaved, it released 1b, forming a transmembrane ion channel. The channel exhibited an average diameter of 6.5 ± 0.2 Å and a permeability ratio of PCl‐⁄PK+ = 7.3 ± 1.5. The geometry‐optimization of protransporter 1b' indicated significant non‐planarity, corroborating its inefficient self‐assembly. In contrast, the crystal structure of 1b demonstrates strong self‐assembly via the formation of an intermolecular H‐bond. Geometry optimization studies revealed the plausible self‐assembled channel model and the interactions between the channel and chloride ion.

Photo-activation of Tolane-based Synthetic Ion Channel for Transmembrane Chloride Transport.

While natural channels respond to external stimuli to regulate ion concentration across cell membranes, creating a synthetic version remains challenging. Here, we present a photo-responsive uncaging technique within an artificial ion channel system, which activates the ion transport process from a transport-inactive o-nitrobenzyl-based caged system. From the comparative ion transport screening, 1b emerged as the most active transporter. Interestingly, its bis(o-nitrobenzyl) derivative, i.e., protransporter 1b' was inefficient in transporting ions. Detailed transport studies indicated that compound 1b is an anion selective transporter with a prominent selectivity towards chloride ions by following the antiport mechanism. Compound 1b' did not form an ion channel, but after the o-nitrobenzyl groups were photocleaved, it released 1b, forming a transmembrane ion channel. The channel exhibited an average diameter of 6.5 ± 0.2 Å and a permeability ratio of PCl-⁄PK+ = 7.3 ± 1.5. The geometry-optimization of protransporter 1b' indicated significant non-planarity, corroborating its inefficient self-assembly. In contrast, the crystal structure of 1b demonstrates strong self-assembly via the formation of an intermolecular H-bond. Geometry optimization studies revealed the plausible self-assembled channel model and the interactions between the channel and chloride ion.