Precise Channel Research Articles

A comprehensive support-free slicing method library for variable posture additive manufacturing

Support-free slicing technology plays a critical role in additive manufacturing by reducing costs and simplifying post-processing. However, due to the complexity of geometric features, existing support-free slicing strategies often lack the necessary universality in industry. This paper proposes a method library comprising six basic support-free slicing methods to enhance the applicability and computational efficiency. The general methods in the library include facet-based methods and voxel-based methods, the former facilitates rapid slicing, while the latter enables the re-decomposition of complex structural parts. For parts that cannot be covered by general methods, customized methods are developed. These include constructing a support bridge for multi-direction slicing of overhanging regions in an arch model and extracting the optimal central axis for internal channels to achieve precise channel decomposition. Additionally, the methods in the library can be flexibly combined. By recognizing surface features and incorporating manual intervention, models can be decomposed into multiple sub-models, with the most computationally efficient method is matched to each sub-model. The layers and tool-paths of each sub-model are generated by the optimal method. Four typical models are deposited without any support in a five-axis printer to verify the feasibility of the proposed methods.

Channel estimation for Massive MIMO systems aided by intelligent reflecting surface using semi-super resolution GAN

Intelligent Reflecting Surfaces (IRSs) coupled with Massive Multiple-Input-Multiple-Output (MIMO) millimeter wave (mmWave) systems hold immense promise for the next generation of wireless communications. However, harnessing their full potential requires accurate channel state information (CSI). Despite the benefits of IRSs, such as passive element integration and energy efficiency, precise channel estimation becomes a formidable challenge due to the absence of active elements. In this paper, we tackle these challenges by employing generative adversarial networks (GANs) to estimate the channel’s cascade matrix between the base station (BS) and mobile users. To leverage the high correlation among adjacent elements in the IRS, we propose turning off a majority of these elements during the estimation phase, effectively creating a low-resolution channel. We then introduce the semi-super resolution GAN (SSRGAN) model, capable of inferring channel values for the deactivated elements based on existing correlations. Our new SSRGAN-based channel estimation method transforms low-resolution channel data into high-resolution channel data. Through a comprehensive comparative analysis, our study showcases the superior performance of our SSRGAN channel estimation method compared to established benchmark schemes.

Design and simulation of an enhanced optical demultiplexer using photonic crystal resonators

The purpose of this paper is to examine the design, simulation, and optimization of a demultiplexer optical system using photonic crystal resonators. Utilizing the distinct properties of photonic crystals, the proposed device achieves high-quality resonances, efficient wavelength separation, and minimal crosstalk. These characteristics are crucial for enhancing the performance of dense wavelength division multiplexing (DWDM) systems, which require precise and efficient channel separation to manage increasing data traffic. Extensive simulations conducted with the RSoft CAD tool explore the impact of varying resonator parameters such as refractive index, geometric dimensions, and material composition on the device's performance. The results indicate substantial improvements in transmission efficiency and quality factor, with average values of 98.675% and 3131.125, respectively. Furthermore, the ability to fine-tune these parameters allows for the customization of the demultiplexer to specific wavelength ranges, making it highly adaptable for various telecommunications applications. This research aims to propel the development of compact, high-performance photonic devices that meet the growing demands of modern communication networks, ultimately contributing to more efficient and reliable optical communication systems.

DFT-spread OTFS-NOMA for downlink integrated positioning and communication

In this article, a non-orthogonal multiple-access (NOMA) transmission system that incorporates discrete Fourier transform (DFT) spread orthogonal time-frequency space (OTFS) modulation is proposed for the downlink integrated positioning and communication (IPAC). This system is designed to decrease the Peak-to-Average Power Ratio (PAPR) within the OTFS-NOMA system while simultaneously serving both communication user (CU) and positioning user (PU) within the same time-frequency resources. Firstly, we demonstrate the applicability of the proposed DFT-spread OTFS-NOMA (DFT-s-OTFS-NOMA) system for downlink transmission. It efficiently serves both the CU and PU concurrently within the same time and frequency resources, thereby increasing spectrum utilization. Subsequently, we develop a two-stage parameter estimation algorithm focusing on accurate positioning parameter estimation for PU and precise channel estimation for CU. Then, the position of the PU is estimated using the time of arrival (TOA) model and the linear least squares (LLS) approach. Simulation demonstrates a 9 dB PAPR reduction in the proposed system compared to the existing OTFS-NOMA system. Additionally, the proposed two-stage positioning method for the PU achieves centimeter-level accuracy in position estimation and near millimeter-per-second-level accuracy in velocity estimation. What is more, the performance of channel estimation and symbol detection, aided by the PU signal in the DFT-s-OTFS-NOMA system, demonstrates resilience against Doppler effects without the need for reliance on pilots. This ensures sustained an effective Bit Error Rate (BER) even in time-variant high mobility scenarios.

Optimized deep learning‐based channel estimation for pilot contamination in a massive multiple‐input‐multiple‐output‐non‐orthogonal multiple access system

SummaryOne of the advanced field in 5G cellular networks is the Massive Multiple‐Input‐Multiple‐Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE.

Deep Learning-Assisted High-Pass-Filter-Based Fixed-Threshold Decision for Free-Space Optical Communications

This paper proposes a deep learning (DL)-assisted high-pass-filter (HPF)-based fixed-threshold decision (FTD) for free-space optical (FSO) communication. HPF is applied to reduce the scintillation effect by filtering out the low-frequency components of the received signal. However, the performance is limited owing to the signal distortion from HPF and remnant scintillation effect due to insufficient filtering. Therefore, the DL model is adopted to improve the performance of HPF-based scintillation effect compensation. The multilayer perceptron (MLP) model is used to adaptively select the peak frequency component of the received signal as the optimized cutoff frequency of HPF. Furthermore, recurrent neural network (RNN) and long short-term memory (LSTM) models are cascaded after HPF to compensate for the remnant scintillation effect and recover the signal distortion without the optimization of HPF cutoff frequency. The simulation was conducted under different turbulence channels and data rates. Simulation results showed that MLP-assisted adaptive optimized cutoff frequency and cascaded LSTM and HPF methods were close to the adaptive-threshold decision with precise channel state information under various turbulence channel degrees.

Analysis of the outage performance of energy-harvesting cooperative-NOMA system with relay selection methods

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.

SCB-YOLOv5: a lightweight intelligent detection model for athletes’ normative movements

Intelligent detection of athlete behavior is beneficial for guiding sports instruction. Existing mature target detection algorithms provide significant support for this task. However, large-scale target detection algorithms often encounter more challenges in practical application scenarios. We propose SCB-YOLOv5, to detect standardized movements of gymnasts. First, the movements of aerobics athletes were captured, labeled using the labelImg software, and utilized to establish the athlete normative behavior dataset, which was then enhanced by the dataset augmentation using Mosaic9. Then, we improved the YOLOv5 by (1) incorporating the structures of ShuffleNet V2 and convolutional block attention module to reconstruct the Backbone, effectively reducing the parameter size while maintaining network feature extraction capability; (2) adding a weighted bidirectional feature pyramid network into the multiscale feature fusion, to acquire precise channel and positional information through the global receptive field of feature maps. Finally, SCB-YOLOv5 was lighter by 56.9% than YOLOv5. The detection precision is 93.7%, with a recall of 99% and mAP value of 94.23%. This represents a 3.53% improvement compared to the original algorithm. Extensive experiments have verified that our method. SCB-YOLOv5 can meet the requirements for on-site athlete action detection. Our code and models are available at https://github.com/qingDu1/SCB-YOLOv5.

Signal Processing and Channel Modelling for 5G Millimeter-Wave Communication Environment

Compared to frequency bands below 6 GHz, 5G millimeter waves offer several advantages, including a large bandwidth, minimal null delay, and flexible null port configuration. To comprehend the channel characteristics of 5G millimeter-wave technology, conducting channel measurements on it is essential. Hence, to ensure precise 5G millimeter-wave channel measurements and facilitate channel modelling, this study recommends utilizing a phased array antenna-based method for the channel measurement. The experimental outcomes demonstrated that the shadow fading term of the actual measurement data follows a normal distribution in both line-of-sight and non-line-of-sight scenarios. Additionally, the K-S test confirms that all Boolean variables are equal to 1 and all numerical variables are greater than α. The line-of-sight scenario produces a logarithmic mean delay extension of –7.4 and a standard deviation of 0.12, based on actual measured data. Meanwhile, the 3GPP shows a logarithmic mean of –7.4 and a standard deviation of 0.15. In the non-line-of-sight scenario, the logarithmic mean delay extension is –7.3 with a standard deviation of 0.17, while the 3GPP produces a logarithmic mean of –7.4 and a standard deviation of 0.19. The data presented closely adheres to the 3GPP model. It is evident that the channel measurement method, proposed within the study, effectively measures the parameters within the delay domain. Concerning the prolonged pitch angle and azimuth angle ranges, they measure 14°–31° and 14°–29° in the line-of-sight situation, and 21°–33° and 19°–37° in the corresponding non-line-of-sight situation. Additionally, the logarithmic mean and standard deviation for both the pitch angle and azimuth angle in the line-of-sight scenario are 1.32 and 0.09, respectively. The logarithmic mean and standard deviation of the azimuth angle of arrival are 1.35 and 0.08, respectively. The above results show that the method proposed in the study enables the measurement of 5G millimeter-wave channels and is important for millimeter-wave channel modelling.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.

Accurate drone corner position estimation in complex backgrounds with boundary classification

This study develops an efficient approach for precise channel frame detection in complex backgrounds, addressing the critical need for accurate drone navigation. Leveraging YOLACT and group regression, our method outperforms conventional techniques that rely solely on color information. We conducted extensive experiments involving channel frames placed at various angles and within intricate backgrounds, training the algorithm to effectively recognize them. The process involves initial edge image detection, noise reduction through binarization and erosion, segmentation of channel frame line segments using the Hough Transform algorithm, and subsequent classification via the K-means algorithm. Ultimately, we obtain the regression line segment through linear regression, enabling precise positioning by identifying intersection points. Experimental validations validate the robustness of our approach across diverse angles and challenging backgrounds, making significant advancements in UAV applications.

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

EAMR-Net: A multiscale effective spatial and cross-channel attention network for retinal vessel segmentation.

Delineation of retinal vessels in fundus images is essential for detecting a range of eye disorders. An automated technique for vessel segmentation can assist clinicians and enhance the efficiency of the diagnostic process. Traditional methods fail to extract multiscale information, discard unnecessary information, and delineate thin vessels. In this paper, a novel residual U-Net architecture that incorporates multi-scale feature learning and effective attention is proposed to delineate the retinal vessels precisely. Since drop block regularization performs better than drop out in preventing overfitting, drop block was used in this study. A multi-scale feature learning module was added instead of a skip connection to learn multi-scale features. A novel effective attention block was proposed and integrated with the decoder block to obtain precise spatial and channel information. Experimental findings indicated that the proposed model exhibited outstanding performance in retinal vessel delineation. The sensitivities achieved for DRIVE, STARE, and CHASE_DB datasets were 0.8293, 0.8151 and 0.8084, respectively.

OFDM-Based VLC Systems: A Systematic Review

Visible Light Communications (VLC) represents a new technology of wireless communications allowing high data rate, high-speed internet access, green and friendly communication system, especially for indoor users, where the use of Light Emitting Diodes (LEDs) is growing as a viable alternative to traditional illumination. Orthogonal frequency division multiplexing (OFDM) is a promising modulation format for optical wireless communication (OWC); however, precise channel estimation is required for synchronization and equalization. Moreover, one of the challenging issues for OFDM is its high Peak-to-Average Power Ratio (PAPR) due to the superposition of all subcarriers; therefore, the system requires a high linearity and dynamic range. This paper provides a systematic overview of OFDM-based VLC systems, highlighting the essential aspects of PAPR reduction and channel estimation techniques. Several technical challenges have been addressed to realize the full potential of OFDM-based VLC technology. Moreover, we provide new insights into the over-explored and under-explored areas, which lead us to identify open research problems of VLC based on OFDM. Concisely, this paper serves as a guide and a starting point for researchers willing to research VLC using OFDM.

Recurrent neural networks for enhanced joint channel estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks

FBMC is a pivotal system in 5G, serving as a cornerstone for efficient use of available bandwidth while simultaneously meeting stringent requirements for high spectral efficiency. Notably, FBMC harnesses the power of multicarrier modulation (MC), a good alternative to orthogonal frequency division multiplexing (OFDM) technology that supports fourth-generation (4G) systems. The wireless communications field is full of challenges, the most important of which are channel estimation and interference cancellation, both of which deserve comprehensive study to increase the efficiency of data transmission. In this paper, our investigation takes a deliberate step towards the convergence of two prominent modulation models: OFDM and FBMC. We specifically contrast these modulation techniques with the intricate field of joint channel estimation and interference cancellation (JCEIC). In this research study, we take advantage of recurrent neural networks' (RNNs') efficiency as a vehicular channel to perform precise channel estimation and recovery of uncorrupted transmitted signals, thereby lowering the bit error rate (BER). Our channel estimation for a dual selective channel is based on the thoughtful placement of pilots scattered over the temporal and frequency dimensions, and is further improved by the interference cancellation method of low complexity that was selected. Our JCEIC proposal aims to integrate RNNs carefully, using the output sequences of JCEIC algorithms as useful inputs to this neural architecture. By clearly demonstrating a decrease in BER as compared to traditional approaches, it is evident that the performance of the novel approach is near to that of a perfect channel. Additionally, a comparison of the performance of FBMC and OFDM systems at various signal-to-noise ratios reveals a clear performance divide that favors the former in terms of system efficiency. The BER is restricted by FBMC to a commendable threshold of less than 0.1 at a modest 5 dB, continuing the higher trend started by its improved RNN-based channel estimate. The accuracy of channel estimation is clearly improved by this paradigm shift, and the computing complexity typical of 5G networks is also clearly reduced.

Precise channel temperature prediction in AlGaN/GaN HEMTs via closed-form empirical expression

In this study, we introduced a closed-form empirical expression for estimating the channel temperature in AlGaN/GaN HEMTs. This model incorporates parameters such as substrate thickness, gate length, gate width, and temperature-dependent thermal conductivity. The model's validity was rigorously established through comprehensive comparisons involving the channel temperature measurement procedure (DC) and TCAD device simulations. The outcomes exhibited a noteworthy alignment with the observed model data, reinforcing its credibility. The model yields a notably improved accuracy in channel temperature estimation compared to assumptions based on constant thermal conductivity. This observation holds particular significance for GaN/Sapphire HEMTs. The utilization of the closed-form expression enables the simultaneous optimization of both electrical and thermal properties, utilizing conventional computer-aided design tools.

A CA and ML approach for M-MIMO optical non-orthogonal multiple access power efficiency

Abstract The non-orthogonal multiple access (NOMA) multiple access approach can be used in future wireless communication systems to support massive connections and increase spectrum efficiency. Because user signal intensities and interference levels vary, precise channel assessment is essential in NOMA. Optimal power allocation and decoding order are made possible by precise algorithms, increasing system effectiveness and performance. However, NOMA can be adversely impacted by high peak-to-average power ratio (PAPR) values, leading to worsened system performance and more complex power amplifiers. In order to solve this issue, this paper recommends PAPR reduction in NOMA using companding methods for 512, 256, and 64 sub-carriers. Nonlinear companding techniques, such as MA and A-law companding, can efficiently reduce the high peak power of NOMA signals while reducing distortion and enhancing overall system dependability. The effectiveness of the proposed companding methods is evaluated using simulations, and the results demonstrate a significant decrease in PAPR, ensuring higher bit error rate (BER) effectiveness and transmission resilience in NOMA-based communication systems. The proposed approach is compared to the traditional Ml (C- Ml) and A-Law (C- A-Law).

Blind reconfigurable intelligent surface-aided fixed non-orthogonal multiple access for intelligent vehicular networks

In intelligent vehicular networks, vehicles should be able to communicate with their surroundings while traveling. This results in more efficient, safer, and comfortable driving experiences, as well as new commercial prospects in a variety of industries. Connected vehicles and autonomous vehicles expect 100% reliable connectivity without any compromise in quality. However, due to challenges such as difficult channel terrains in urban scenarios and dead zones, the reliability of current vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communication systems cannot be guaranteed. The performance of vehicular networks can be considerably enhanced with reconfigurable intelligent surfaces (RIS). Non-orthogonal multiple access (NOMA) allows for massive connectivity with the surroundings. In vehicular networks, the RIS-assisted NOMA can ensure regulated channel gains, better coverage, throughput, and energy efficiency. In this work, a blind RIS-assisted fixed NOMA (FNOMA) system is proposed for a downlink V2I scenario. The closed-form analytical outage probability and throughput expressions are derived by considering RIS as an intelligent reflector and as a roadside unit. It is observed that the analytical and Monte Carlo simulation results are closely related. In simulations, it has been discovered that RIS-assisted FNOMA outperforms the traditional NOMA variants in terms of outage and throughput. Even without precise channel knowledge, blind RIS transmission outperforms traditional NOMA variants due to huge array gain. The increase in the number of reflective elements also results in a significant improvement in signal-to-noise ratio gains.

Measurement system for ultraviolet channel modeling and communications.

There has been an increasing interest in ultraviolet (UV) communications as a promising technology for non-line-of-sight (NLOS) networking by exploiting atmospheric scattering at UV wavelengths that enables a unique NLOS UV communication channel. While there has been significant theoretical and simulation-based investigation of the UV channel characteristics, there is limited work in terms of experimental research and validation of the analytical models. In this paper, we present a flexible experimental system for precise UV channel and communications measurements. Specifically, a transceiver system is developed that consists of a gimbal, UV light-emitting-diode array, and photomultiplier tube detector, node synchronization, and LabVIEW-based data acquisition subsystems. Novel techniques to precisely characterize the UV LED array radiation pattern, absolute transmit power, and field of view of the detector are also presented. The utility of the developed system is then demonstrated by performing a variety of experiments including UV channel model validation and steering optimization for UV communication links where the results were in very good agreement with theory and simulation.

DCSaNet: Dilated Convolution and Self-Attention-Based Neural Network for Channel Estimation in IRS-Aided Multi-User Communication System

Channel estimation (CE) is a critical part for intelligent reflecting surface (IRS) aided multi-user communication (MUC) systems. However, the cascaded channel of the IRS-aided MUC (IRS-MUC) is a complex multi-dimensional channel, which is difficult to estimate the precise channel matrix in practice. In this letter, we propose a dilated convolution and self-attention based neural network (DCSaNet) to handle the CE in the IRS-MUC system. Specifically, the dilated convolution block is used to improve the feature extraction for CE during the network training. Further, the weighed features are obtained by the self-attention block. Last, a lightweight model, DCSaNet-l is applied to reduce the network parameters for the practical IRS deployment. Experimental results show that the proposed DCSaNet can significantly lower the normalized mean square error (NMSE), accelerate the training speed under different SNR cases and different channel dimensions. The results also verify that the lightweight DCSaNet-l can reach a near optimal performance of the proposed DCSaNet, but further significantly reduce the parameter amount by more than 83.7%.