Fading Channels Research Articles

Reconstructing Signals in Millimeter Wave Channels Using Bayesian-Based Fading Models

Fading in communication channels presents eminently stochastic characteristics and is a significant challenge, especially at millimeter wave (mmW) frequencies, where the need for lines of sight and the high attenuation of obstacles complicate transmission. This article presents a model based on Bayesian fundamentals intended to improve the description and simulation of stochastic fading effects in these channels. It also includes the use of signal processing techniques to simulate and reconstruct the received signal, simulating the communication channel with an FIR filter. The results obtained by simulating the model show its ability to efficiently capture rapid and profound variations in the signal, typical of those that occur in urban and suburban environments and transmissions in the mmW spectrum. It also provides greater uniformity in signal reconstruction compared to the traditional models that are in use. Using Bayesian fundamentals, which allow dynamic adaptation to change in channel behavior, can improve the efficiency and reliability of networks, especially modern smart networks. Compared to traditional models, the proposed model offers improved signal reconstruction and fading mitigation accuracy, with prospects for future integration in smart communication systems. The better capacity in signal reconstruction presents itself as a differentiator of the model, suggesting greater precision in data transmission.

Joint Design of Altitude and Channel Statistics Based Energy Beamforming for UAV-Enabled Wireless Energy Transfer

In recent years, UAV-enabled wireless energy transfer (WET) has attracted significant attention for its ability to provide ground devices with efficient and stable power by flexibly navigating three-dimensional (3D) space and utilizing favorable line-of-sight (LoS) channels. At the same time, energy beamforming utilizing multiple antennas, in which energy beams are focused toward devices in desirable directions, has been highlighted as a key technology for substantially enhancing radio frequency (RF)-based WET efficiency. Despite its significant utility, energy beamforming has not been studied in the context of UAV-enabled WET system design. In this paper, we propose the joint design of UAV altitude and channel statistics based energy beamforming to minimize the overall charging time required for all energy-harvesting devices (EHDs) to meet their energy demands while reducing the additional resources and costs associated with channel estimation. Unlike previous works, in which only the LoS dominant channel without small-scale fading was considered, we adopt a more general air-to-ground (A2G) Rician fading channel, where the LoS probability as well as the Rician factor is dependent on the UAV altitude. To tackle this highly nonconvex and nonlinear design problem, we first examine the scenario of a single EHD, drawing insights by deriving an optimal energy beamforming solution in closed form. We then devise efficient methods for jointly designing altitude and energy beamforming in scenarios with multiple EHDs. Our numerical results demonstrate that the proposed joint design considerably reduces the overall charging time while significantly lowering the computational complexity compared to conventional methods.

An Innovative Method for Improving the Performance of UAV‐SM‐NOMA for 6G Networks Over Generalized Fading Channels

ABSTRACTThe emergence of 6G wireless communications has brought numerous challenges and opportunities for meeting the growing demands of enhanced system capacity, higher data rates, lower latency, improved security, and stringent quality of service (QoS) requirements. One promising solution to tackle these challenges involves utilizing unmanned aerial vehicles (UAVs) in conjunction with nonorthogonal multiple access (NOMA) and spatial modulation (SM). However, optimizing the performance of UAV‐based SM‐NOMA systems for both perfect and imperfect channel state information (CSI) over generalized fading channels (α–μ, k–μ and η–μ) while simultaneously considering multiple objectives is a complex task. To address these challenges, this paper proposes integrating the monarch butterfly optimization algorithm (MBOA) into UAV‐SM‐NOMA systems for 6G networks. Inspired by the foraging behavior of monarch butterflies, the MBOA aims to enhance UAV‐based SM‐NOMA systems' performance by optimizing resource allocation, increasing system sum rate, reducing outage probability (OP) and bit error rate (BER), improving spectrum efficiency (SE), and ensuring desirable signal‐to‐interference‐plus‐noise ratio (SINR) levels. Key findings include that the MBOA effectively improves performance metrics across generalized fading channels compared to existing UAV‐assisted NOMA and orthogonal multiple access (OMA) models. Simulation results confirm that the proposed UAV‐assisted SM‐NOMA model with MBOA significantly outperforms conventional approaches, demonstrating superior capacity, reliability, and efficiency in 6G network scenarios.

A novel carrier index M‐ary differential chaos shift keying modulation scheme

AbstractTo obtain better spectral efficiency and higher bit rate, a novel carrier index M‐ary differential chaos shift keying modulation scheme is proposed. In this system, part of subcarriers is assigned to the chaotic reference so that it can carry extra data bits through carrier index modulation, while one of the remaining subcarriers is activated to transmit the data‐bearing signal. In addition, M‐ary DCSK modulation is also applied to data‐bearing signals based on the constellation theory and Walsh codes. Theoretical bit error rate expressions are derived over the multipath Rayleigh fading and additive white Gaussian noise channels. Simulations and comparisons are performed with various combinations of chaotic sequence lengths, subcarrier numbers and constellation sizes. Results show that the proposed scheme can outperform other counterparts in both spectral efficiency and bit error rate performances.

Signal detection of M-MIMO-orthogonal time frequency space modulation using hybrid algorithms: ZFE + MMSE and ZFE + MF

Orthogonal Time Frequency Space (OTFS) modulation, coupled with Massive Multiple Input Multiple Output (Massive-MIMO) technology, presents a promising avenue for enhancing the efficiency and reliability of fifth-generation (5G) and beyond fifth-generation (B5G) systems. OTFS modulation offers robust communication in high-mobility environments by converting signals into the delay-Doppler domain, ensuring better performance over fading channels. MIMO enhances wireless networks by using large antenna arrays to boost capacity, spectral efficiency, and reliability, making both technologies vital for next-generation radio systems. In this study, we explore the detection of (8 × 8, 16 × 16, 64 × 64, and 256 × 256) Massive-MIMO-OTFS signals utilizing two prominent detection algorithms: zero forcing equalization (ZFE) with matched filter (MF) known as (ZFE + MF) and Zero Forcing with minimum mean square error (MMSE) known as (ZFE + MMSE). The combination of Massive MIMO and OTFS offers improved spectral efficiency, robustness against fading, and enhanced spatial multiplexing capabilities. The parameters such as bit error rate (BER) and power spectral density (PSD) are analyzed and estimated for the proposed hybrid and conventional algorithms. The proposed algorithms obtained the SNR and PSD gain of 3.2 dB, 3.2 dB, 4.8 dB, and 6.1 dB gain, respectively, for 8 × 8, 16 × 16, 64 × 54, and 256 × 256 MIMO systems. Further, the PSD gain of -390 is obtained for the 256 × 256 system, resulting in high spectral efficiency.

SM-VBLAST Aided Ultra-Massive MIMO Over Generalized Fading Channel

SM-VBLAST Aided Ultra-Massive MIMO Over Generalized Fading Channel

Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel

Deep learning has revolutionized communication systems by introducing innovative approaches to address channel impairments through end-to-end models. Autoencoders, a type of deep learning architecture, are adept at learning compact data representations. However, conventional autoencoders in end-to-end models can suffer from overfitting, which limits their effectiveness in noisy communication environments. To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features. Despite its effectiveness, the SAE model may still lack the ability to focus on the most relevant features of the input data. To overcome this limitation, we further introduce an Attention Mechanism-based Sparse Autoencoder (ASA) model. This model integrates the feature extraction capabilities of a sparse autoencoder with an attention mechanism that selectively highlights informative features of the signal. Through simulations, we demonstrate that both proposed models significantly improve M-PSK and M-QAM communication system performance. When trained at 7 dB, both proposed models exhibit significant performance improvements at higher testing average SNRs. Our results show that the SAE model outperforms the conventional Maximum Likelihood Detection (MLD) model and baseline autoencoder systems but suffers from error floor issues. The SAE model suffers from an error floor at average SNRs beyond 16 dB for BPSK and 14 dB for higher-order modulation schemes. As the value of M increases, the performance gap between the MLD and the proposed SAE model narrows. The ASA model, however, effectively mitigates the error floor observed in the SAE model for all values of M and across all modulation schemes. This research highlights the benefits of integrating an attention mechanism with SAE, resulting in enhanced robustness and reliability in communication systems characterized by improved accuracy and reduced error rates.

An Estimator for Input Sent by ILC Controllers Through Multi-path Fading Channels

An Estimator for Input Sent by ILC Controllers Through Multi-path Fading Channels

Wireless Channel Propagation Model for Inland Waterway Bridge Scenario

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

Cooperative Spacing Control of Vehicle Platoon via Relative Output Feedback Considering Analog Fading Networks

ABSTRACTThe influence of analog fading channels on platoon control performance is studied via relative output feedback. The main goal is to determine the existence of a distributed output feedback controller capable of achieving mean square stability (MSS) for the underlying vehicle platoon systems over analog fading channels. First, the conditions, closely associated with the statistical characteristics of fading channels, for achieving platoon MSS are derived for three cases: undirected information flow (UIF) topology with identical fading channels, balanced directed information flow (BDIF) topology with identical fading channels, and general information flow topology (IFT) with nonidentical fading channels, respectively. These conditions explicitly reveal how the statistical characteristics of fading channels and IFT jointly affect vehicular stability. Second, in order to mitigate the impact of fading channels on platoon MSS, some feasible dynamic observer‐based controllers by using the relative output feedback, are proposed for each one of the former two situations by solving the corresponding modified Riccati inequalities (MRIs); and in the last situation, by employing edge Laplacian to model the vehicle platoon dynamics, a sufficient condition based on the solution of an LMI, and a feasible state‐based controller based on the solution of an MRI, are provided. Finally, simulations are carried out to compare three distinct fading channels, namely the Nakagami channel, Rician channel, and Rayleigh channel. The outcomes substantiate the superior and effective performance of the implemented control methods in guaranteeing the platoon MSS.

Tridiagonal Weighting‐Based Interference Cancellation Method in High‐Mobility OFDM Systems

ABSTRACTIn orthogonal frequency division multiplexing (OFDM) systems, the time‐selective fading channel causes the signal Doppler expansion in high‐mobility scenarios, leading to intercarrier interference (ICI) in the received signals. People had employed a full‐dimensional weighting matrix method to reduce the Doppler‐assisted ICI, though it required a high implementation complexity. In order to reduce the ICI and produce lower complexities, this paper proposes to exploit the tridiagonal weighting matrix to suppress ICIs, in which an optimization problem is constructed to design the tridiagonal weighting matrix, and the nonzero coefficients within the tridiagonal matrix denote optimization variables. Moreover, the optimization objective exploits the system bit error rate (BER) instead of the conventional signal to interference plus noise ratio (SINR), which results in a highly nonlinear optimization problem. Therefore, an iterative solving approach is proposed according to the successive convex approximation (SCA). Simulations show that the proposed method can achieve a good balance between the complexity and BER degradation, especially in the scenario with moderate Doppler spreads.

On the Effective Rate and Error Rate Analysis over Fluctuating Nakagami-m Fading Channel

On the Effective Rate and Error Rate Analysis over Fluctuating Nakagami-m Fading Channel

Improving cell edge user performance using cooperative cyclic interleaved non-orthogonal multiple access under rayleigh fading channel

Improving cell edge user performance using cooperative cyclic interleaved non-orthogonal multiple access under rayleigh fading channel

Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules

ABSTRACTCurrently, researchers and commercial entities worldwide are highly interested in optical wireless communication links operating in terrestrial environments. These links offer the distinct advantage of enabling high‐speed data transmission while keeping operational and installation costs low, all without requiring licensing. Free‐space optical (FSO) communication has established itself as a reliable method for delivering ultra‐high data‐rate services. However, the presence of atmospheric turbulence‐induced fading poses a significant challenge for FSO communication systems, particularly when operating over channels affected by time‐varying turbulence. The effectiveness of FSO systems is greatly influenced by atmospheric conditions in a specific area because the laser beam propagates through the atmosphere. As a consequence, this can lead to a significant degradation such as high complexity, high estimation error, and high bit error rate (BER) in the performance of FSO systems. This paper presents a stacked dual attention LSTM network with layered neural Turing machine (SDALSTM‐LaNTM) for FSO channel estimation over lognormal turbulence‐induced fading channels. In this approach, the stacked structure ensures a robust feature extraction capacity of SDALSTM and enhances its ability to capture intricate relationships among multiple atmospheric variables. After employing the stacked LSTM networks, the extracted features are combined with the augmented features obtained through the dual attention (DAttn) mechanism by concatenation. Then the LaNTM architecture used external memory to solve the channel parameter estimation problem and update new set of hidden features for estimation task. The ability to access and update memory enables the model to capture long‐term dependencies and context, making it beneficial for channel prediction in FSO systems. Presently, we are in the process of verifying the channel estimation model using measured FSO channel data. Simulation results clearly demonstrate the outstanding estimation performance of the SDALSTM‐LaNTM model in FSO systems, specifically when dealing with lognormal turbulence‐induced fading channels. Moreover, we highlight the effectiveness of the SDALSTM‐LaNTM model by conducting a comprehensive comparison with existing models, considering factors such as average capacity performance, outage probability estimation, and BER.

Discrete modulation continuous variable quantum key distribution under fast fading channel

After decades of development, continuous variable quantum key distribution technology still cannot meet people’s needs for secure communication over long distances. Free-space quantum communication provides a new way for secure communication over long distances. However, because the communication connected in free space is inevitably affected by atmospheric turbulence, the transmission rate of the channel changes rapidly according to the probability distribution, making it difficult for the communication personnel to determine the transmission rate of the channel. At the same time, discrete modulated continuously variable quantum key Distribution protocol (DM-CVQKD) has certain advantages over Gauss modulated continuously variable quantum key Distribution protocol (GM-CVQKD) in the communication distance. Therefore, this paper analyzes the key rate of DM-CVQKD in this case (that is, in the fast attenuation channel), proves that it can resist the collective attack, and observes the relationship between the parameters and the key rate in the fast attenuation channel by numerical simulation and control variables. Finally, through the analysis of numerical simulation results, it is found that DM-CVQKD can still maintain a high key rate under certain conditions, which proves that long-distance free space quantum communication is feasible under certain conditions. At the same time, it was found that the performance of DM-CVQKD was affected by the extreme case where Eve completely controlled the instantaneous transmittance.

OFDM Transmission in Rayleigh Fading Channel for S-Box and 3D Chaotic Maps Based Encrypted Image

Data encryption is an important part of the communication system. The Chaos essential properties, make it a crucial candidate for encryption applications. There is a compromise between complexity and security in previous studies. In this study, high security was achieved with low complexity. This paper proposed a 3D chaotic map and S-Box has been cascaded to get a high efficiency as complex algorithms or multi-iteration schemes. The first stage is ciphering using 3D cat-map, the second stage is S-box based on 3D henon map, while the third stage is another ciphering stage using 3D henon map. In this study, various encryption techniques, including cipher algorithms and substitution box, are combined with the OFDM system to establish a secure image transmission over a Rayleigh fading channel. QPSK modulation is used to ensure the simplicity of the proposed system. Six gray images are used, Lena, the cameraman, Barbara, Baboon, pepper and Elaine for testing and comparing with previous works. Security analysis is performed to evaluate the quality and security of the encryption process, the entropy value reach 7.99, correlation coefficient is around zero and the histogram is uniform. In addition, the key size is 2630. For image transmission evaluation, the PSNR and BER are utilized and it reached 10-5 for BER. According to the statistical results, the proposed image encryption scheme is secure and efficient.

Coverage Probability Analysis of a Double IRS Assisted SISO Communication System

Intelligent reflecting surface (IRS) improves wireless communication efficiency and coverage probability. Existing studies focus on IRS deployment but overlook optimal fading channels. This research aims to identify and analyze the most effective channel fading estimation for the double IRS system model. The coverage probability using two-IRS is derived under different fading channels like Rayleigh and Rician to find the optimal solution in a Single-Input-Single-Output (SISO) system. Additionally, a closed-form expression for the coverage probability using statistical channel state information is derived while assuming no direct path between the transmitter and receiver. The analytical results are validated through simulations. It offers valuable insights into the modeling and analysis of double IRS-assisted wireless communication systems.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

Weighted joint LRTs for cooperative spectrum sensing using K-means clustering

In this paper, a blind centralized cooperative spectrum sensing (CSS) with soft decision fusion is considered. The fusion center (FC) constructs the energy vector from the collaborating secondary nodes. The energy feature is assumed to be an efficient measure, as it is widely used and directly correlates with the strength of the received signal. The K-means clustering algorithm is employed to extract descriptive statistical features about the distributions of the absence and presence of the primary user (PU) signal, such as the mean and non-centrality parameter. In the framework of this statistical analysis, the signal-to-noise ratio (SNR) at each node is easily estimated and examined. Equal, selective, and weighted combining techniques are applied to develop three joint likelihood ratio test (JLRT)-based algorithms. These algorithms are implemented in the context of simple hypothesis testing, where the distribution of the data is fully specified. Furthermore, they are justified by the Neyman-Pearson theorem, which constructs the most powerful test for a given significance level. The proposed selective and weighted JLRT approaches are based on the estimated SNRs at each sensor, reflecting their reliability. Several comparison scenarios between the proposed algorithms, K-means, fuzzy c-means (FCM), and OR-rule are simulated over Rayleigh fading channel with low average SNR and few samples. The simulation results reveal that the proposed tests outperform other CSS techniques. Additionally, asymptotic theoretical expressions for probability of detection and the probability of false alarm are derived, which show high agreement with the simulated results.

5G network performance using novel MIMO mixed FSO/MMW communication systems under pointing errors effect: test analysis using image transmission

Abstract Millimeter waves (MMW) enable high data rates over short distances; free-space optical (FSO) provides high-capacity optical wireless transmissions; and multiple-input multiple-output (MIMO) maximizes antenna utilization. These are the three leading technologies that work harmoniously to deliver 5G’s superior performance. This collaborative effort results in 5G’s ability to offer high connection capacities, low latency, and rapid speeds, creating new opportunities for industrial and internet of things applications. In this paper, the proposed system combines FSO links and MMW radio frequency (RF) links to enhance the performance of mixed FSO/RF systems. FSO experiences turbulence and pointing errors (PE), while MMW undergoes fading. Using both FSO and MMW provides diversity against channel fading. Furthermore, both the FSO and MMW links utilize MIMO technology to combat fading through spatial diversity. The FSO link is modeled with a Gamma-Gamma Turbulence model and PE. The MMW link is modeled with Rayleigh fading. By combining these two types of links with MIMO, the system can achieve improved performance compared to using either FSO or MMW alone. The dual nature of the system provides robustness against different types of channels fading effects. Overall, the goal is to enhance performance by capitalizing on the complementary channel characteristics of optical and RF links and exploiting MIMO’s spatial diversity benefits. In the present work, closed-form formulas accounting for the influence of PE are generated for the bit error rate (BER). This article also explores the MIMO mixed FSO/MMW system with multiple modulation techniques under various turbulence situations. The suggested system is ultimately verified by transmitting images with suitable fixed BER.