Channel Scenarios Research Articles

Performance Analysis of RIS-Assisted Index Modulation for Terahertz Communications

The significance of Terahertz (THz) communication for the upcoming sixth generation (6G) communications, which offers abundant bandwidth for high-speed data transmission. However, THz frequencies face challenges such as propagation attenuation and molecular absorption that limit their coverage range. To address this issue, the use of reconfigurable intelligent surfaces (RIS) and index modulation (IM) has been proposed to extend coverage and reduce bit error rates (BER) at low transmitted power. This paper introduces a novel RIS-assisted IM system for THz communication, employing semi-definite relaxation (SDR) optimization techniques for passive beam forming at the detector. The proposed system, named PRIS-IM, demonstrates superior performance compared to conventional RIS-IM (CRIS-IM) and conventional IM (CIM) without RIS in THz frequencies. Simulation results illustrate a lower BER at a higher signal to-noise ratio (SNR) achieved by increasing the number of passive elements. Furthermore, the mutual information (MI) of the PRIS-IM system is derived and validated with simulation, and the achievable rate of the PRIS-IM system is improved by increasing the number of transmitting antennas and reflecting elements. Moreover, the PRIS-IM system is analyzed in different channel scenarios and distances. Overall, the proposed system exhibits the potential to significantly enhance the BER and MI performance of THz communication, indicating promising prospects for future wireless networks.

S-D HFFM: A Network Shallow-Deep Improvement Mechanism for Ship Detection in Low-Light Navigational Channel Scenarios

S-D HFFM: A Network Shallow-Deep Improvement Mechanism for Ship Detection in Low-Light Navigational Channel Scenarios

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Transmission Diversity by Alamouti-Coding, Propagation Control by IRS in CDMA-FBMC-OQAM System

This manuscript suggests a modulation scheme for exploiting transmission diversity by integrating Alamouti coding and controlling the transmission by intelligent reflecting surface (IRS) in code division multiple access filter bank multicarrier offset quadrature amplitude modulation (CDMA-FBMC-OQAM) system, a promising technique for future-generation wireless communication. To neutralize the impact of channel phases, the IRS elements are in charge of intelligently adjusting the incident signal phases. By maximizing the signal-to-noise-ratio (SNR) of the received signal, the bit-error-rate (BER) performance is improved. By changing the number of IRS elements, taking into account various channel scenarios and modulation schemes, we compare the BER performance of the proposed system with that of the conventional Alamouti coded CDMA-FBMC-OQAM system in simulation results. The findings demonstrate that the IRS-assisted Alamouti coded CDMA-FBMC-OQAM transmission is a feasible choice for future-generation wireless communication systems.

An in-depth assessment of the physical layer performance in the proposed B5G framework

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks.

Research on the effect of connected cable-type channel structure and flow field arrangement on the performance of SOFC

In order to address Solid oxide fuel cells(SOFC) the issues of uneven gas distribution and low fuel utilization rate in conventional straight-channel. In this research, a connected cable-type channel with enhanced connectivity and broader radiation coverage was developed. The research revealed that in a singular flow channel scenario, the distribution of oxygen on the electrode within the connected cable-type channel exhibited greater uniformity compared to the conventional straight-channel design. The gas utilization rate is increased by 2.38% compared with the conventional straight-channel, and the output power density is increased by 8.55%. In the flow field, it is observed that the gas distribution on the cathode side of connected cable-type channel is significantly superior to that of the conventional straight-channel. In the case of a parallel flow field, the output power of the connected cable channel increases by 8.39% compared to the conventional straight channel, while the serpentine channel shows an increase of 11.65% in output power. Then, the impact of the flow field combination on the flow field was investigated. It was discovered that the output power of the cell could reach up to 5158.17 W/m2 when the serpentine-parallel staggered flow field distribution structure was utilized. This output was 11.33% higher than that of the parallel flow field.

Numerical investigation of the scour around a diamond- and square-shaped pile in a narrow channel

In this paper, a numerical investigation is conducted to study the scour dynamics around vertical square- and diamond-shaped piles in clear-water and live-bed conditions. The focus is particularly on cases involving narrow channels, aiming to examine the differences compared to more extensively studied scenarios, such as wide channels with circular piles. This work employs a Large Eddy Simulation (LES) model to simulate the turbulent flows around the structures. The hydrodynamic model is based on the Navier–Stokes equations in σ-coordinate, and the bed evolution is simulated by solving the Exner-Polya equation. The governing equations are solved using a second-order unstructured finite-volume method. The model is initially validated through a comparison with experimental data and numerical results found in the literature. Subsequently, it is applied to conduct a comprehensive study, enhancing our understanding of the various conditions influencing the scour process. The results indicate that the shape of the piles has different effects on the scour depth under clear-water and live-bed conditions. In live-bed conditions, the simulations reveal a deeper scour around the square-shaped pile compared to the diamond-shaped pile at the equilibrium stage. Conversely, an opposite behavior is observed in clear-water conditions, with a deeper scour around the diamond pile. Moreover, this behavior is consistent across both narrow and wide channels. It is discovered that, in clear-water conditions, the evolution of the scour in a narrow channel reaches equilibrium faster than in a wide channel, leading to a deeper equilibrium scour hole in the wide channel scenario.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

Evaluation of the effectiveness of using FBMC/OQAM signals with OTFS preprocessing in 5G NR for high-mobility channel scenarios

Evaluation of the effectiveness of using FBMC/OQAM signals with OTFS preprocessing in 5G NR for high-mobility channel scenarios

A deep learning approach based on Richardson and Gauss–Seidel for massive MIMO detection

AbstractMassive multiple‐input multiple‐output (MIMO) systems can improve the spectrum utilization and the system capacity, but this also increases the computational complexity of the signal detection. The existing iterative algorithms can greatly reduce the computational complexity; however, the detection performance is limited. In order to achieve a better balance between the computational complexity and the detection performance, this article combines the model‐driven deep learning approached with Massive MIMO signal detection to construct RGNet (RIGS‐based deep learning Network). First, RIGS is proposed as a hybrid method of RI (Richardson) and GS (Gauss–Seidel). The RIGS algorithm combines these methods to achieve faster convergence. However, the performance of RIGS joint algorithm is limited to the spatially correlated channel scenarios. To improve robustness, we further extend RIGS, by adding learnable parameters in each iteration and introducing staircase activation functions to significantly improve detection performance. Simulation results show that the proposed RGNet has low computational complexity and a simple and fast training process. It can also achieve excellent detection performance in Rayleigh fading channel and spatially correlated channel.

Channel Scenario Extensions, Identifications, and Adaptive Modeling for 6G Wireless Communications

Channel Scenario Extensions, Identifications, and Adaptive Modeling for 6G Wireless Communications

Optimal channel strategy for an e-seller: Whether and when to introduce live streaming?

The introduction of a live-streaming channel presents numerous opportunities for e-sellers, effectively reducing consumer uncertainty and enhancing consumer valuation. Few studies have investigated whether and when an e-seller should introduce a live-streaming channel. To fill this gap, this paper considers two live-streaming channel strategies, where one is the e-seller and another is live streamer. This study aims to investigate the impact of live-streaming selling on the e-seller’s optimal decisions, our findings show that introducing the live-streaming channel benefits for the e-seller when the slotting fee is sufficiently low in the e-seller operated live-streaming channel, or when the live streamer’s sales ability is sufficiently high in the live streamer operated live-streaming channel. Then, we examine the pricing decisions in different channels of the e-seller considering live-streaming selling, it shows that the price in the live-streaming channel is not always lower, which is dependent on the live streamer’s sales ability. Moreover, both the e-seller and the live streamer consistently prefer the e-seller to operate the live-streaming channel when introducing the live-streaming channel within a specific range of slotting fee. Finally, the research was extended to a hybrid channel scenario, and the results reveal that the e-seller may not consistently benefit from such configurations.

Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04.

Variational autoencoder-enhanced deep neural network-based detection for MIMO systems

Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN’s ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods.

Joint Statistics of Partial Sums of Ordered i.n.d. Gamma Random Variables

From the perspective of wireless communication, as communication systems become more complex, order statistics have gained increasing importance, particularly in evaluating the performance of advanced technologies in fading channels. However, existing analytical methods are often too complex for practical use. In this research paper, we introduce innovative statistical findings concerning the sum of ordered gamma-distributed random variables. We examine various channel scenarios where these variables are independent but not-identically distributed. To demonstrate the practical applicability of our results, we provide a comprehensive closed-form expression for the statistics of the signal-to-interference-plus-noise ratio in a multiuser scheduling system. We also present numerical examples to illustrate the effectiveness of our approach. To ensure the accuracy of our analysis, we validate our analytical results through Monte Carlo simulations.

Characteristics of cellular structure of detonation waves propagating in annular channels

This study investigates the characteristics of stable and unstable cells and wavefronts of detonation waves propagating in annular channels with different inner radii and channel widths using two-dimensional Euler equations along with a two-step induction-exothermic reaction kinetics. The results reveal that the effect of annular channels on the detonation cell structure depends on both the inner radius and channel width. To quantify this effect, a parameter σ is introduced, representing the ratio of the inner and outer radii of the channel. We have discovered that for values of the parameter σ exceeding a critical value σs, the detonation wavefront demonstrates characteristics similar to those observed in a straight channel scenario. On the contrary, when σ is below σs, the wavefront becomes distorted, potentially leading to Mach reflection as σ decreases further to another critical value σm. Additionally, the interaction among expansion waves induced by the inner walls leads to an augmented induced length and the potential occurrence of localized decoupling of the detonation wave, particularly for unstable detonation waves. However, it is worth noting that the re-initiation of the detonation wave may be triggered by the formation of hotspots resulting from the interaction between transverse shock waves and the detonation wave. This study aims to characterize the propagation characteristics of detonation waves within annular channels, with the objective of providing valuable insights for the design and optimization of annular chamber configurations in systems involving detonation.

Deep Reinforcement Learning Empowers Wireless Powered Mobile Edge Computing: Towards Energy-Aware Online Offloading

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

5G and Beyond: Channel Classification Enhancement Using VIF-Driven Preprocessing and Machine Learning

The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).

OF-FSE: An Efficient Adaptive Equalization for QAM-Based UAV Modulation Systems

Quadrature amplitude modulation (QAM) is one of the essential components of unmanned 1 aerial vehicle (UAV) communications. However, the output signal accuracy of QAM deteriorates dramatically and even collapses in the case of UAVs in a harsh channel environment. This is due to the fractionally spaced equalization based on the multi-modulus blind equalization algorithm being implemented prior to carrier synchronization in QAM-based UAV modulation systems. The carrier frequency offset from the harsh channel signal thus contributes to the significantly degraded performance of MMA by suffering the fractionally spaced equalization. Therefore, in this paper, a novel offset feedback fractionally spaced equalization architecture for UAVs to eliminate the carrier frequency offset is first proposed. In this architecture, the carrier frequency offset allows estimated and incorporation into the input signal of fractionally spaced equalization to compensate for the offset. Moreover, a new multi-modulus decision-directed algorithm is presented for the novel architecture to improve the received signal accuracy of UAVs further. It enables adaptive optimization of the convergence process in accordance with the dynamic UAV communication environment employing the multi-modulus blind equalization algorithm and decision-directed blind equalization algorithm (MDD). Simulation results demonstrate the effectiveness of the OF-FSE framework in enabling the QAM-based UAV modulation systems operation in harsh channel scenarios. Moreover, the performance of the presented new MDD algorithm compared with baseline approaches is also confirmed.

Deep Communication: Exploring End-to-End Wireless with Convolutional Neural Network

In recent years, end-to-end wireless communication has gained significant attention in the field of wireless communication. In this paper, we propose a new approach to achieving end-to-end wireless communication using convolutional neural networks (CNNs) in the presence of Nakagami fading, Additive white Gaussian noise (AWGN), and multiple-input multiple-output (MIMO) fading channels. Further, we have applied the bursty noise to the AWGN channel. We first develop a CNN-based transmitter architecture that can efficiently encode information bits into signals, followed by a CNN-based receiver architecture that can accurately decode the received signals. Our proposed method leverages the strengths of CNNs in learning and extracting features from raw data and applies them to wireless communication. We then evaluate the performance of our proposed method by extensive sets of simulations in different AWGN, Nakagami fading, and MIMO fading channel scenarios. The simulation results show that the method proposed shows superior performance compared to existing state-of-the-art techniques at low Signal to Noise ratio(SNR) in terms of bit error rate (BER) and Binary cross-entropy loss. Our proposed method can be a promising solution for achieving end-to-end wireless communication in various practical applications.