Channel Impairments Research Articles

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

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.

Unraveling the dynamics of firing patterns for neurons with impairment of sodium channels.

Various factors such as mechanical trauma, chemical trauma, local ischemia, and inflammation can impair voltage-gated sodium channels (Nav) in neurons. These impairments lead to a distinctive leftward shift in the activation and inactivation curves of voltage-gated sodium channels. The resulting sodium channel impairments in neurons are known to affect firing patterns, which play a significant role in neuronal activities within the nervous system. However, the underlying dynamic mechanism for the emergence of these firing patterns remains unclear. In this study, we systematically investigated the effects of sodium channel dysfunction on individual neuronal dynamics and firing patterns. By employing codimension-1 bifurcation analysis, we revealed the underlying dynamical mechanism responsible for the generation of different firing patterns. Additionally, through codimension-2 bifurcation analysis, we theoretically determined the distribution of firing patterns on different parameter planes. Our results indicate that the firing patterns of impaired neurons are regulated by multiple parameters, with firing pattern transitions caused by the degree of sodium channel impairment being more diverse than those caused by the ratio of impaired sodium channel and current. Furthermore, we observed that the firing pattern of tonic firing is more likely to be the norm in impaired sodium channel neurons, providing valuable insights into the signaling of impaired neurons. Overall, our findings highlight the intricate relationships among sodium channel impairments, neuronal dynamics, and firing patterns, shedding light on the impact of disruptions in ion concentration gradients on neuronal function.

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.

Optimizing High-Speed Serial Links for Multicore Processors and Network Interfaces

In modern computing environments, high-speed serial links have become a critical component for ensuring efficient data transfer between multicore processors and network interfaces. These links, characterized by their ability to transmit data at very high rates over single or multiple lanes, are essential for meeting the increasing bandwidth demands of contemporary applications. The optimization of these serial links is crucial for maintaining performance, reliability, and energy efficiency in systems that leverage multicore processors and advanced network interfaces. This paper explores the key strategies for optimizing high-speed serial links in the context of multicore processors and network interfaces. We begin by examining the architectural considerations and design principles that influence the performance of serial links, including signal integrity, power consumption, and thermal management. We also discuss the impact of link speed and data encoding techniques on overall system efficiency. One of the central challenges in optimizing high-speed serial links is managing signal integrity across different operating conditions. Techniques such as equalization and adaptive filtering are essential for mitigating the effects of signal degradation due to channel impairments and crosstalk. The paper provides a detailed analysis of these techniques, including their implementation in both hardware and software.

Design and investigation of symmetric bidirectional VCSEL laser incorporated extensive reach OWC system using considering channel impairments for beyond 5G application

Design and investigation of symmetric bidirectional VCSEL laser incorporated extensive reach OWC system using considering channel impairments for beyond 5G application

Transceiver impairment robust and joint monitoring of PDL, DGD, and CD using frequency domain pilot tones for digital subcarrier multiplexing systems

Optical performance monitoring is vital for enabling dynamically reconfigurable optical networks. This paper introduces a monitoring scheme that is robust to transceiver impairments for coherent digital subcarrier multiplexing (DSCM) systems that simultaneously monitors polarization-dependent loss (PDL), differential group delay (DGD), and chromatic dispersion (CD). In the proposed scheme, a pair of frequency domain pilot tones (FPTs) are inserted into the X and Y polarizations of the transmitted dual-polarization signal. Both the signal and the FPTs endure identical channel impairments during fiber transmission. At the receiver side, the transmitted FPTs are extracted to monitor the channel impairments. We begin by establishing a simplified system model using a single frequency tone to analyze the effects of system impairments on the frequency domain pilots. Based on this model, CD, PDL, and DGD are jointly and accurately estimated. In addition, the influences of temporal, amplitude, and phase mismatches between in-phase (I) and quadrature (Q) components of transceivers on channel impairment monitoring are completely eliminated. This achieves a channel impairment monitoring scheme that is robust to transceiver impairments. Experimental verification shows that the proposed scheme can jointly and accurately estimate a wide range of CD, PDL, and DGD. Additionally, the proposed monitoring scheme demonstrates strong robustness against transceiver impairments, rapid polarization fluctuations, and amplified spontaneous emission (ASE) noise.

Advanced Neural Network-Based Equalization in Intensity-Modulated Direct-Detection Optical Systems: Current Status and Future Trends

Intensity-modulated direct-detection (IM/DD) optical systems are most widely employed in short-reach optical interconnects due to their simple structure and cost-effectiveness. However, IM/DD systems face mixed linear and nonlinear channel impairments, mainly induced by the combination of square-law detection and chromatic dispersion, as well as the utilization of low-cost non-ideal transceivers. To solve this issue, recent years have witnessed a growing trend of introducing machine learning technologies such as neural networks (NNs) into IM/DD systems for channel equalization. NNs usually present better system performance than traditional approaches, and various types of NNs have been investigated. Despite the excellent system performance, the associated high computational complexity is a major drawback that hinders the practical application of NN-based equalizers. This paper focuses on the performance and complexity trade-off of NNs employed in IM/DD systems, presenting a systematic review of the current status of NN-based equalizers as well as a number of effective complexity reduction approaches. The future trends of leveraging advanced NN in IM/DD links are also discussed.

Modified receiver architecture in software-defined radio for real-time modulation classification

Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications.

Dynamic multipath routing for energy‐efficient and reliable communication in 6G networks with MIMO

AbstractIn the era of 6G networks, Multiple Input Multiple Output (MIMO) technology offers unprecedented opportunities for high‐throughput and low‐latency communication. Existing communication frameworks, however, have difficulty optimizing both energy efficiency and reliability at the same time. In most cases, conventional routing protocols fail to meet the needs of MIMO systems, making them inefficient and prone to reliability problems due to their inability to dynamically adapt to different network conditions. This research addresses the intricate interplay between energy efficiency and reliability within the context of 6G networks with MIMO. The motivation for this research arises from the imperative to unlock the full potential of 6G networks with MIMO for achieving energy‐efficient and reliable communication. With the advancement of communication technology, seamless connectivity, minimal energy consumption, and robust reliability become increasingly critical. Currently, solutions cannot adapt dynamically to the diverse and dynamic conditions of a 6G environment. Through this research, we aim to bridge this gap, enhancing 6G network performance and sustainability with unprecedented gains in energy efficiency and reliability. We have developed the Dynamic Multipath Routing (DMR) algorithm by harnessing the advanced features of MIMO technology. The DMR algorithm strategically chooses paths to minimize the effects of fading, interference, and channel impairments, creating a resilient communication network. This improvement is essential for meeting the demanding connectivity needs of various 6G applications, covering ultra‐reliable low‐latency communication and massive machine‐type communication.

Optimized equalization based on MAP detection for C-band bandwidth-limited IM/DD systems.

Entangled dynamic and deterministic inter-symbol interferences (ISIs) induced by complicated channel impairments, limit the transmission capacity of intensity modulation and direct detection (IM/DD) systems. This Letter proposes a colored noise-suppressed channel shortening filter (CNS-CSF)-enabled maximum a posteriori (MAP) estimation (CNS-CSF-MAP) scheme to disentangle and mitigate deterministic and dynamic ISIs, where the CNS-CSF is deployed to perform the optimized dynamic ISI equalization with equalization-enhanced noise suppression, and the subsequent MAP algorithm is used to eliminate the residual deterministic ISI. The performance of the CNS-CSF-MAP scheme is evaluated and demonstrated in a C-band 61-Gb/s 100-km optical on-off keying (OOK) IM/DD system. The experimental results show that the proposed CNS-CSF-MAP scheme reaches the 20% and 7% forward error correction (FEC) thresholds at received optical powers (ROPs) of -6.6 dBm and -4 dBm, achieving 0.5- and 1.5-dB gains over a conventional post-filter-enabled MAP (PF-MAP) scheme.

Nonlinear impairment compensation in multi-channel communication systems based on correlated digital backpropagation with separation of walk-off effect

Digital backpropagation (DBP) is a commonly used method to compensate for the nonlinear impairments of optical fiber channels in the electrical domain, offering a new approach to enhancing the data capacity without affecting the system performance. However, due to the repeated Fourier transform and inverse transform of the split-step Fourier method (SSFM), the complexity of DBP algorithm has increased significantly. In this paper, a correlated backpropagation algorithm with the separation of the walk-off effect is developed, which improves the computational step and significantly reduces the complexity of the algorithm. Simulation results demonstrate that in a 6-channel PDM-16QAM system, the proposed hybrid algorithm reduces the computational complexity by 73.96 % compared to the traditional DBP algorithm for an 800 km transmission.

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands.

As fixed wireless access (FWA) is still envisioned as a reasonable way to achieve communications links, foliage attenuation becomes an important wireless channel impairment in the millimeter-wave bandwidth. Foliage is modeled in the radiative transfer equation as a medium of random scatterers. However, other phenomena in the wireless channel may also occur. In this work, vegetation attenuation measurements are presented for a single tree alley for 26-32 GHz. The results show that vegetation loss increases significantly after the second tree in the alley. Measurement-based foliage losses are compared with model-based, and new tuning parameters are proposed for models.

Symbol error rate minimization using deep learning approaches for short-reach optical communication networks

Short-reach optical communication networks have various applications in areas where high-speed connectivity is needed, for example, inter- and intra-data center links, optical access networks, and indoor and in-building communication systems. Machine learning (ML) approaches provide a key solution for numerous challenging situations due to their robust decision-making, problem-solving, and pattern-recognition abilities. In this work, our focus is on utilizing deep learning models to minimize symbol error rates in short-reach optical communication setups. Various channel impairments, such as nonlinearity, chromatic dispersion (CD), and attenuation, are accurately modeled. Initially, we address the challenge of modeling a nonlinear channel. Consequently, we harness a deep learning model called autoencoders (AEs) to facilitate communication over nonlinear channels. Furthermore, we investigate how the inclusion of a nonlinear channel within an autoencoder influences the received constellation as the optical fiber length increases. Another facet of our work involves the deployment of a deep neural network-based receiver utilizing a channel influenced by chromatic dispersion. By gradually extending the optical length, we explore its impact on the received constellation and, consequently, the symbol error rate. Finally, we incorporate the split-step Fourier method (SSFM) to emulate the combined effects of nonlinearities, chromatic dispersion, and attenuation in the optical channel. This is accomplished through a neural network-based receiver. The outcome of this work is an evaluation and reduction of the symbol error rate as the length of the optical fiber is varied.

Machine Learning Algorithms for Performance Enhancement in DCO-OFDM-Based LiFi Systems

This study describes how machine learning methods can be used to enhance the performance of LiFi (light fidelity) systems that are based on DCO-OFDM (direct current bias optical orthogonal frequency division multiplexing). LiFi, a rapidly developing technology that uses light for wireless communication, has to contend with issues like channel impairments and signal interference. This work investigates several machine learning strategies to address these problems and improve the DCO-OFDM LiFi systems' overall performance. It is determined how well various machine learning techniques perform in terms of improving system parameters, decreasing latency, and raising throughput by analyzing real-world data and simulation studies. Various sorts of symmetrical recurrence division multiplexing (OFDM, for example, DC one-sided optical OFDM (DCO-OFDM), are utilized in light devotion (LiFi). A critical DC predisposition in DCO-OFDM brings about optical power failure, while a minor inclination increment cutting commotion. In this manner, it's essential to decide the appropriate DC predisposition level for DCO-OFDM. This examination finds the ideal DC-predisposition an incentive for DCO-OFDM based LiFi frameworks utilizing machine learning (ML) algorithms. To do this, a MATLAB device is utilized to create a dataset for DCO-OFDM. Thusly, Python writing computer programs is utilized to apply machine learning methods. ML is used to recognize the basic DCO-OFDM qualities that influence the ideal DC predisposition. Here, it is exhibited that the base, greatest, and standard deviation of the bipolar OFDM signal, as well as the size of the heavenly body, decide the ideal DC inclination.

Performance evaluation of energy harvesting-enabled NOMA under imperfect [formula omitted] shadowed fading channel information and hardware impairment

Energy harvesting (EH)-enabled nonorthogonal multiple access (NOMA) is one of wireless communication technologies which has superiority of energy and spectral efficiencies. Nevertheless, EH-enabled NOMA is affected by multifarious practical factors including EH nonlinearity, channel impairments (fading, shadowing, path loss), and more especially, imperfect channel information and hardware impairment. Consequently, the paper evaluates swiftly its performance accounting for these factors by proposing explicit computational expressions for system throughput, energy efficiency, and outage probability. The findings reveal that system performance is significantly mitigated by these factors. Notwithstanding, it is improved, optimized, complete outage-avoided by selecting appropriate parameters. Furthermore, the findings indicate the advantage of NOMA to the conventional orthogonal multiple access.

Efficient VLSI Implementation of Hybrid LDPC-STBC Codes for Enhanced Satellite Communication Systems

Satellite communication systems play a pivotal role in facilitating global connectivity, necessitating the development of robust error correction codes to ensure reliable data transmission. In contemporary communication systems, the demand for higher data rates and improved spectral efficiency has led to the integration of advanced error correction techniques. However, existing systems often face challenges in striking a balance between complexity, power consumption, and performance. This work addresses the limitations of current systems by proposing a hybrid approach that combines the advantages of LDPC (Low-Density Parity-Check) codes and STBC (Space-Time Block Coding) techniques. The hybrid LDPC-STBC codes aim to enhance the error correction capabilities of satellite communication systems, ensuring robust data transmission in the presence of channel impairments. Despite the advancements in error correction coding, current systems encounter drawbacks such as increased computational complexity, higher power consumption, and potential performance degradation under adverse channel conditions. The proposed hybrid LDPC-STBC codes mitigate these issues by leveraging the strengths of both coding schemes, achieving a synergistic balance between error correction performance and computational efficiency.

Modified DDFTN Algorithm for Band-Limited Short-Reach Optical Interconnects

Inter symbol interference (ISI) degrades the performance of intensity modulation and direct detection (IM/DD) transmission systems due to various transceiver and channel impairments. To overcome the ISI of IM/DD transmission systems, the direct detection faster than Nyquist (DDFTN) algorithm has been widely used. However, when a system is subjected to severe ISI, the performance of the conventional DDFTN algorithm is suboptimal. To further improve the performance of IM/DD transmission systems, a modified DDFTN algorithm is proposed. We add least squares (LS) channel estimation after the postfilter. The coefficients of maximum likelihood sequence estimation (MLSE) are close to the initial channel response with LS channel estimation. The performance of the conventional and modified DDFTN algorithms is compared for 112, 140, and 160 Gbit/s PAM4 signals over 2 km, 1 km, and 500 m standard single-mode fibers (SSMFs) in a 3 dB bandwidth, 19 GHz IM/DD system in the C band. For 140 Gbit/s signal transmission over 1 km, the modified DDFTN algorithm achieves the optimal receiver sensitivity of -1.5 dBm at a 7% forward error correction (FEC) threshold of 3.8E-3, which is better than the receiver sensitivity of -0.5 dBm achieved with the conventional DDFTN algorithm. Furthermore, for 160 Gbit/s signal transmission over 500 m, the modified DDFTN algorithm achieves the optimal receiver sensitivity of 1 dBm at 3.8E-3, which is also a superior result compared to the conventional DDFTN algorithm.

Joint equalization of frequency offset and phase noise using two-stage cascaded extended Kalman filter for discrete spectrum 16/64APSK NFDM systems.

For the discrete spectrum nonlinear frequency division multiplexing (DS-NFDM) 16/64 amplitude phase shift keying (APSK) system, the inevitable laser impairments including frequency offset (FO) and carrier phase noise (CPN) would cause different rotations of the received signal constellations. In addition, the combined effect of FO and amplifier spontaneous emission (ASE) noise induces the eigenvalue shift, accordingly the residual channel impairment (RCI) is inevitably yielded. To address the above problems, we deduce the joint impairment model of FO, CPN and RCI, and then propose a joint equalization scheme using two-stage cascaded extended Kalman filter (TSC-EKF) for these impairments. It performs frequency offset compensation in the first stage, subsequently carries out joint equalization of CPN and RCI in the second stage. Meanwhile, the minimum Euclidean distance and phase difference between the received symbols and the ideal 16/64APSK constellations are ingeniously fused to calculate the innovations of TSC-EKF. The effectiveness has been verified by 2 GBaud DS-NFDM 16/64 APSK simulations and DS-NFDM 16APSK transmission experiments. The results demonstrate that when performing the joint equalization of FO, CPN and RCI, the maximum FOE range of TSC-EKF scheme achieves 1.2 and 9.6 times as that of nonlinear frequency domain (NFD) scheme and fast Fourier transform -Like (FFT-Like) scheme, respectively. Furthermore, its maximum LW tolerance reaches 3.3 times as that of the M-th power scheme. Importantly, the complexity of TSC-EKF is 63.4% as that of NFD scheme and on an order of O(N).