Time-varying Channels Research Articles

Adaptive Tracking Method for Time-Varying Underwater Acoustic Channel Based on Dynamic Gaussian Window

Adaptive Tracking Method for Time-Varying Underwater Acoustic Channel Based on Dynamic Gaussian Window

Efficient Pruned-DFT FBMC-OQAM for Performance Enhancement of MIMO Channel Equalization Techniques

A The next generation of wireless systems, like 5G/6G, ought to accommodate a wide range of potential use cases like massive real-time and machine-type communications. Accordingly, such systems need to provide high throughput, low latency, and a low peak to average power ratio (PAPR). It is not feasible to fulfil each of these needs with a single 5G/6G technique. On one hand, MIMO technique can potentially improve spectral efficiency and hence support more date rates. On the other hand, the utilization of multi-carriers’ techniques like FBMC/OQAM, or offset quadrature amplitude modulation, offers a compelling substitute for the traditional cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM) because of its excellent spectral efficiency and extremely low out-of-band radiation. However, the FBMC/OQAM orthogonality constraint is loosened, confined to the real field alone, resulting in intrinsic interference. This interference causes incompatibilities between FBMC/OQAM and certain MIMO schemes. Also, equalizing the time-varying MIMO channel is even more difficult when the complex valued symbol—which includes both real and imaginary FBMC/OQAM task, primarily due to the general non-flatness of the subchannels. From this perspective, we first suggest in this paper an efficient pruned-DFT-based implementation of MIMO to restoring the complex orthogonality and also reduce the complexity of the whole system. Then, we optimize MIMO channel equalization by efficiently separating and equalizing multiple parallel channels using various equalization techniques. The numerical results, presented as MSE versus subcarrier index, show that Pruned DFT technique consistently achieves lower MSE values compared to the standard MIMO FBMC-OQAM across various configurations, which indicates better error performance and its robustness in dynamic channel conditions.

Clipper: Online Joint Client Sampling and Power Allocation for Wireless Federated Learning

Communication overhead is a main bottleneck in federated learning (FL) especially in the wireless environment due to the limited data rate and unstable radio channels. The communication challenge necessitates holistic selection of participating clients that accounts for both the computation needs and communication cost, as well as judicious allocation of the limited transmission resource. Meanwhile, the random unpredictable nature of both the training data samples and the communication channels requires an online optimization approach that adapts to the changing system state over time. In this work, we consider a general framework of online joint client sampling and power allocation for wireless FL under time-varying communication channels. We formulate it as a stochastic network optimization problem that admits a Lyapunov-typed solution approach. This leads to per-training-round subproblems with a special bi-convex structure, which we leverage to propose globally optimal solutions, culminating in a meta algorithm that provides strong performance guarantees. We further study three specific FL problems covering multiple scenarios, namely with IID or non-IID data, whether robustness against data drift is required, and with unbiased or biased client sampling. We derive detailed algorithms for each of these problems. Simulation with standard classification tasks demonstrate that the proposed communication-aware algorithms outperform their counterparts under a wide range of learning and communication scenarios.

A Novel Embedded Pilot-Aided OCDM System Over Time-Varying Channels

A Novel Embedded Pilot-Aided OCDM System Over Time-Varying Channels

A Multidimensional Feature Metric-Based Cluster-Tracking Algorithm and Its Application to Time-Varying Millimeter-Wave Channels

A Multidimensional Feature Metric-Based Cluster-Tracking Algorithm and Its Application to Time-Varying Millimeter-Wave Channels

A D* orthogonal matching pursuit algorithm for time-varying channel estimation.

Orthogonal matching pursuit (OMP) combined with the A* search algorithm (A*OMP) exhibits robust reconstruction capabilities for synthesizing sparse data and signals, achieving relatively low reconstruction errors and a higher exact recovery ability than conventional OMP. However, A*OMP is only suitable for static channel estimation and cannot be applied to dynamic scenarios. This is because the channel delays for several consecutive orthogonal frequency-division multiplexing blocks per frame are similar and the path gains exhibit temporal correlation. This paper introduces a dynamic OMP approach (D*OMP) that employs a heuristic function of A*OMP and a unique reverse process, enabling sparse solutions to be identified in unknown and changing environments. The proposed method is highly practical for joint channel estimation across multiple blocks. Simulation and sea trial results indicate that D*OMP not only possesses superior channel recovery accuracy, but also has a more efficient channel reconstruction process, outperforming both A*OMP and conventional OMP.

On the Pilot-Aided Channel Estimation for Windowed OTFS With Data Interference in Rapidly Time-Varying Channels

On the Pilot-Aided Channel Estimation for Windowed OTFS With Data Interference in Rapidly Time-Varying Channels

A Three-Dimensional Time-Varying Channel Model for THz UAV-Based Dual-Mobility Channels.

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems.

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.

A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications.

Recursive least squares (RLS)-based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.

A Novel Domain Transformation Based on Iterative Rotation for Linear Time-Variant Fading Channels

A Novel Domain Transformation Based on Iterative Rotation for Linear Time-Variant Fading Channels

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Adaptive Data-Aided Time-Varying Channel Tracking for Massive MIMO Systems

Adaptive Data-Aided Time-Varying Channel Tracking for Massive MIMO Systems

Diversity Analysis of OTFS Over Block Time-Varying Channels

Diversity Analysis of OTFS Over Block Time-Varying Channels

Performance Evaluation of Carrier-Frequency Offset as a Radiometric Fingerprint in Time-Varying Channels.

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter-receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.

Bit Error Rate Performance Improvement for Orthogonal Time Frequency Space Modulation with a Selective Decode-and-Forward Cooperative Communication Scenario in an Internet of Vehicles System.

Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the scope of Internet of Things (IoT). There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems a bit low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. In this study, to the best of the authors' knowledge, it is addressed for the first time in the literature that better performance is achieved for the OTFS waveform transmission in a selective decode-and-forward (SDF) cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source, a traffic sign/relay and a smart vehicle/destination moving at a constant speed, an end-to-end BER expression is derived. SNR-BER analysis is performed with this SDF-OTFS scheme and it is shown that a superior BER performance is achieved compared to the traditional point-to-point single-input single-output (SISO)-OTFS structure.

Sensor Transmission Scheduling Over a Time-Varying Channel

Sensor Transmission Scheduling Over a Time-Varying Channel

Experimental Assessment of Spectrum Sensing Under Time-Varying Channels for Cognitive Radio

Experimental Assessment of Spectrum Sensing Under Time-Varying Channels for Cognitive Radio

A Time-varying Equivalent Circuit Modeling and Measuring Approach for Intracardiac Communication in Leadless Pacemakers.

Intracardiac wireless communication is crucial for the development of multi-chamber leadless cardiac pacemakers (LCP). However, the time-varying characteristics of intracardiac channel pose major challenges. As such, mastering the dynamic conduction properties of the intracardiac channel and modeling the equivalent time-varying channel are imperative for realizing LCP multi-chamber pacing. In this paper, we present a limiting volume variational approach based on the electrical properties of cardiac tissues and trends in chamber volume variation. This approach was used to establish a quasi-static and a continuous time-varying equivalent circuit model of an intracardiac channel. An equivalence analysis was conducted on the model, and a discrete time-varying equivalent circuit phantom grounded on the cardiac cycle was subsequently established. Moreover, an ex vivo cardiac experimental platform was developed for verification. Results indicate that in the frequency domain, the congruence between phantom and ex vivo experimental outcomes is as high as 94.3%, affirming the reliability of the equivalent circuit model. In the time domain, the correlation is up to 75.3%, corroborating its effectiveness. The proposed time-varying equivalent circuit model exhibits stable and standardized dynamic attributes, serving as a powerful tool for addressing time-varying challenges and simplifying in vivo or ex vivo experiments.