Abdul Latif Sarker

Seismic data processing is crucial to identify earth’s subsurface layers. However, during seismic data acquisition, seismic data are often corrupted by random and erratic noise, which may degrade seismic data quality and lead to misinterpretation of earth layers. Random noise elimination has been addressed using various standard denoising techniques, and erratic noise poses a significant challenge due to its discontinuous high-amplitude peaks. Existing dictionary-based denoising methods, such as double sparsity dictionary learning (DSDL), etc., involve iterative processes in various stages that are computationally expensive. To address these limitations, we propose a convolutional dictionary learning (CDL) method using ℓ_2,1 norm to provide group sparsity and alternating direction method of multipliers (ADMM). The ℓ_2,1 norm combines local sparsity, internal patch self-similarity, and their correlation to external reference estimates to effectively preserve weak signals through group sparsity. CDL method effectively eliminates both random and erratic noise while preserving critical signal features and reducing computational complexity. Performance of CDL is evaluated in terms of evaluation metrics such as mean squared error (MSE), signal-to-noise ratio (SNR), and energy values. Proposed CDL method demonstrated better denoising with signal feature preservation and reduced computational time.

Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral distortions caused by water absorption and scattering. Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel autoencoder-based unmixing network incorporating parallel linear and nonlinear decoders to improve underwater target detection. Our method effectively reduces the effect of spectral distortions and addresses variability using a combined loss function integrating Kullback-Leibler divergence, mean squared error, and spectral angle distance. Experimental validation on real-world and simulated datasets demonstrates that our proposed SVHAE outperformed state-of-the-art methods by achieving superior AUC values. These advancements contribute to the progressing field of HUTD, making the way for robust solutions in marine exploration and detecting targets under the water.

Hybrid beamforming is a signal processing technique composed of multiple components that is of significant importance in reducing transceiver costs and improving spectral efficiency in the context of highly directive wireless communications. Conventional hybrid beamforming leads to considerable baseband computation and suboptimal amplitude factor due to the use of extensive antenna arrays. Hence, we propose a blockwise beam selection (BBS) algorithm to achieve a low-dimensional hybrid beamformer and minimize the computation of baseband processing in mmWave beamspace multiple-input multiple-output lens antenna array systems. In this paper, we first present a block-wise design of a transmitting analog beamformer. In order to achieve a low-dimensional hybrid beamformer, we then apply the proposed BBS algorithm that significantly reduces the dimensions of the baseband processor and enhances the system performance. Finally, the simulation results validate the superiority of the proposed BBS algorithm compared to its traditional non-blockwise beam selection counterparts.

A reconfigurable intelligent surface (RIS) is a kind of metasurface that provides an adaptable way of controlling the propagation of radio signals in wireless systems, allowing for improved coverage and system efficiency. Instead of the traditional RIS-assisted single-to-multiple reflection channel, we present a time-variant location-centric hybrid reflection channel (TL-HRC) model in this paper. The designed TL-HRC model can easily enable mobile blockages and establish a cooperative relationship between the static and mobile RIS in millimeter wave multiple-input and multiple-output systems. Based on the designed TL-HRC model, we establish a joint active and passive beamforming problem for the weighted sum rate maximization and propose an alternative optimization with an RIS element selection (AO-RES) algorithm to tackle the challenging high dimensional coupled parameters. Through simulation experiments, the effectiveness of the proposed TL-HRC-based AO-RES algorithm is validated in terms of the weighted sum rate with the case of perfect channel state information.

Submitted to the IEEE Communications Letters

The revised version has been submitted to the IEEE Transactions on Vehicular Technology. A reconfigurable intelligent surface (RIS) is a kind of metasurface that provides an adaptable way of controlling the propagation of radio signals in wireless systems, allowing for improved coverage and system efficiency. Instead of the traditional RIS-assisted single-to-multiple reflection channel, we present a time-variant location-based hybrid reflection channel(TL-HRC) model in this paper. The designed TL-HRC model can easily tune a cooperative situation between the mobile blockage and RIS in millimeter wave massive multiple-input and multiple-output systems and satisfactorily scale a high received power gain. Based on the designed TL-HRC model, we establish a joint active and passive beamforming problem for the weighted sum rate maximization and propose an alternative optimization with a RIS element selection (AO-RES) algorithm to tackle the challenging high dimensional coupled parameters. Through simulation experiments the effectiveness of the proposed TL-HRC-based AO-RES algorithm is validated in terms of the weighted sum rate with the case of perfect channel state information.

Reconfigurable intelligent surfaces (RIS) have recently been investigated for intelligently reforming the blockage or mobility-aware wireless propagation environment as well as improving spectral efficiency and localization error performance. A blockage generally impacts the visual path of the millimeter wave (mmWave) channel and increases its signaling overhead. Thus, the system performance may suffer due to interruptions caused by static or mobile blockers, such as buildings, trees, vehicles or people. In this paper, we consider an RIS-assisted mmWave multiple-input and multiple-output (MIMO) channel model to overcome the blockage awareness problem. In order to solve the subarray rate maximization problem, we propose a conjugate gradient-based low-cost hybrid beamforming algorithm. Next, we consider a channel covariance splitting method and propose a double-step iterative algorithm to perform the localization error-probability of the user autonomous vehicle. Finally, the simulation results show and verify the proposed algorithm in terms of the RIS-assisted equivalent channel for the mmWave vehicle-to-vehicle MIMO systems.