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Researcher profile

Maryam Sabbaghian

Maryam Sabbaghian contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
eess.SPMachine Learningmath.OCOther Computer Science

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Published work

3 published item(s)

preprint2026arXiv

Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO

Intelligent Reflecting Surfaces (IRSs) are a promising technology for enhancing the spectral and energy efficiency of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In these systems, accurate channel estimation remains challenging due to the passive nature of IRS elements and the high pilot overhead in large-scale deployments. This paper presents a deep learning-based Multi-Block Attention (MBA) framework for efficient cascaded channel estimation in IRS-assisted mmWave MIMO systems that utilize orthogonal frequency division multiplexing (OFDM). First, we show the optimality of the discrete Fourier transform (DFT) and Hadamard matrices as phase configurations for least squares (LS) estimation. To reduce training overhead, we selectively deactivate IRS elements and compensate for induced feature loss using a two-stage architecture: (i) a Convolutional Attention Network (CAN) for spatial correlation recovery and (ii) a Complex Multi-Convolutional Network (CMN) for noise suppression. The MBA architecture mitigates error propagation through attention-guided feature refinement and denoising. Simulation results indicate that the MBA method reduces pilot overhead by up to 87% compared to the LS estimator. Additionally, at signal-to-noise ratios of 10 dB, our proposed method achieves approximately 51% lower normalized mean squared error (NMSE) than leading methods. It also maintains low computational complexity and adapts effectively to various propagation environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self Interference Management in In-Band Full-Duplex Systems

The evolution of wireless systems has led to a continuous increase in the demand for radio frequency spectrum. To address this issue, a technology that has received a lot of attention is In-Band Full-Duplex (IBFD). The interest in IBFD systems stems from its capability to simultaneously transmit and receive data in the same frequency. Cancelling the self interference (SI) from the transmitter to the collocated receiver plays a pivotal role in the performance of the system. There are two types of SI cancellation (SIC) approaches, passive and active. In this research, the focus is on active cancellation and, in particular, SIC in the digital domain. Among the direct and backscattered SI, the former has been studied for a long time; therefore, the backscatter is considered in this research and two SIC approaches are analyzed. The first achieves SIC through beamforming. This requires knowing the angle of the received SI to put the beam null-space in this direction. The second method removes SI by employing an Artificial Neural Networks (ANNs). Using an ANN, there is no need to know the direction of the SI. The neural network is trained with pilots which results in the network being able to separate the desired signal from the SI at the receiver. Bayesian Neural Networks show the importance of the weights and assign a parameter that facilitates ignoring the less significant ones. Through comparative simulations we demonstrate that the ANN-based SIC achieves equivalent bit error rate performance as two beamforming methods.

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preprint2020arXiv

No Cross-Validation Required: An Analytical Framework for Regularized Mixed-Integer Problems (Extended Version)

This paper develops a method to obtain the optimal value for the regularization coefficient in a general mixed-integer problem (MIP). This approach eliminates the cross-validation performed in the existing penalty techniques to obtain a proper value for the regularization coefficient. We obtain this goal by proposing an alternating method to solve MIPs. First, via regularization, we convert the MIP into a more mathematically tractable form. Then, we develop an iterative algorithm to update the solution along with the regularization (penalty) coefficient. We show that our update procedure guarantees the convergence of the algorithm. Moreover, assuming the objective function is continuously differentiable, we derive the convergence rate, a lower bound on the value of regularization coefficient, and an upper bound on the number of iterations required for the convergence. We use a radio access technology (RAT) selection problem in a heterogeneous network to benchmark the performance of our method. Simulation results demonstrate near-optimality of the solution and consistency of the convergence behavior with obtained theoretical bounds.

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