arXiv subjects

Zavareh Bozorgasl

Publications and source records attributed to Zavareh Bozorgasl.

8 recordsLinked to original sources

arXiv2026

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

Over-the-air federated learning (OTA-FL) reduces uplink latency by aggregating client updates directly over the wireless multiple-access channel. Coherent analog aggregation realizes this idea by aligning the phases and amplitudes of simultaneously transmitted waveforms, which typically requires synchronization, instantaneous channel-state information (CSI), phase compensation, and power control. Noncoherent energy detection removes the need for phase-coherent combining, but a single energy measurement is nonnegative and, therefore, cannot represent signed model updates. This paper introduces resource-element energy difference (REED), a noncoherent physical-layer primitive for continuous signed aggregation. REED maps the positive and negative parts of each real-valued update to transmit energies on paired orthogonal resource elements and estimates the signed sum by subtracting the corresponding received energies. The construction uses slow-timescale calibration of average channel powers, but does not require instantaneous transmitter- or receiver-side CSI or channel inversion. For independent Rayleigh fading, we derive exact first- and second-moment expressions for single-shot REED and for a chip-diverse extension that spreads each coordinate over multiple independently faded paired chips. The resulting variance laws separate fading-induced self-noise, signal-noise interaction, and receiver-noise fluctuation, giving an explicit diversity-resource tradeoff. More->The rest of abstract is in the paper.

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arXiv2026

SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation

We propose SCENE (Self-Centering Noncoherent Estimator), a pilot-free and phase-invariant aggregation primitive for over-the-air federated distillation (OTA-FD). Each device maps its soft-label (class-probability) vector to nonnegative transmit energies under constant per-round power and constant-envelope signaling (PAPR near 1). At the server, a self-centering energy estimator removes the noise-energy offset and yields an unbiased estimate of the weighted soft-label average, with variance decaying on the order of 1/(SM) in the number of receive antennas M and repetition factor S. We also develop a pilot-free ratio-normalized variant that cancels unknown large-scale gains, provide a convergence bound consistent with coherent OTA-FD analyses, and present an overhead-based crossover comparison. SCENE targets short-coherence and hardware-constrained regimes, where avoiding per-round CSI is essential: it trades a modest noncoherent variance constant for zero uplink pilots, unbiased aggregation, and hardware-friendly transmission, and can outperform coherent designs when pilot overhead is non-negligible.

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arXiv2024

Communication-Efficient Federated Learning via Clipped Uniform Quantization

This paper presents a novel approach to enhance communication efficiency in federated learning through clipped uniform quantization. By leveraging optimal clipping thresholds and client-specific adaptive quantization schemes, the proposed method significantly reduces bandwidth and memory requirements for model weight transmission between clients and the server while maintaining competitive accuracy. We investigate the effects of symmetric clipping and uniform quantization on model performance, emphasizing the role of stochastic quantization in mitigating artifacts and improving robustness. Extensive simulations demonstrate that the method achieves near-full-precision performance with substantial communication savings. Moreover, the proposed approach facilitates efficient weight averaging based on the inverse of the mean squared quantization errors, effectively balancing the trade-off between communication efficiency and model accuracy. Moreover, in contrast to federated averaging, this design obviates the need to disclose client-specific data volumes to the server, thereby enhancing client privacy. Comparative analysis with conventional quantization methods further confirms the efficacy of the proposed scheme.

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arXiv2024

Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification

Hyperspectral image classification is a crucial but challenging task due to the high dimensionality and complex spatial-spectral correlations inherent in hyperspectral data. This paper employs Wavelet-based Kolmogorov-Arnold Network (wav-kan) architecture tailored for efficient modeling of these intricate dependencies. Inspired by the Kolmogorov-Arnold representation theorem, Wav-KAN incorporates wavelet functions as learnable activation functions, enabling non-linear mapping of the input spectral signatures. The wavelet-based activation allows Wav-KAN to effectively capture multi-scale spatial and spectral patterns through dilations and translations. Experimental evaluation on three benchmark hyperspectral datasets (Salinas, Pavia, Indian Pines) demonstrates the superior performance of Wav-KAN compared to traditional multilayer perceptrons (MLPs) and the recently proposed Spline-based KAN (Spline-KAN) model. In this work we are: (1) conducting more experiments on additional hyperspectral datasets (Pavia University, WHU-Hi, and Urban Hyperspectral Image) to further validate the generalizability of Wav-KAN; (2) developing a multiresolution Wav-KAN architecture to capture scale-invariant features; (3) analyzing the effect of dimensional reduction techniques on classification performance; (4) exploring optimization methods for tuning the hyperparameters of KAN models; and (5) comparing Wav-KAN with other state-of-the-art models in hyperspectral image classification.

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arXiv2024

An Innovative Networks in Federated Learning

This paper presents the development and application of Wavelet Kolmogorov-Arnold Networks (Wav-KAN) in federated learning. We implemented Wav-KAN \cite{wav-kan} in the clients. Indeed, we have considered both continuous wavelet transform (CWT) and also discrete wavelet transform (DWT) to enable multiresolution capabaility which helps in heteregeneous data distribution across clients. Extensive experiments were conducted on different datasets, demonstrating Wav-KAN's superior performance in terms of interpretability, computational speed, training and test accuracy. Our federated learning algorithm integrates wavelet-based activation functions, parameterized by weight, scale, and translation, to enhance local and global model performance. Results show significant improvements in computational efficiency, robustness, and accuracy, highlighting the effectiveness of wavelet selection in scalable neural network design.

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arXiv2024

Wav-KAN: Wavelet Kolmogorov-Arnold Networks

In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN face challenges related to interpretability, training speed, robustness, computational efficiency, and performance. Wav-KAN addresses these limitations by incorporating wavelet functions into the Kolmogorov-Arnold network structure, enabling the network to capture both high-frequency and low-frequency components of the input data efficiently. Wavelet-based approximations employ orthogonal or semi-orthogonal basis and maintain a balance between accurately representing the underlying data structure and avoiding overfitting to the noise. While continuous wavelet transform (CWT) has a lot of potentials, we also employed discrete wavelet transform (DWT) for multiresolution analysis, which obviated the need for recalculation of the previous steps in finding the details. Analogous to how water conforms to the shape of its container, Wav-KAN adapts to the data structure, resulting in enhanced accuracy, faster training speeds, and increased robustness compared to Spl-KAN and MLPs. Our results highlight the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields. This work sets the stage for further exploration and implementation of Wav-KAN in frameworks such as PyTorch and TensorFlow, aiming to make wavelets in KAN as widespread as activation functions like ReLU and sigmoid in universal approximation theory (UAT). The codes to replicate the simulations are available at https://github.com/zavareh1/Wav-KAN.

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arXiv2024

Auto-Calibration and 2D-DOA Estimation in UCAs via an Integrated Wideband Dictionary

In this paper, we present a novel auto-calibration scheme for the joint estimation of the two-dimensional (2-D) direction-of-arrival (DOA) and the mutual coupling matrix (MCM) for a signal measured using uniform circular arrays. The method employs an integrated wideband dictionary to mitigate the detrimental effects of the discretization of the continuous parameter space over the considered azimuth and elevation angles. This leads to a reduction of the computational complexity and obtaining of more accurate DOA estimates. Given the more reliable DOA estimates, the method also allows for the estimation of more accurate mutual coupling coefficients. The method utilizes an integrated dictionary in order to iteratively refine the active parameter space, thereby reducing the required computational complexity without reducing the overall performance. The complexity is further reduced by employing only the dominant subspace of the measured signal. Furthermore, the proposed method does not require a constraint on the prior knowledge of the number of nonzero coupling coefficients nor suffer from ambiguity problems. Moreover, a simple formulation for 2-D non-numerical integration is presented. Simulation results show the effectiveness of the proposed method.

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arXiv2024

Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation

Despite many advantages of direction-of-arrivals (DOAs) in sparse representation domain, they have high computational complexity. This paper presents a new method for real-valued 2-D DOAs estimation of sources in a uniform circular array configuration. This method uses a transformation based on phase mode excitation in uniform circular arrays which called real beamspace L1-SVD (RB-L1SVD). This unitary transformation converts complex manifold matrix to real one, so that the computational complexity is decreased with respect to complex valued computations,its computation, at least, is one,fourth of the complex-valued case; moreover, some benefits from using this transformation are robustness to array imperfections, a better noise suppression because of exploiting an additional real structure, and etc. Numerical results demonstrate the better performance of the proposed approach over previous techniques such as C-L1SVD, RB-ESPRIT, and RB-MUSIC, especially in low signal-to-noise ratios.

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