arXiv subjects

Mohammad Amir Fallah

Publications and source records attributed to Mohammad Amir Fallah.

6 recordsLinked to original sources

arXiv2026

Beam Squint Mitigation in Wideband Hybrid Beamformers: Full-TTD, Sparse-TTD, or Non-TTD?

Beam squint poses a fundamental challenge in wideband hybrid beamforming, particularly for mmWave and THz systems that demand both ultra-wide bandwidth and high directional beams. While conventional phase shifter-based beamformers may offer partial mitigation, True Time Delay (TTD) units provide a fundamentally more effective solution by enabling frequency-independent beam steering. However, the high cost of TTD units has recently driven much interest in Sparse-TTD architectures, which combine a limited number of TTDs with a higher number of conventional PSs to balance performance and cost. This paper provides a critical examination of beam squint mitigation strategies in wideband hybrid beamformers, comparing Full-TTD, Sparse-TTD, and Non-TTD architectures. We analyze recent Non-TTD approaches, specifically the scheme leveraging the wideband beam gain (WBBG) concept, evaluating their performance and cost characteristics against TTD-based solutions. A key focus is placed on the practical limitations of Sparse-TTD architectures, particularly the often-overlooked requirement for wideband PSs operating alongside TTDs, which can significantly impact performance and implementation cost in real-world scenarios, especially for ultra-wideband applications. Finally, we conduct a cost-performance analysis to examine the trade-offs inherent in each architecture and provide guidance on selecting the most suitable hybrid beamforming structure for various fractional bandwidth regimes.

eess.SY↗

arXiv2025

Practical Challenges for Reliable RIS Deployment in Heterogeneous Multi-Operator Multi-Band Networks

Reconfigurable intelligent surfaces (RISs) have been introduced as arrays of nearly passive elements with software-tunable electromagnetic properties to dynamically manipulate the reflection/transmission of radio signals. Research works in this area are focused on two applications, namely {\it user-assist} RIS aiming at tuning the RIS to enhance the quality-of-service (QoS) of target users, and the {\it malicious} RIS aiming for an attacker to degrade the QoS at victim receivers through generating {\it intended} destructive interference. While both user-assist and malicious RIS applications have been explored extensively, the impact of RIS deployments on imposing {\it unintended} interference on various wireless user-equipments (EUs) remains underexplored. This paper investigates the challenges of integrating RISs into multi-carrier, multi-user, and multi-operator networks. We discuss how RIS deployments intended to benefit specific users can negatively impact other users served at various carrier frequencies through different network operators. While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks. We also present a simulation scenario to illuminate some practical challenges associated with the deployment of RISs in shared public environments.

eess.SY↗

arXiv2025

Vessel Length Estimation from Magnetic Wake Signature: A Physics-Informed Residual Neural Network Approach

Marine remote sensing enhances maritime surveillance, environmental monitoring, and naval operations. Vessel length estimation, a key component of this technology, supports effective maritime surveillance by empowering features such as vessel classification. Departing from traditional methods relying on two-dimensional hydrodynamic wakes or computationally intensive satellite imagery, this paper introduces an innovative approach for vessel length estimation that leverages the subtle magnetic wake signatures of vessels, captured through a low-complexity one-dimensional profile from a single airborne magnetic sensor scan. The proposed method centers around our characterized nonlinear integral equations that connect the magnetic wake to the vessel length within a realistic finite-depth marine environment. To solve the derived equations, we initially leverage a deep residual neural network (DRNN). The proposed DRNN-based solution framework is shown to be unable to exactly learn the intricate relationships between parameters when constrained by a limited training-dataset. To overcome this issue, we introduce an innovative approach leveraging a physics-informed residual neural network (PIRNN). This model integrates physical formulations directly into the loss function, leading to improved performance in terms of both accuracy and convergence speed. Considering a sensor scan angle of less than $15^\circ$, which maintains a reasonable margin below Kelvin's limit angle of $19.5^\circ$, we explore the impact of various parameters on the accuracy of the vessel length estimation, including sensor scan angle, vessel speed, and sea depth. Numerical simulations demonstrate the superiority of the proposed PIRNN method, achieving mean length estimation errors consistently below 5\% for vessels longer than 100m. For shorter vessels, the errors generally remain under 10\%.

cs.NE↗

arXiv2024

Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

eess.SP↗

arXiv2024

6G Fresnel Spot Beamfocusing using Large-Scale Metasurfaces: A Distributed DRL-Based Approach

In this paper, we introduce the concept of spot beamfocusing (SBF) in the Fresnel zone through extremely large-scale programmable metasurfaces (ELPMs) as a key enabling technology for 6G networks. A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including efficient wireless power transfer (WPT), interference mitigation, reduced RF pollution, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by proposing an adaptive novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structures benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.

eess.SY↗

arXiv2024

Towards Near-Field 3D Spot Beamfocusing: Possibilities, Challenges, and Use-cases

Spot beamfocusing (SBF) is the process of focusing the signal power in a small spot-like region in the 3D space, which can be either hard-tuned (HT) using traditional tools like lenses and mirrors or electronically reconfigured (ER) using modern large-scale intelligent surface phased arrays. ER-SBF can be a key enabling technology (KET) for the next-generation 6G wireless networks offering benefits to many future wireless application areas such as wireless communication and security, mid-range high-power and safe wireless chargers, medical and health, physics, etc. Although near-field HT-SBF and ER-beamfocusing have been studied in the literature and applied in the industry, there is no comprehensive study of different aspects of ER-SBF and its future applications, especially for nonoptical (mmWave, sub-THz, and THz) electromagnetic waves in the next generation wireless technology, which is the aim of this paper. The theoretical concepts behind ER-SBF, different antenna technologies for implementing ER-SBF, employing machine learning (ML)-based schemes for enabling channel-state-information (CSI)-independent ER-SBF, and different practical application areas that can benefit from ER-SBF will be explored.

cs.IT↗