arXiv subjects

Vahid Jamali

Publications and source records attributed to Vahid Jamali.

At least 19 recordsLinked to original sources

arXiv2026

Resource Allocation for Cloud Radar Networks with Communication Constraints

Distributed radar sensing exploits spatial diversity to resolve occlusions and improve estimation accuracy. Realizing these gains, however, relies on the transmission of high-dimensional radar data to a Fusion Center (FC). This imposes significant demands on the wireless network, especially in dense, dynamic, and interference-prone environments like factories, where resilience and latency are critical. This paper studies the resource allocation problem in a capacity-constrained two-hop cloud radar network. We propose a buffered access protocol where sensors perform local spectral windowing to reduce data rates before transmitting measurements to the FC via intermediate Edge Servers (ESs). The resource allocation is formulated as a mixed-integer optimization problem aimed at minimizing the aggregate Cramer-Rao Lower Bound (CRLB) of the target parameters subject to fronthaul and backhaul capacity constraints. We develop an iterative solution algorithm based on Big-M formulation and Successive Convex Approximation (SCA). The proposed framework efficiently identifies the most informative sensor subsets and optimizes time-frequency resource assignments, ensuring high-fidelity sensing within stringent communication budgets.

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arXiv2026

Temperature-aware Optimization of Liquid Crystal Reconfigurable Intelligent Surfaces: Physics-based Modeling and Robust Design

While LC technology facilitates the realization of energy-efficient and scalable RISs, their phase shift response is inherently temperature-dependent. Neglecting this thermal dependency can lead to performance degradation, which is particularly detrimental in secure wireless systems where phase-shift inaccuracies may result in unintended information leakage. To address this challenge, we investigate secure communication in LC-RIS-aided systems and develop a temperature-adaptive phase-shift design. Beyond thermal sensitivity, the massive number of elements at mmWave frequencies is required to compensate for high path loss. This large-scale deployment of LC-RISs can lead to significant overhead challenges due to the acquisition of CSI. To ensure practical feasibility, this work proposes a phase-shift design that does not rely on the full CSI; instead, it employs only the possible locations of legitimate users and potential eavesdroppers. By illuminating a spatial zone rather than a single target location, the proposed temperature-adaptive algorithm enhances robustness against both thermally induced phase errors and positioning inaccuracies. To solve the resulting optimization problem, we present an SDP-based approach to serve as a high-performance benchmark, as well as a low-complexity heuristic method. The latter demonstrates superior scalability as the number of RIS elements increases, which makes it highly effective for deploying extremely large surfaces in dynamic, real-time environments. Based on this scalable framework, we further design a temperature-robust algorithm that maintains high security without requiring real-time temperature data. Extensive simulation results confirm that our temperature-adaptive and temperature-robust approaches yield a superior secrecy rate compared to conventional designs that neglect temperature impacts.

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arXiv2026

Uncertainty-Aware Fusion for Resilient Distributed Radar Sensing

Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a moving agent equipped with a frequency modulated continuous wave (FMCW) radar that fuses its local measurements with those of surrounding static radar sensors to reduce the uncertainty in target state estimation. The benefit of sharing measurements over capacity-limited wireless links come at the expense of two competing degradation mechanisms: quantization distortion, which increases the effective noise floor, and latency, which causes the received information to age. A unified framework is thus needed to quantify how individual radars contribute to uncertainty reduction under limited communication resources. To this end, we derive the Cramer-Rao lower bound (CRLB) for the fused target state by combining local Fisher information matrices (FIMs) through coordinate transformations, incorporating distortion bounds from rate-distortion theory and information aging via a state transition model. We show that the resulting expression reveals a fundamental tradeoff between update rate and quantization fidelity governed by the available channel capacity. Our numerical simulations illustrate this tradeoff and demonstrate that a careful choice of system parameters (e.g., selected radar systems, quantization, update rate) is necessary for maximum uncertainty reduction.

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arXiv2026

Fault-Aware Design for Reconfigurable Holographic Surface-Aided ISAC Systems

Reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) systems hold great promise for achieving both sensing and communication with low hardware costs and high energy efficiency. However, existing works largely overlook practical hardware impairments in RHSs, particularly faulty RHS elements with uncontrollable amplitudes, which degrade system performance if left unaddressed. This work aims to fill the gap by i) quantifying the impact of faulty RHS elements on ISAC performance and ii) optimizing the functional RHS elements to preserve the ISAC performance. Specifically, we derive the misspecified Cramer-Rao bound (MCRB) for sensing and the signal-to-interference-and-noise ratio (SINR) for communication to measure the performance loss caused by faulty elements. We then formulate an optimization problem that minimizes MCRB, subject to constraints on SINR, transmit power budget, and RHS amplitude. The high non-convexity of the formulated problem poses a significant challenge, which we address by reformulating and proposing a block coordinate descent-based solution incorporating majorization-minimization and successive convex approximation techniques. Simulation results verify that the proposed approach achieves an average 13.7% performance gain compared to the fault-unaware benchmark.

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arXiv2026

Source Distance Estimation in Turbulent Airflow: Exploiting Molecule Degradation Diversity

In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also received increasing attention in synthetic molecular communication (SMC), e.g., for leakage detection. One important aspect of source localization is to estimate the distance to the molecule source, e.g., to determine whether it is worth to travel to a potential mating partner or food source, or to decide whether a leak is close enough for inspection. In this study, based on realistic simulations, we show that the diversity induced by molecule mixtures can aid source localization. In particular, when different molecule types in a mixture are subject to atmospheric degradation with different degradation rates, the relative abundance of the different species observed at the receiver enables low-complexity estimation of the source distance. Furthermore, this feature can be combined with already established concentration-based and temporal features of observed molecular signals to further increase estimation accuracy. Thereby, we show that molecule degradation diversity of molecule mixtures can help to realize one of the important envisioned SMC applications, namely source localization, even in turbulent airflow, opening new opportunities for the exploitation of SMC to solve real-world problems.

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arXiv2026

Wideband Illumination with Liquid Crystal Reconfigurable Intelligent Surfaces: Modeling, Design, and Experimental Tests

Liquid crystal (LC) is a promising hardware solution for implementing large RISs, as it is cost-effective, energy efficient, scalable, and capable of providing continuous phase shifts with low power consumption. However, the phase shift response of LC-based RISs is inherently frequency dependent. If unaddressed, this characteristic leads to performance degradation, particularly in wideband scenarios. This issue is especially critical in secure communication applications, where minor phase shift variations across elements can result in considerable information leakage. This paper addresses these frequency-induced variations by developing a physics-based model for an LC unit cell across varying frequencies and proposing a novel phase shift design framework that maximizes secure communication across all subcarriers. Given the large number of elements in millimeter wave (mmWave) LC-RISs, acquiring full channel state information (CSI) is often impractical. Therefore, we optimize the phase shifts based solely on the locations of the legitimate mobile users (MUs) and potential eavesdroppers. Rather than targeting a single user point, the RIS is designed to illuminate a broader area. This approach enhances communication reliability for the MUs and mitigates performance degradation caused by location estimation errors. To solve the problem, we introduce both a semi-definite programming (SDP)-based solution and a low complexity heuristic method. While the SDP-based approach yields superior performance, it incurs higher computational complexity. Conversely, the scalable method exhibits a much slower scaling of complexity, which makes it highly suitable for extremely large RISs. Simulation results demonstrate that both algorithms improve the secrecy rate compared to baseline methods. Finally, the proposed design is validated through experimental evaluations on an LC RIS setup.

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arXiv2026

Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems

In this paper, we introduce an autoencoder (AE)-based scheme for end-to-end optimization of a multi-user molecule mixture communication system. In the proposed scheme, each transmitter leverages an encoder network that maps the user symbol to a molecule mixture. The mixtures then propagate through the channel to the receiver, which samples the channel using a non-linear, cross-reactive sensor array. A decoder network then estimates the symbol transmitted by each user based on the sensor observations. The proposed scheme achieves, for a given signal-to-noise ratio, lower symbol error rates than a baseline scheme from the literature in a single-user setting with full channel state information. We additionally demonstrate that the proposed AE-based scheme allows reliable communication when the channel is unknown or changing. Finally, we show that for multiple access the system can account for different user priorities. In summary, the proposed AE-based scheme enables end-to-end system optimization in complex scenarios unsuitable for analytical treatment and thereby brings molecular communication systems closer to real-world deployment.

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arXiv2026

Matched Filter-Based Molecule Source Localization in Advection-Diffusion-Driven Pipe Networks with Known Topology

Synthetic molecular communication (MC) has emerged as a powerful framework for modeling, analyzing, and designing communication systems where information is encoded into properties of molecules. Among the envisioned applications of MC is the localization of molecule sources in pipe networks (PNs) like the human cardiovascular system (CVS), sewage networks (SNs), and industrial plants. While existing algorithms mostly focus on simplified scenarios, in this paper, we propose the first framework for source localization in complex PNs with known topology, by leveraging the mixture of inverse Gaussians for hemodynamic transport (MIGHT) model as a closed-form representation for advection-diffusion-driven MC in PNs. We propose a matched filter (MF)-based approach to identify molecule sources under realistic conditions such as unknown release times, random numbers of released molecules, sensor noise, and limited sensor sampling rate. We apply the algorithm to localize a source of viral markers in a real-world SN and show that the proposed scheme outperforms randomly guessing sources even at low signal-to-noise ratios (SNRs) at the sensor and achieves error-free localization under favorable conditions, i.e., high SNRs and sampling rates. Furthermore, by identifying clusters of frequently confused sources, reliable cluster-level localization is possible at substantially lower SNRs and sampling rates.

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arXiv2026

Molecule Mixture Detection and Design for MC Systems with Non-linear, Cross-reactive Receiver Arrays

Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by commercially available sensors. However, these sensors usually exhibit non-linear and cross-reactive behavior, contrary to the idealizing assumption of linear and perfectly molecule type-specific sensing often made in the MC literature. To address this mismatch, we propose several detectors and transmission schemes for a molecule mixture communication system where the receiver (RX) employs non-linear, cross-reactive sensors. All proposed schemes are based on the first- and second-order moments of the symbol likelihoods that are fed through the non-linear RX using the Unscented Transform. In particular, we propose an approximate maximum likelihood (AML) symbol-by-symbol detector for inter-symbol-interference (ISI)-free transmission scenarios and a complementary mixture alphabet design algorithm which accounts for the RX characteristics. When significant ISI is present at high data rates, the AML detector can be adapted to exploit statistical ISI knowledge. Additionally, we propose a sequence detector which combines information from multiple symbol intervals. For settings where sequence detection is not possible due to extremely limited computational power at the RX, we propose an adaptive transmission scheme which can be combined with symbol-by-symbol detection. Using computer simulations, we validate all proposed detectors and algorithms based on the responses of commercially available sensors as well as artificially generated sensor data incorporating the characteristics of metal-oxide semiconductor sensors. By employing a general system model that accounts for transmitter noise, ISI, and general non-linear, cross-reactive RX arrays, this work enables reliable communication for a large class of MC systems.

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arXiv2026

Flexible RISs: Learning-based Array Manifold Estimation and Phase-shift Optimization

Reconfigurable intelligent surfaces (RISs) are envisioned as a key enabler for next-generation wireless networks, offering programmable control over propagation environments. While extensive research focuses on planar RIS architectures, practical deployments often involve non-planar surfaces, such as structural columns or curved facades, where standard planar beamforming models fail. Moreover, existing analytical solutions for curved RISs are often restricted to specific, pre-defined array manifold geometries. To address this limitation, this paper proposes a novel deep learning (DL) framework for optimizing the phase shifts of non-planar RISs. We first introduce a low-dimensional parametric model to capture arbitrary surface curvature effectively. Based on this, we design a neural network (NN) that utilizes a sparse set of received power measurements to estimate the surface geometry and derive the optimal phase configuration. Simulation results demonstrate that the proposed algorithm converges fast and significantly outperforms conventional planar beamforming designs, validating its robustness against arbitrary surface curvature. We also analyze the impact of the measurement location error on the algorithm's performance.

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arXiv2026

Transparent and Resilient Activity Recognition via Attention-Based Distributed Radar Sensing

Distributed radar sensors enable robust human activity recognition. However, scaling the number of coordinated nodes introduces challenges in feature extraction from large datasets, and transparent data fusion. We propose an end-to-end framework that operates directly on raw radar data. Each radar node employs a lightweight 2D Convolutional Neural Network (CNN) to extract local features. A self-attention fusion block then models inter-node relationships and performs adaptive information fusion. Local feature extraction reduces the input dimensionality by up to 480x. This significantly lowers communication overhead and latency. The attention mechanism provides inherent interpretability by quantifying the contribution of each radar node. A hybrid supervised contrastive loss further improves feature separability, especially for fine-grained and imbalanced activity classes. Experiments on real-world distributed Ultra Wide Band (UWB) radar data demonstrate that the proposed method reduces model complexity by 70.8\%, while achieving higher average accuracy than baseline approaches. Overall, the framework enables transparent, efficient, and low-overhead distributed radar sensing.

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arXiv2025

Learning-Based Phase Shift Optimization of Liquid Crystal RIS in Dynamic mmWave Networks

To enhance coverage and signal quality in millimeter-wave (mmWave) frequencies, reconfigurable intelligent surfaces (RISs) have emerged as a game-changing solution to manipulate the wireless environment. Traditional semiconductor-based RISs face scalability issues due to high power consumption. Meanwhile, liquid crystal-based RISs (LC-RISs) offer energy-efficient and cost-effective operation even for large arrays. However, this promise has a caveat. LC-RISs suffer from long reconfiguration times, on the order of tens of milliseconds, which limits their applicability in dynamic scenarios. To date, prior works have focused on hardware design aspects or static scenarios to address this limitation, but little attention has been paid to optimization solutions for dynamic settings. Our paper fills this gap by proposing a reinforcement learning-based optimization framework to dynamically control the phase shifts of LC-RISs and maximize the data rate of a moving user. Specifically, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm that adapts the LC-RIS phase shifts without requiring perfect channel state information and balances the tradeoff between signal-to-noise ratio (SNR) and configuration time. We validate our approach through high-fidelity ray tracing simulations, leveraging measurement data from an LC-RIS prototype. Our results demonstrate the potential of our solution to bring adaptive control to dynamic LC-RIS-assisted mmWave systems.

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arXiv2025

Near-Field Multipath MIMO Channels: Modeling Reflectors and Exploiting NLOS Paths

Near-field (NF) communications is receiving renewed interest in the context of multiple-input multiple-output (MIMO) systems involving large physical apertures with respect to the signal wavelength. While line-of-sight (LOS) links are typically expected to dominate in NF scenarios, the impact of non-LOS (NLOS) components at both in centimeter- and millimeter-wave frequencies may be in general non-negligible. Moreover, although weaker than the LOS path, NLOS links may be essential for achieving multiplexing gains in MIMO systems. The commonly used NF channel models for NLOS links in the literature are based on the point scattering assumption, which is not valid for large reflectors such as walls, ceilings, and the ground. In this paper, we develop a generalized statistical NF MIMO channel model that extends the widely adopted point scattering framework to account for imperfect reflections from large surfaces. This model is then leveraged to investigate how the physical characteristics of these reflectors influence the resulting NF MIMO channel. In addition, using the proposed channel model, we analytically demonstrate for a multi-user scenario that, even when users are located within the NF regime, relying solely on LOS NF links may be insufficient to achieve multiplexing gains, thus exploiting NLOS links becomes essential. Our simulation results validate the accuracy of the proposed model and show that, in many practical settings, the contribution of NLOS components is non-negligible and must be carefully accounted for in the system design.

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arXiv2025

Resilient Vital Sign Monitoring Using RIS-Assisted Radar

Vital sign monitoring plays a critical role in healthcare and well-being, as parameters such as respiration and heart rate offer valuable insights into an individual's physiological state. While wearable devices allow for continuous measurement, their use in settings like in-home elderly care is often hindered by discomfort or user noncompliance. As a result, contactless solutions based on radar sensing have garnered increasing attention. This is due to their unobtrusive design and preservation of privacy advantages compared to camera-based systems. However, a single radar perspective can fail to capture breathing-induced chest movements reliably, particularly when the subject's orientation is unfavorable. To address this limitation, we integrate a reconfigurable intelligent surface (RIS) that provides an additional sensing path, thereby enhancing the robustness of respiratory monitoring. We present a novel model for multi-path vital sign sensing that leverages both the direct radar path and an RIS-reflected path. We further discuss the potential benefits and improved performance our approach offers in continuous, privacy-preserving vital sign monitoring.

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arXiv2025

CKM-Assisted Physical-Layer Security for Resilience Against Unknown Eavesdropping Location

Channel Knowledge Map (CKM) is an emerging data-driven toolbox that captures our awareness of the wireless channel and enables efficient communication and resource allocation beyond the state of the art. In this work, we consider CKM for improving physical-layer security (PLS) in the presence of a passive eavesdropper (Eve), without making any assumptions about Eve's location or channel state information (CSI). We employ highly directional mmWave transmissions, with the confidential message jointly encoded across multiple beams. By exploiting CKM, we derive an algorithm for time and power allocation among the beams that maximizes the absolute secrecy rate under the worst-case scenario for Eve's location.

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arXiv2025

Liquid Crystal-Based RIS Loss-Trade-Off Analysis

Liquid crystal (LC) technology has emerged as a promising solution for large reconfigurable intelligent surfaces (RISs) at millimeter wave (mmWave) bands, offering advantages such as low power consumption, scalability, and continuously tunable phase shifts. For LC-RIS based on the delay-line architecture, i.e., with dedicated phase shifters, there exists a trade-off between the maximum achievable phase-shift range and the corresponding insertion loss, which has not been studied for LC-RIS-assisted wireless systems yet. In this paper, we investigate this trade-off where a base station (BS) and an RIS are configured to minimize the transmit power while satisfying a given quality of service (QoS) for a number of users. Simulation results reveal a fundamental trade-off between the total transmit power and the achievable data rate as a function of the LC phase-shift range.

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arXiv2025

Molecule Mixture Detection and Alphabet Design for Non-linear, Cross-reactive Receiver Arrays in MC

Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by existing sensor technologies such as metal-oxide semi-conductor (MOS) sensors. However, commercially available sensors usually exhibit non-linear and cross-reactive behavior, contrary to the idealizing assumptions about linear and perfectly molecule type-specific sensing often made in the MC literature. To address this gap, we propose a detector for molecule mixture communication with a general non-linear, cross-reactive receiver (RX) array that performs approximate maximum likelihood detection on the sensor outputs. Additionally, we introduce an algorithm for the design of mixture alphabets that accounts for the RX characteristics. We evaluate our detector and alphabet design algorithm through simulations that are based on measurements reported for two commercial MOS sensors. Our simulations demonstrate that the proposed detector achieves similar symbol error rates as data-driven methods without requiring large numbers of training samples and that the alphabet design algorithm outperforms methods that do not account for the RX characteristics. Since the proposed detector and alphabet design algorithm are also applicable to other chemical sensors, they pave the way for reliable air-based MC.

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arXiv2025

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge in communication. Compared to state-of-the-art, the proposed approach combines analytical optimal BS precoding and ML-enabled RIS, has a high scalability to control more than 1000 RIS elements, has a low requirement for channel state information (CSI) in input, and addresses the mutual coupling between RIS elements. Beyond the considered problem, this work is an early contribution to domain knowledge enabled ML, which exploit the domain expertise of communication systems to design better approaches than general ML methods.

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