arXiv subjects

Ahmed M. Eltawil

Publications and source records attributed to Ahmed M. Eltawil.

At least 19 recordsLinked to original sources

arXiv2026

Cramér-Rao Bound Analysis for Cell-Free ISAC Systems with Fluid Intelligent Metasurfaces

Fluid intelligent metasurfaces (FIMs) reshape antenna geometry to improve angular sensitivity. This paper studies their role in cell-free integrated sensing and communication, where distributed access points provide distinct observation directions. Under calibrated path gains, we derive the conditional angular Fisher information matrix, retain cross-AP coupling, and map it to a position-domain Cramér--Rao bound (CRB). A diagonal-information specialization exposes the dependence on angular diversity, while a multi-target extension quantifies the loss from jointly estimating other target positions. We use CRB-driven coordinate search to design transmit and receive shapes and evaluate sensing performance, equal-resource AP scaling, and MRT-based communication--sensing allocation. In the considered four-Tx/four-Rx configuration, shape design reduces the position root CRB from 0.300 to 0.227 m at 10 dB reference SNR. Two-target results further illustrate how cross-target coupling increases the joint localization bound at small separations.

cs.IT↗

arXiv2026

Orientation-Aware Deep Learning Framework for RSSI-Based Indoor Localization in RIS-Aided mmWave Systems

Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) systems provide directional measurements of received power for indoor localization, but device orientation changes the fingerprints. This work proposes an orientation-aware dual-stream gated (O-DSG) deep learning framework for received signal strength indicator (RSSI)-based indoor localization. An orientation classifier estimates device orientation from RSSI, so inference needs only received power. An embedding of the predicted angle and posterior entropy controls feature modulation and soft expert gating. Parallel convolutional and statistical streams are fused through bidirectional long short-term memory (BiLSTM) processing and temporal attention. Single-user measurements in an indoor laboratory at 28 GHz under non-line-of-sight (NLoS) conditions yield a median localization error of 0.141 m, a 25.8% improvement over the closest evaluated baseline (supplied with the measured orientation), and 92.3% orientation classification accuracy across four discrete classes. In spatial regions withheld from training, O-DSG has an average median error of 0.247 m, against 0.392 m for a validation-selected sequence model. A separate offline evaluation constructs non-orthogonal multiple access (NOMA)-style power mixtures from the single-user measurements and measures localization accuracy and computational scaling with up to eight emulated users whose power contributions overlap, under the specified mixture model.

cs.NI↗

arXiv2026

Digital Twin-Conditioned RSS-Fingerprint Localization in Cell-Free Millimeter-Wave Networks

Digital twins (DTs) can generate dense synthetic fingerprints for localization via ray tracing, yet the sim-to-real domain gap limits their direct use, particularly in cell-free millimeter-wave (mmWave) deployments where distributed geometries and blockage-induced sparsity widen the mismatch. We propose DT-conditioned (DTCond), a framework that fuses real and DT beam-domain fingerprints as complementary inputs through per-domain encoders, learnable per-access point (AP) attention, and calibrated uncertainty. We derive oracle and pipeline Cramér-Rao bounds (CRBs) for DT-aided fingerprint localization, proving that DT information strictly tightens the positioning bound whenever spatial structure is preserved, with a heteroscedastic refinement that tightens the bound by a further 9.1%. On a real 28 GHz cell-free testbed, the deployable two-stage pipeline, which reads the DT fingerprint at a coarse k-nearest neighbor (k-NN) estimate, achieves 0.284 m mean error against 0.313 m for real-only k-NN. With the DT fingerprint read at the true position (oracle input), DT conditioning achieves 0.186 m mean error, validated through a ten-method ablation against domain-adaptation (DA) and transfer-learning baselines, and a 48% improvement on unseen regions. Multi-user self-attention provides 31-41% per-user gain for emulated simultaneous users, and progressive calibration reduces the measurement budget by 50%. As a use case, localization drives network configuration in closed loop. Between beam sweeps, a DT updated from measured RSS and read at the tracked position estimate selects the 3 of 81 beam pairs that each AP trains. With 49 calibration anchors in the update, the mean beam-selection loss stays below 1 dB over 2 m of motion, whereas reusing the last sweep loses 2.7 dB after 1 m.

cs.NI↗

arXiv2026

RF Chain Count to Mitigate Beam Split in Wideband Hybrid Arrays: From Far-Field to Near-Field

Deploying large antenna arrays transitions wireless communication into the radiative near-field (NF), where spherical wavefronts enable beamfocusing in the joint angle-distance domain. However, wideband beamforming with large antenna arrays suffers from beam split across both spatial dimensions. Although fully digital arrays can eliminate this impairment, their prohibitive hardware cost and power consumption motivate the adoption of hybrid array architectures equipped with a limited number of radio-frequency (RF) chains. This paper investigates the minimum number of RF chains required to mitigate the beam split. We derive closed-form expressions for the required number of RF chains in fully-connected (FC) architectures and propose an algorithm for sub-connected (SC) architectures, considering both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). Our analysis reveals that beyond a focal distance of 10L, where L denotes the aperture length, the NF RF chain count converges to its far-field counterpart and becomes a function of the angle alone. Moreover, the FC hybrid architecture mitigates the beam split by using fewer RF chains than the SC architecture, albeit at the cost of increased complexity in the phase shifter network.

eess.SP↗

arXiv2026

WALoMA: A Multitask Wireless Foundation Model via Adaptive Low-Rank Masked Autoencoders

This paper proposes a multitask wireless foundation model via adaptive low-rank masked autoencoders (WALoMA), a unified multi-task foundation model for sixth-generation (6G) wireless physical layer architectures, to address the limitations of specialized, task-specific deep learning models and the practical challenge of scarce labeled wireless datasets. By leveraging concepts inspired by foundation models, the proposed framework adopts a masked autoencoder (MAE) paradigm to learn from unlabeled channel data, to significantly reduce reliance on extensive annotations. The model treats wireless channel state information (CSI) as a universal modality and learns transferable representations through self-supervised channel reconstruction. Key architectural novelties include the use of 2D positional encoding (PE) to explicitly preserve the spatial-frequency relationships between antennas and subcarriers, and low-rank adaptation (LoRA) for parameter-efficient fine-tuning. The framework's efficacy is demonstrated across five downstream tasks, achieving individual scores of 96.47\% for LoS/NLoS classification, 80.45\% for beam prediction, 85.78\% for channel interpolation, 99.12\% for channel estimation, and 77.18\% for channel charting. Consequently, numerical results show that the proposed model achieves a composite score of 87.80\%, significantly outperforming the 59.90\% achieved by the large wireless model (LWM) baseline while training an average of only 14.68\% of total parameters, and maintaining strong performance even under extremely limited labeled data conditions.

cs.NI↗

arXiv2026

Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data

Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrained by the lack of annotated training data. Recent advances in vision-based foundational models have shown promising results in zero-shot generalization to novel domains, but their performance drops in complex agricultural environments. In this work, we present a sim-to-real framework for tomato plant segmentation that combines synthetic data generation with fine-tuning of a foundation model. We model a commercial cherry tomato greenhouse and use it to generate a large-scale synthetic dataset under diverse viewpoints, lighting conditions, and plant morphology. Subsequently, we fine-tune the Segment Anything Model 3 (SAM 3) on the synthetic dataset, specializing its text-conditioned segmentation behavior for greenhouse crop organs while retaining the general visual prior that makes zero-shot transfer possible. By evaluating our framework on multiple real-world greenhouse datasets, we demonstrate that combining synthetic data with SAM 3 fine-tuning significantly improves segmentation performance and model confidence. To support community benchmarking, we publicly release the procedural model, the generated synthetic dataset, and our fine-tuned SAM 3 weights.

cs.CV↗

arXiv2026

Sparse Array Design for Near-Field MU-MIMO: Reconfigurable Array Thinning Approach

Future wireless networks, deploying thousands of antenna elements, may operate in the radiative near-field (NF), enabling spatial multiplexing across both angle and range domains. Sparse arrays have the potential to achieve comparable performance with fewer antenna elements. However, fixed sparse array designs are generally suboptimal under dynamic user distributions, while movable antenna architectures rely on mechanically reconfigurable elements, introducing latency and increased hardware complexity. To address these limitations, we propose a reconfigurable array thinning approach that selectively activates a subset of antennas to form a flexible sparse array design without physical repositioning. We first analyze grating lobes for uniform sparse arrays in the angle and range domains, showing their absence along the range dimension. Based on the analysis, we develop two particle swarm optimization-based strategies: a grating-lobe-based thinned array (GTA) for grating-lobe suppression and a sum-rate-based thinned array (STA) for multiuser sum-rate maximization. Simulation results demonstrate that GTA outperforms conventional uniform sparse arrays, while STA achieves performance comparable to movable antennas, thereby offering a practical and efficient array deployment strategy without the associated mechanical complexity.

eess.SP↗

arXiv2026

Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Management

Maintaining robust millimeter-wave (mmWave) connectivity in vehicular networks requires real-time adaptation to environmental dynamics, sensor degradation, and link variability. This paper presents Enwar 3.0, an environment-aware reasoning framework that unifies multi-modal sensing, agentic large language models (LLMs), and context-driven model selection for predictive beamforming, blockage detection, and handover management. Building upon prior iterations of Enwar, the proposed architecture integrates a classifier-driven assessment of sensor health with a primed LLM that orchestrates multiple specialized agents through structured, task-aware prompting. A novel synthetic degradation pipeline enables the training of a sensor degradation classifier that detects real-time impairments across camera, radar, LiDAR, and GPS inputs, achieving over 99% accuracy. The LLM, trained via chain-of-thought (CoT) priming and human-in-the-loop feedback, coordinates agent calls for beam selection, blockage forecasting, and environment perception while dynamically loading sensor-specific models based on environmental context. Extensive evaluations across 15 sensor combinations demonstrate that Enwar 3.0 delivers state-of-the-art performance in both predictive accuracy and interpretability, with beam selection accuracy exceeding 88%, blockage F1-scores surpassing 98%, and reasoning correctness reaching 87% on complex decision prompts. This work establishes a scalable foundation for LLM-integrated wireless systems that reason, perceive, and adapt in real-time.

cs.MA↗

arXiv2026

Uniform Circular Arrays in Near-Field: Omnidirectional Coverage with Limited Capacity

Recent studies suggest that uniform circular arrays (UCAs) can extend the angular coverage of the radiative near field region. This work investigates whether such enhanced angular coverage translates into improved spatial multiplexing performance when compared to uniform linear arrays (ULAs). To more accurately delineate the effective near field region, we introduce the effective beamfocusing Rayleigh distance (EBRD), an angle dependent metric that bounds the spatial region where beamfocusing remains effective. Closed form expressions for both beamdepth and EBRD are derived for UCAs. Our analysis shows that, under a fixed antenna element count, ULAs achieve narrower beamdepth and a longer EBRD than UCAs. Conversely, under a fixed aperture length, UCAs provide slightly narrower beamdepth and a marginally longer EBRD. Simulation results further confirm that ULAs achieve higher sum rate under the fixed element constraint, while UCAs offer marginal performance gain under the fixed aperture constraint.

eess.SP↗

arXiv2026

Boosting Spectral Efficiency via Spatial Path Index Modulation in RIS-Aided mMIMO

Next generation wireless networks focus on improving spectral efficiency (SE) while reducing power consumption and hardware cost. Reconfigurable intelligent surfaces (RISs) offer a viable solution to meet these requirements. In order to enhance the SE, index modulation (IM) has been regarded as one of the enabling technologies via the transmission of additional information bits over the transmission media such as subcarriers, antennas and spatial paths. In this work, we explore the usage of spatial paths and introduce spatial path IM (SPIM) for RIS-aided massive multiple-input multiple-output (mMIMO) systems. Thus, the proposed framework improves the network efficiency and the coverage with the use of RIS while SPIM provides SE improvement. In order to perform SPIM, we exploit the spatial diversity of the millimeter wave channel and assign the index bits to the spatial patterns of the channel between the base station and the users through RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for SPIM-mMIMO. Furthermore, we conduct a theoretical analysis on the SE of the proposed SPIM approach, and derive the SE relationship between the SPIM-based hybrid beamforming and fully digital (FD) beamforming. Via numerical simulations, we validate our theoretical results and show that the proposed SPIM approach presents an improved SE performance, even higher than that of the use of FD beamformers while using a few RF chains.

cs.IT↗

arXiv2026

Array Geometry-Centric Axial Sidelobe Interference Analysis for Near-Field Multi-User MIMO

With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field (NF). Unlike far-field beam steering, NF beams can be focused on a spatial region with finite depth, enabling user multiplexing in both range and angle. In NF multiuser multiple-input multiple-output (MU-MIMO) systems, achievable rates are limited by interference arising from sidelobes in both the axial (range) and lateral (angle) dimensions. This work investigates how axial sidelobes (ASLs) vary with array geometry. Closed-form array gain expressions are derived to characterize ASLs for uniform planar arrays. Analytical results show that the uniform square array (USA) yields the lowest ASLs, followed by the uniform concentric circular array (UCCA), uniform linear array (ULA), and uniform circular array (UCA). Specifically, the USA achieves a peak sidelobe level (PSLL) of -17.6 dB versus -7.9 dB for the UCA. Numerical simulations confirm that the USA provides superior sidelobe suppression and highest sumrate performance.

eess.SP↗

arXiv2026

Analyzing URA Geometry for Enhanced Near-Field Beamfocusing and Spatial Degrees of Freedom

With the deployment of large antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field. Unlike far-field beam steering, near-field beams can be focused on a spatial region with a finite depth, enabling spatial multiplexing in the range dimension. Moreover, in the line-of-sight MIMO near-field, multiple spatial degrees of freedom (DoF) are accessible, akin to a scattering- rich environment. In this paper, we derive the beamdepth for a generalized uniform rectangular array (URA) and investigate how the array geometry influences near-field beamdepth and its limits. We define the effective beamfocusing Rayleigh distance (EBRD), to present a near-field boundary with respect to beamfocusing and spatial multiplexing gains for the generalized URA. Our results demonstrate that under a fixed element count constraint, the array geometry has a strong impact on beamdepth, whereas this effect diminishes under a fixed aperture length constraint. Moreover, compared to uniform square arrays, elongated configurations such as uniform linear arrays (ULAs) yield narrower beamdepth and extend the effective near-field region defined by the EBRD. Building on these insights, we design a polar codebook for compressed-sensing-based channel estimation that leverages our findings. Simulation results show that the proposed polar codebook achieves a 2 dB NMSE improvement over state-of-the-art methods. Additionally, we present an analytical expression to quantify the effective spatial DoF in the near-field, revealing that they are also constrained by the EBRD. Notably, the maximum spatial DoF is achieved with a ULA configuration, outperforming a square URA in this regard.

eess.SP↗

arXiv2026

Spatial Degrees of Freedom in Near Field MIMO: Experimental Validation of Beamspace Perspective

Conventional far-field multiple-input multiple-output (MIMO) channels are limited to a single spatial degree of freedom (DoF) under a line-of-sight (LoS) condition. In contrast, the radiative near field (NF) supports multiple spatial DoF, enabled by spherical wavefronts and the reduced spatial footprint at short ranges. While recent research indicates that the effective DoF (EDoF) increases in NF, experimental validation and clear identification of the transition distances remain limited. In this letter, we develop an intuitive framework for characterizing the EDoF of a ULA-based MIMO system and derive two complementary analytical expressions: a closed-form formulation that relates the EDoF to the physical transmit beamwidth and receive aperture, and a discrete formulation based on the discrete Fourier transform (DFT) domain angular decomposition of the NF spherical wavefront, which is well suited for experimental evaluation. We further introduce the effective MIMO Rayleigh distance (EMRD) and the maximum spatial multiplexing distance (MSMD), which mark the distances where the EDoF reduces to one and attains its maximum, respectively. Experimental measurements using widely spaced phased arrays closely match the theoretical EDoF trends and validate the proposed distance metrics.

eess.SP↗

arXiv2026

Sparsity-Aware Streaming SNN Accelerator with Output-Channel Dataflow for Automatic Modulation Classification

The rapid advancement of wireless communication technologies, including 5G, emerging 6G networks, and the large-scale deployment of the Internet of Things (IoT), has intensified the need for efficient spectrum utilization. Automatic modulation classification (AMC) plays a vital role in cognitive radio systems by enabling real-time identification of modulation schemes for dynamic spectrum access and interference mitigation. While deep neural networks (DNNs) offer high classification accuracy, their computational and energy demands pose challenges for real-time edge deployment. Spiking neural networks (SNNs), with their event-driven nature, offer inherent energy efficiency, but achieving both high throughput and low power under constrained hardware resources remains challenging. This work proposes a sparsity-aware SNN streaming accelerator optimized for AMC tasks. Unlike traditional systolic arrays that exploit sparsity but suffer from low throughput, or streaming architectures that achieve high throughput but cannot fully utilize input and weight sparsity, our design integrates both advantages. By leveraging the fixed nature of kernels during inference, we apply the gated one-to-all product (GOAP) algorithm to compute only on non-zero input-weight intersections. Extra or empty iterations are precomputed and embedded into the inference dataflow, eliminating dynamic data fetches and enabling fully pipelined, control-free inter-layer execution. Implemented on an FPGA, our sparsity-aware output-channel dataflow streaming (SAOCDS) accelerator achieves 23.5 MS/s (approximately double the baseline throughput) on the RadioML 2016 dataset, while reducing dynamic power and maintaining comparable classification accuracy. These results demonstrate strong potential for real-time, low-power deployment in edge cognitive radio systems.

cs.AR↗

arXiv2025

Near Field Tapering with Slepian Window: Balancing the Range Angle Sidelobe Trade off

Near-field beamforming enables target discrimination in both range (axial) and angle (lateral) dimensions. Elevated sidelobes along either dimension, however, increase susceptibility to interference and degrade detection performance. Conventional amplitude tapering techniques, designed for far-field scenarios, cannot simultaneously suppress axial and lateral sidelobes in near-field. In this letter, we propose a Slepian-based amplitude tapering approach that maximizes mainlobe energy concentration, achieving significant sidelobe reduction in both dimensions. Numerical results show that the proposed taper improves peak sidelobe suppression by approximately 24 dB in the lateral domain and 10 dB in the axial domain compared to a conventional uniform window.

eess.SP↗

arXiv2025

Mixed-Precision Quantization for Language Models: Techniques and Prospects

The rapid scaling of language models (LMs) has resulted in unprecedented computational, memory, and energy requirements, making their training and deployment increasingly unsustainable. Quantization has emerged as an essential compression technique to reduce model size, alleviate memory bottlenecks, and accelerate inference. However, while uniform low-bit quantization (e.g., INT8, INT4) provides significant efficiency gains, it can degrade accuracy in sensitive components of transformer-based LMs. Mixed-precision quantization offers a promising alternative by selectively allocating precision across layers or within tensors to balance efficiency and accuracy. This survey provides a comprehensive overview of Mixed-Precision quantization frameworks for LMs (MXPLMs). We first review quantization fundamentals, including uniform and non-uniform quantizers, quantization granularity, and methods widely used in post-training quantization. We then categorize and compare recent MXPLM frameworks according to their bit allocation strategies and precision configurations across weights, activations, and key-value caches. A comparative analysis highlights differences in perplexity, zero-shot task performance, and deployment trade-offs. Furthermore, we contrast MXPLMs with earlier mixed-precision quantization methods for deep neural networks, identifying strategies that transfer and those that face challenges in the LM setting. Finally, we summarize open issues and future directions, including hardware-aware design, activation quantization, and scalable optimization methods for billion-parameter models. By consolidating recent advances, this work serves as a reference for understanding the current landscape and research prospects of mixed-precision quantization for large-scale language models.

cs.LG↗

arXiv2025

Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management

Efficient and reliable beam alignment is a critical requirement for mmWave multiple-input multiple-output (MIMO) systems, especially in 6G and beyond, where communication must be fast, adaptive, and resilient to real-world uncertainties. Existing deep learning (DL)-based beam alignment methods often neglect the underlying causal relationships between inputs and outputs, leading to limited interpretability, poor generalization, and unnecessary beam sweeping overhead. In this work, we propose a causally-aware DL framework that integrates causal discovery into beam management pipeline. Particularly, we propose a novel two-stage causal beam selection algorithm to identify a minimal set of relevant inputs for beam prediction. First, causal discovery learns a Bayesian graph capturing dependencies between received power inputs and the optimal beam. Then, this graph guides causal feature selection for the DL-based classifier. Simulation results reveal that the proposed causal beam selection matches the performance of conventional methods while drastically reducing input selection time by 94.4% and beam sweeping overhead by 59.4% by focusing only on causally relevant features.

cs.AI↗

arXiv2025

Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks

Vehicular communication systems operating in the millimeter wave (mmWave) band are highly susceptible to signal blockage from dynamic obstacles such as vehicles, pedestrians, and infrastructure. To address this challenge, we propose a proactive blockage prediction framework that utilizes multi-modal sensing, including camera, GPS, LiDAR, and radar inputs in an infrastructure-to-vehicle (I2V) setting. This approach uses modality-specific deep learning models to process each sensor stream independently and fuses their outputs using a softmax-weighted ensemble strategy based on validation performance. Our evaluations, for up to 1.5s in advance, show that the camera-only model achieves the best standalone trade-off with an F1-score of 97.1% and an inference time of 89.8ms. A camera+radar configuration further improves accuracy to 97.2% F1 at 95.7ms. Our results display the effectiveness and efficiency of multi-modal sensing for mmWave blockage prediction and provide a pathway for proactive wireless communication in dynamic environments.

cs.LG↗