arXiv subjects

Naveed Iqbal

Publications and source records attributed to Naveed Iqbal.

15 recordsLinked to original sources

arXiv2026

Redefining Digital Twins as Predictive Decision Engines for AI-Native Wireless Networks

Future artificial intelligence (AI)-native 6G networks require wireless systems that move beyond reactive optimization toward autonomous, predictive, and continuously adaptive intelligence. Most existing digital twin (DT) frameworks use AI only to improve modeling, data generation, or isolated optimization tasks, leaving the DT itself in a passive, synchronization-only role. This article redefines the DT as a predictive decision engine, which is model-agnostic framework that fuses continuous synchronization, predictive reasoning, autonomous decision-making, and closed-loop wireless control into a single system. Reasoning over synchronized network state, the framework anticipates future conditions and acts autonomously before performance degrades, using generative adversarial networks (GANs), large language models (LLMs), diffusion models, or other learning-based engines interchangeably as the underlying predictor. An illustrative unmanned aerial vehicle (UAV)-assisted non-terrestrial network (NTN) deployment, using a lightweight conditional generative adversarial network (cGAN) as one illustrative predictor, demonstrates the practical effectiveness of the proposed framework by achieving considerable energy savings over reactive baselines while maintaining reliable quality of service (QoS) under highly dynamic conditions.

eess.SP↗

arXiv2026

Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator

Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pickers trained on extensive earthquake catalogs. On a broader scale, these models are generally tuned to perform optimally in high signal-to-noise and long-duration networks and often fail to perform satisfactorily when applied to campaign-based microseismic datasets, which are characterized by low signal-to-noise ratios, sparse geometries, and limited labeled data. In this study, we present a microseismic adaptation of a network-wide earthquake phase picker, Phase Neural Operator (PhaseNO), using transfer learning and parameter-efficient fine-tuning. Starting from a model pre-trained on more than 57,000 three-component earthquake and noise records, we fine-tune it using only 200 labeled and noisy microseismic recordings from hydraulic fracturing settings. We present a parameter-efficient adaptation of PhaseNO that fine-tunes a small fraction of its parameters (only 3.6%) while retaining its global spatiotemporal representations learned from a large dataset of earthquake recordings. We then evaluate our adapted model on three independent microseismic datasets and compare its performance against the original pre-trained PhaseNO, a STA/LTA-based workflow, and two state-of-the-art deep learning models, PhaseNet and EQTransformer. We demonstrate that our adapted model significantly outperforms the original PhaseNO in F1 and accuracy metrics, achieving up to 30% absolute improvements in all test sets and consistently performing better than STA/LTA and state-of-the-art models. With our adaptation being based on a small calibration set, our proposed workflow is a practical and efficient tool to deploy network-wide models in data-limited microseismic applications.

physics.geo-ph↗

arXiv2024

Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures

Model-based deep learning methods such as loop unrolling (LU) and deep equilibrium model}(DEQ) extensions offer outstanding performance in solving inverse problems (IP). These methods unroll the optimization iterations into a sequence of neural networks that in effect learn a regularization function from data. While these architectures are currently state-of-the-art in numerous applications, their success heavily relies on the accuracy of the forward model. This assumption can be limiting in many physical applications due to model simplifications or uncertainties in the apparatus. To address forward model mismatch, we introduce an untrained forward model residual block within the model-based architecture to match the data consistency in the measurement domain for each instance. We propose two variants in well-known model-based architectures (LU and DEQ) and prove convergence under mild conditions. Our approach offers a unified solution that is less parameter-sensitive, requires no additional data, and enables simultaneous fitting of the forward model and reconstruction in a single pass, benefiting both linear and nonlinear inverse problems. The experiments show significant quality improvement in removing artifacts and preserving details across three distinct applications, encompassing both linear and nonlinear inverse problems. Moreover, we highlight reconstruction effectiveness in intermediate steps and showcase robustness to random initialization of the residual block and a higher number of iterations during evaluation. Code is available at \texttt{https://github.com/InvProbs/A-adaptive-model-based-methods}.

cs.LG↗

arXiv2024

Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach.

cs.RO↗

arXiv2023

Review of Contemporary Energy Harvesting Techniques and Their Feasibility in Wireless Geophones

Energy harvesting converts ambient energy to electrical energy providing numerous opportunities to realize wireless sensors. Seismic exploration is a prime avenue to benefit from it as energy harvesting equipped geophones would relieve the burden of cables which account for the biggest chunk of exploration cost and equipment weight. Since numerous energies are abundantly available in seismic fields, these can be harvested to power up geophones. However, due to the random and intermittent nature of the harvested energy, it is important that geophones must be equipped to tap from several energy sources for a stable operation. It may involve some initial installation cost but in the long run, it is cost-effective and beneficial as the sources for energy harvesting are available naturally. Extensive research has been carried out in recent years to harvest energies from various sources. However, there has not been a thorough investigation of utilizing these developments in the seismic context. In this survey, a comprehensive literature review is provided on the research progress in energy harvesting methods suitable for direct adaptation in geophones. Specifically, the focus is on small form factor energy harvesting circuits and systems capable of harvesting energy from wind, sun, vibrations, temperature difference, and radio frequencies. Furthermore, case studies are presented to assess the suitability of the studied energy harvesting methods. Finally, a design of energy harvesting equipped geophone is also proposed.

eess.SP↗

arXiv2023

Learned Proximal Operator for Solving Seismic Deconvolution Problem

Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem with non-unique solutions. Due to the sparse nature of the reflectivity sequence, spike-promoting regularizers such as the $\ell_1$-norm are frequently used. They either require rigorous coefficient tuning or strong assumptions about reflectivity, such as assuming reflectivity as sparse signals with known sparsity levels and zero-mean Gaussian noise with known noise levels. To overcome the limitations of traditional regularizers, learning-based regularizers are proposed in the recent past. This paper proposes a Learned Proximal operator for Seismic Deconvolution (LP4SD), which leverages a neural network to learn the proximal operator of a regularizer. LP4SD is trained in a loop unrolled manner and is capable of learning complicated structures from the training data. It is worth mentioning that the network is trained with synthetic data and evaluated on both synthetic and real data. LP4SD is shown to generate better reconstruction results in terms of three different metrics as compared to learning a direct inverse.

eess.SP↗

arXiv2022

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise).

physics.geo-ph↗

arXiv2020

Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets

Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

cs.LG↗

arXiv2020

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

In this work, an efficient numerical scheme is presented for seismic blind deconvolution in a multichannel scenario. The proposed method iterate with wo steps: first, wavelet estimation across all channels and second, refinement of the reflectivity estimate simultaneously in all channels using sparse deconvolution. The reflectivity update step is formulated as a basis pursuit denoising problem and a sparse solution is obtained with the spectral projected-gradient algorithm - faithfulness to the recorded traces is constrained by the measured noise level. Wavelet re-estimation has a closed form solution when performed in the frequency domain by finding the minimum energy wavelet common to all channels. Nothing is assumed known about the wavelet apart from its time duration. In tests with both synthetic and real data, the method yields sparse reflectivity series and stable wavelet estimates results compared to existing methods with significantly less computational effort.

physics.comp-ph↗

arXiv2020

Sum-Rate Maximization and Data Delivery for Wireless Seismic Acquisition

Traditional seismic acquisition systems suffer from a number of difficulties related to telemetry cables that are used as a means of data transmission. Transforming the traditional seismic acquisition system to a wireless system has been considered as a potential solution to most of these difficulties. The wireless seismic acquisition system has to serve a huge aggregate data rate requirement as is usually the case in a large wireless sensor network. This paper considers the wireless acquisition system, and studies the maximum achievable transmission data rates from the geophones to the wireless gateways. Successive interference cancellation decoding is assumed to be used at the gateway nodes. We consider the problem of sum-rate maximization by optimizing the decoding process at each gateway node. The optimization searches for optimal decoding set at each gateway, i.e. which group of geophones will be decoded at each gateway. Various integer programming algorithms are proposed for solving the maximization problem. These optimization algorithms are simulated and compared among each other, where it is shown that the ant system algorithm achieves the highest sum-rate with lower computational complexity compared to other algorithms. Furthermore, the data delivery from the gateways to the data center is also considered. In this stage, two gateways with different buffer sizes are studied. For small-size buffers, two optimization problems are identified and solved. The first problem considers the minimization of the total power of the gateways, and the second problem considers power fairness between the gateways. For large-size buffers, the problem of maximizing the weighted sum rate of the gateways is solved.

cs.IT↗

arXiv2019

Topology optimization of unsaturated flows in multi-material porous media: application to a simple diaper model

We present a mathematical approach to optimize the material distribution for fluid transport in unsaturated porous media. Our benchmark problem is a simplified diaper model as an exemplary liquid absorber. Our model has up to three materials with vastly different properties, which in the reference configuration are arranged in parallel layers. Here, swelling is neglected and the geometry of a swollen diaper is assumed and treated as a porous medium of high porosity. The imbibition process is then simulated by solving an unsaturated flow problem based on Richards' equation. Our aim is to maximize the amount of absorbed liquid by redistributing the materials. To this end, a density based multi-material topology optimization (based on the well known SIMP model) is performed. A sensitivity analysis for the nonlinear transient problem is provided, which enables the application of first order optimization solvers. We perform two- and three-material optimization and discuss several variants of the problem setting. We present designs with up to 45% more absorbed liquid compared to the reference configuration.

physics.comp-ph↗

arXiv2019

Lower Bound on the Sum-rate of Decremental Beam Selection Algorithm for Beamspace MIMO Systems

In general, the zero-forcing (ZF) precoding suffers from a severe receive signal-to-noise ratio (SNR) degradation in the high interference regime. However, recent evidences from realistic measurements demonstrated that millimeter wave (mmWave) systems are mainly noise-limited as high gain antennas behave as spatial filters to the interference signal. This makes ZF precoding equally attractive as that of other linear precoding counterparts. Considering ZF precoding, this paper aims to derive a lower bound on the sum-rate achieved by a decremental beam selection (BS) algorithm in a beamspace MIMO (B-MIMO) system operating at mmWave frequencies. This bound relates Frobenious norms of precoding matrices of full and reduced dimensional (i.e. after BS) B-MIMO systems through a deterministic square-hyperbolic function. Note that, both ZF precoding and decremental BS are not new concepts. However, the derived sum-rate bound provides a new insight to the topic. Given a particular full dimensional B-MIMO channel, the presented bound can be used to understand limits of BS algorithms.

cs.IT↗

arXiv2017

On the Frequency Dependency of Radio Channel's Delay Spread: Analyses and Findings From mmMAGIC Multi-frequency Channel Sounding

This paper analyzes the frequency dependency of the radio propagation channel's root mean square (rms) delay spread (DS), based on the multi-frequency measurement campaigns in the mmMAGIC project. The campaigns cover indoor, outdoor, and outdoor-to-indoor (O2I) scenarios and a wide frequency range from 2 to 86 GHz. Several requirements have been identified that define the parameters which need to be aligned in order to make a reasonable comparison among the different channel sounders employed for this study. A new modelling approach enabling the evaluation of the statistical significance of the model parameters from different measurements and the establishment of a unified model is proposed. After careful analysis, the conclusion is that any frequency trend of the DS is small considering its confidence intervals. There is statistically significant difference from the 3GPP New Radio (NR) model TR 38.901, except for the O2I scenario.

cs.IT↗

arXiv2017

Iterative interferometry-based method for picking microseismic events

Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.

physics.geo-ph↗

arXiv2016

Microseismic events enhancement and detection in sensor arrays using autocorrelation based filtering

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centered at zero in the lag domain. The effect of white noise is concentrated near zero lag, so the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with colored noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.

physics.geo-ph↗