arXiv subjects

Rahul Gulia

Publications and source records attributed to Rahul Gulia.

7 recordsLinked to original sources

arXiv2026

Sensing-Aware Backscatter Communications: A Survey on Envelope Stability, Waveform Design, and Selection Diversity

The vision of ubiquitous battery-free connectivity has positioned backscatter communication as a foundational technology for next-generation Internet of Things (IoT) and industrial sensing applications. However, as backscatter systems evolve from simple identification tags toward high-fidelity integrated sensing and communication (ISAC) devices, fundamental challenges emerge at the intersection of waveform design, hardware nonlinearity, and channel dynamics. This paper presents a comprehensive survey of sensing-aware backscatter communications, with a focus on three tightly coupled dimensions: envelope stability, spatial diversity, and temporal channel robustness. We introduce the concept of the ``Illuminator's Dilemma'' to describe the inherent conflict between high-peak-to-average power ratio (PAPR) multicarrier waveforms optimized for active communication and the stringent dynamic range requirements of passive tags. The survey provides a unified treatment of multi-objective waveform design strategies, a comprehensive taxonomy of near-far mitigation techniques, and an in-depth analysis of channel state information (CSI) aging in backscatter networks. Key contributions include tag-centric metrics such as the Backscatter Crest Factor (BCF), Envelope Stability Factor (ESF), and Sensing Fidelity Index (SFI), along with a comparative evaluation of PAPR reduction techniques, spatial diversity methods, and non-coherent detection schemes. This survey is intended for researchers, engineers, and graduate students working in wireless communications, IoT, RF sensing, and integrated sensing and communications.

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arXiv2026

4D Fresnel Space-Time Modulation for Near-Field ELAA: Kinematic Multiplexing and O(N log N) Precoding at Sub-THz Frequencies

Extremely Large Antenna Arrays (ELAA) operating at sub-terahertz frequencies introduce a regime where near-field Fresnel propagation and high-mobility carrier Doppler interact simultaneously, creating a four-dimensional signal space that existing schemes exploit only partially. This paper proposes \textbf{4D Fresnel Space-Time Modulation (4D-FSM)}, a unified framework encoding information jointly across angle, depth, synthetic velocity, and QAM amplitude through a structured symbol manifold $\mathcal{S}$. Synthetic velocity is introduced via Space-Time Modulation (STM): a linear phase ramp $u(\xi,t) = \exp(j[\Omega t + g_k\xi])$ induces a Doppler-equivalent shift without physical motion, creating velocity-orthogonal bubbles that resolve co-located users. We derive the joint orthogonality surface governing simultaneous user separability in depth and velocity, revealing that users separated in depth require strictly less velocity separation to remain orthogonal -- a multiplexing gain with no counterpart in OTFS or LDMA. The Discrete Fresnel Transform (DFnT) factorization $\mathbf{H} = \mathbf{F}_D \mathbf{C}(z) \mathbf{P}$ reduces precoder complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N\log N)$, completing within \SI{500}{\nano\second} against a \SI{5.4}{\micro\second} coherence window. Monte Carlo evaluation at $f_c = \SI{140}{\giga\hertz}$, $N = 4096$ confirms $\rho \approx 0.998$ across the full velocity range, \SI{6.16}{\bit\per\second\per\hertz} spectral efficiency where all baselines collapse, and $K_{\max} = 64$ orthogonal users -- a $248\times$ sum-rate advantage over TTD at $K = 50$.

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arXiv2025

Resource Allocation via Backscatter-Aware Transmit Antenna Selection for Low-PAPR and Ultra-Reliable WSNs

This paper addresses a fundamental physical layer conflict in hybrid Wireless Sensor Networks (WSNs) between high-throughput primary communication and the stringent power envelope requirements of passive backscatter sensors. We propose a Backscatter-Constrained Transmit Antenna Selection (BC-TAS) framework, a per-subcarrier selection strategy for multi-antenna illuminators operating within a Multi-Dimensional Orthogonal Frequency Division Multiplexing (MD-OFDM) architecture. Unlike conventional signal-to-noise ratio (SNR) centric selection schemes, BC-TAS employs a multi-objective cost function that jointly maximizes desired link reliability, stabilizes the incident RF energy envelope at passive Surface Acoustic Wave (SAW) sensors, and suppresses interference toward coexisting victim receivers. By exploiting the inherent sparsity of MD-OFDM, the proposed framework enables dual-envelope regulation, simultaneously reducing the transmitter Peak-to-Average Power Ratio (PAPR) and the Backscatter Crest Factor (BCF) observed at the tag. To enhance robustness under imperfect Channel State Information (CSI), a Kalman-based channel smoothing mechanism is incorporated to maintain selection stability in low-SNR regimes. Numerical results using IEEE 802.11be dispersive channel models and a nonlinear Rapp power amplifier demonstrate that BC-TAS achieves orders-of-magnitude improvement in outage probability and significant gains in energy efficiency compared to conventional MU-MIMO baselines, while ensuring spectral mask compliance under reduced power amplifier back-off. These results establish BC-TAS as an effective illuminator-side control mechanism for enabling reliable and energy-stable sensing and communication coexistence in dense, power-constrained wireless environments.

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arXiv2025

White-Box Modeling of V2X Link Performance Using Stabilized Symbolic Regression

Reliable modeling of block error rate in vehicle-to-everything wireless networks is critical for designing robust communication systems under dynamic mobility and diverse channel conditions. Traditional machine learning approaches, such as deep neural networks, achieve high predictive accuracy but lack interpretability and impose significant computational costs, limiting their applicability in real-time, resource-constrained environments. In this work, we propose a stabilized symbolic regression framework to derive compact, analytically interpretable expressions for block error rate prediction. Trained on realistic vehicle-to-everything simulation data, the symbolic regression framework for vehicle-to-everything model accurately captures nonlinear dependencies on key system parameters, including signal-to-noise ratio, relative velocity, modulation and coding schemes, number of demodulation reference signal symbols, and environmental factors (line of sight/non-line of sight). Our final symbolic expression comprises only 158 nodes, enabling ultra-fast inference suitable for embedded deployment. On the test set, the symbolic regression framework for vehicle-to-everything model achieves a coefficient of determination $R^2 = 0.8684$ and mean squared error $= 2.08 \times 10^{-2}$ in the original block error rate domain, outperforming conventional fixed-form regressions and offering comparable accuracy to neural networks while remaining fully interpretable. Overall, the proposed Stabilized Symbolic Regression Framework for V2X combines predictive performance, physical fidelity, and computational efficiency thus providing a powerful tool for real-time V2X communication system design, adaptive resource allocation, and rapid scenario evaluation.

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arXiv2025

Memory-Augmented Generative AI for Real-time Wireless Prediction in Dynamic Industrial Environments

Accurate and real-time prediction of wireless channel conditions, particularly the Signal-to-Interference-plus-Noise Ratio (SINR), is a foundational requirement for enabling Ultra-Reliable Low-Latency Communication (URLLC) in highly dynamic Industry 4.0 environments. Traditional physics-based or statistical models fail to cope with the spatio-temporal complexities introduced by mobile obstacles and transient interference inherent to smart warehouses. To address this, we introduce Evo-WISVA (Evolutionary Wireless Infrastructure for Smart Warehouse using VAE), a novel synergistic deep learning architecture that functions as a lightweight 2D predictive digital twin of the radio environment. Evo-WISVA integrates a memory-augmented Variational Autoencoder (VAE) featuring an Attention-driven Latent Memory Module (LMM) for robust, context-aware spatial feature extraction, with a Convolutional Long Short-Term Memory (ConvLSTM) network for precise temporal forecasting and sequential refinement. The entire pipeline is optimized end-to-end via a joint loss function, ensuring optimal feature alignment between the generative and predictive components. Rigorous experimental evaluation conducted on a high-fidelity ns-3-generated industrial warehouse dataset demonstrates that Evo-WISVA significantly surpasses state-of-the-art baselines, achieving up to a 47.6\% reduction in average reconstruction error. Crucially, the model exhibits exceptional generalization capacity to unseen environments with vastly increased dynamic complexity (up to ten simultaneously moving obstacles) while maintaining amortized computational efficiency essential for real-time deployment. Evo-WISVA establishes a foundational technology for proactive wireless resource management, enabling autonomous optimization and advancing the realization of predictive digital twins in industrial communication networks.

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arXiv2025

MD-OFDM: An Energy-Efficient and Low-PAPR MIMO-OFDM Variant for Resource-Constrained Applications

Orthogonal Frequency Division Multiplexing (OFDM) combined with Multiple-Input Multiple-Output (MIMO) techniques forms the backbone of modern wireless communication systems. While offering high spectral efficiency and robustness, conventional MIMO-OFDM, especially with complex equalizers like Minimum Mean Square Error (MMSE), suffers from high Peak-to-Average Power Ratio (PAPR) and significant power consumption due to multiple active Radio Frequency (RF) chains. This paper proposes and mathematically models an alternative system, termed Multi-Dimensional OFDM (MD-OFDM), which employs a per-subcarrier transmit antenna selection strategy. By activating only one transmit antenna for each subcarrier, MD-OFDM aims to reduce PAPR, lower power consumption, and improve Bit Error Rate (BER) performance. We provide detailed mathematical formulations for BER, Energy Efficiency (EE), and PAPR, and discuss the suitability of MD-OFDM for various applications, particularly in energy-constrained and cost-sensitive scenarios such as the Internet of Things (IoT) and Low-Power Wide Area Networks (LPWAN). Simulation results demonstrate that MD-OFDM achieves superior BER and significantly lower PAPR compared to MMSE MIMO, albeit with a trade-off in peak overall energy efficiency due to reduced spectral multiplexing.

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arXiv2025

AI-Driven Radio Propagation Prediction in Automated Warehouses using Variational Autoencoders

The pervasive demand for data-intensive applications and the rapid integration of emerging technologies are driving an unprecedented transformation in wireless communication, particularly within Industry 4.0. Optimizing 5G and future networks for automated environments like smart warehouses requires advanced solutions for indoor radio propagation. To this end, this paper introduces WISVA (Wireless Infrastructure for Smart Warehouses using VAE), an AI-based framework utilizing a novel Variational Autoencoder (VAE) model (AI Contribution). The VAE's unique architecture learns complex electromagnetic (EM) wave interactions from meticulously crafted, physics-informed data tensors, enabling it to accurately model signal behavior impacted by diverse obstacles. This engineering application provides site specific signal-to-interference-plus-noise ratio (SINR) heatmaps with relative fine granularity for 5G wireless bands in automated Industry 4.0 settings. We demonstrate the remarkable robustness and adaptability of WISVA through its superior performance in spatial field reconstruction tasks, its validation, and, critically, its ability to extrapolate to entirely unseen warehouse layouts and configurations. Comparative analysis via reconstruction error heatmaps reveals WISVA's significantly higher accuracy against traditional autoencoders, establishing its potential as a critical enabler for efficient wireless infrastructure optimization in Industry 4.0.

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