arXiv subjects

Ashish Sheikh

Publications and source records attributed to Ashish Sheikh.

3 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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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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