Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.10997

Flexible Analog Self-Interference Cancellation for Waveform-Robust Monostatic ISAC

Anh Tuyen Le·Xiaojing Huang·J. Andrew Zhang·Peiyuan Qin·Le Chung Tran·Nhan Thanh Nguyen·Y. Jay Guo
View original source ↗Read source document ↗Export metadata ↓

Abstract

Monostatic integrated sensing and communication (ISAC) requires in-band full-duplex operation, where self-interference cancellation (SIC) must suppress the strong transmit leakage while preserving weak sensing echoes. In the analog domain, this task is complicated by transmitter impairments, which generate conjugate and nonlinear SI components, and by waveforms with widely different statistics. This paper develops a flexible analog self-interference cancellation (FASIC) framework for monostatic ISAC based on the widely linear normalized least-mean-square (WL-NLMS) algorithm. FASIC generates the cancellation signal in digital baseband, subtracts it in the radio-frequency domain, and enables flexible sensing-range control via a configurable cancellation span. Its normalized adaptation remains stable across waveforms with substantially different peak-to-average power ratios (PAPRs), for which fixed-step adaptation can become unstable. We further relate transmit-waveform statistics to analog SIC performance and predict the waveform-dependent cancellation floor. Simulations validate the analysis and demonstrate stable analog SIC of approximately $30$--$34$ dB, with the low-PAPR waveforms achieving a $3.3$ dB deeper floor owing to their smaller high-order amplitude moments. Near-field static reflections within the cancellation span are suppressed while delayed and Doppler-shifted echoes are preserved. These results establish FASIC as a waveform-robust analog SIC solution and identify waveform statistics as a key consideration for sixth-generation (6G) waveform selection.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anh Tuyen Le, Xiaojing Huang, J. Andrew Zhang, Peiyuan Qin, Le Chung Tran, Nhan Thanh Nguyen, Y. Jay Guo. 2026-10-07. Flexible Analog Self-Interference Cancellation for Waveform-Robust Monostatic ISAC. https://arxiv.org/abs/2610.10997

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking

We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2, which is collected using a dedicated acquisition platform under diverse operational conditions. All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings. The dataset is systematically organized into seven well-defined subsets corresponding to different operational and signal composition scenarios to facilitate structured experimentation. Each file follows a clearly annotated naming convention to enable convenient data indexing and reproducible analysis. The dataset contains RF recordings from 26 UAV units spanning 8 distinct models, captured across varying flight states, altitudes, speeds, acquisition days, and receiver configurations. By covering diverse acquisition settings and signal compositions, the dataset provides a comprehensive resource for future UAV RF signal research, including RF fingerprinting (RFF) identification, model-level recognition, flight state analysis, time-varying RFF study, and interference-aware signal processing.

eess.SP↗
arXiv2026

Cramer-Rao Bound Analysis of Bistatic ISAC Under Partial Symbol Knowledge and Clutter

Integrated sensing and communication (ISAC) systems rely on communication waveforms to perform sensing tasks, thus making their sensing performance strongly dependent on the level of communication symbol knowledge available to the sensing receivers. However, the existing literature fails to capture this dependency, often relying on assumptions of full symbol knowledge. In this paper, we present a Cramer Rao bound (CRB) analysis of a bistatic ISAC network with heterogeneous uplink and downlink illumination and structured clutter. We consider different symbol knowledge regimes by modeling unknown communication symbols as nuisance parameters. Assuming a temporal evolution of the communication channel, we derive a correlation aware channel estimator and an expression for the UEs uplink spectral efficiency. Numerical results show the CRB degradation induced by clutter and symbol uncertainty and how this can affect resource allocation policies. We also show the performance gain of our channel estimator over conventional block fading architectures.

eess.SP↗
arXiv2026

Dynamic Heartbeat Modeling with Recurrent Neural Networks and Inverse Gaussian Point Process

Heart rate variability (HRV) analysis is important for the assessment of autonomic cardiovascular regulation. The inverse Gaussian process (IGP) has been widely used for beat-to-beat HRV modeling. However, dynamic estimation of the IGP parameters remains a critical challenge. Conventional parametric approaches such as autoregressive models might be insufficient to capture the nonlinear characteristics of R-R series. In this study, we investigated whether recurrent neural networks (RNNs) can be used for IGP parameter identification and thereby enhance probabilistic modeling of R-R dynamics. Four representative RNN architectures, namely, GRU, LSTM, Structured State Space sequence model (S4), and Mamba, were compared and evaluated using the Kolmogorov-Smirnov statistics. The results demonstrate the possibility of combining RNN with the IGP for beat-wise R-R series modeling. This approach provides a flexible basis for probabilistic HRV modeling and for future incorporation of more complex physiological mechanisms and dynamic conditions.

eess.SP↗