Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.23420

Pilot-Based Sensing With OFDM and AFDM: A Resource-Matched Performance Comparison

Siyi Tang·Yin Xu·Aimin Tang·Dazhi He·Wenjun Zhang
View original source ↗Read source document ↗Export metadata ↓

Abstract

As a waveform candidate for next-generation wireless systems, affine frequency division multiplexing (AFDM) has often been compared with orthogonal frequency division multiplexing (OFDM) under different configurations, while a systematic comparison of their pilot-based sensing performance remains lacking. This paper establishes a comparison with the same bandwidth, frame duration, pilot energy, and reserved-resource overhead. OFDM-random, OFDM-fixed-comb, and OFDM-shifted-comb pilots are compared with AFDM pilots surrounded by guard resources using aperiodic ambiguity functions without cyclic prefix/chirp-periodic prefix (CP/CPP) and cyclic ambiguity functions with CP/CPP, followed by target detection experiments. The results show that OFDM-fixed-comb exhibits low range sidelobes but periodic range ambiguity peaks, while AFDM and OFDM-shifted-comb effectively suppress zero-Doppler range ambiguities. Within the common unambiguous region, all pilot configurations for both waveforms exhibit similar detection thresholds in strong-target detection, while structured OFDM and AFDM pilot placements outperform OFDM-random in weak-target detection. At high mobility, the AFDM receiver using the discrete affine Fourier transform (DAFT) can resolve integer Doppler beyond the conventional slow-time maximum unambiguous velocity, outperforming the tested conventional OFDM and AFDM receivers at the increased search complexity. These results separate the effects of waveform, pilot placement, and receiver processing, providing a basis for AFDM and OFDM pilot-based sensing design.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Siyi Tang, Yin Xu, Aimin Tang, Dazhi He, Wenjun Zhang. 2026-10-16. Pilot-Based Sensing With OFDM and AFDM: A Resource-Matched Performance Comparison. https://arxiv.org/abs/2610.23420

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

KEEP EXPLORING

Related papers

arXiv2026

Integrated sensing and communications in the 3GPP New Radio: sensing limits

Integrated Sensing and Communications (ISAC) is regarded as a key element of the beyond-fifth-generation (5G) and sixth-generation (6G) systems, raising the question of whether current 5G New Radio (NR) signal structures can meet the sensing accuracy requirements specified by the Third Generation Partnership Project (3GPP). This paper addresses this issue by analyzing the fundamental limits of range and velocity estimation through the Cramér-Rao lower bound (CRLB) for a monostatic unmanned aerial vehicle (UAV) sensing use case currently under consideration in the 3GPP standardization process. The study focuses on standardized signals and also evaluates the potential performance gains achievable with reference signals specifically designed for sensing purposes. The compact CRLB expressions derived in this work highlight the fundamental trade-offs between estimation accuracy and system parameters. The results further indicate that information from multiple slots must be exploited in the estimation process to attain the performance targets defined by the 3GPP. As a result, the 5G NR positioning reference signal (PRS), whose patterns may be suboptimal for velocity estimation when using single-slot resources, becomes suitable when multislot estimation is employed. Finally, we propose a two-step iterative range and radial-velocity estimator that attains the CRLB over a significantly wider range of distances than conventional maximum-likelihood (ML) estimators, for which the well-known threshold effect severely limits the distance range over which the accuracy requirements imposed by the 3GPP are satisfied.

eess.SP↗
arXiv2026

Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction

Data representation is a fundamental issue in deep learning. However, as wireless data scales and deeply couples across many physical domains such as time, space, and frequency, existing wireless artificial intelligence (AI) technologies lack dedicated representation solutions. Instead, they mainly rely on stitching general-purpose networks, a tool-driven paradigm that inevitably results in structural redundancy and bottlenecks in information flow. To fill this gap, this paper proposes Coupler, a wireless native-AI neural backbone designed for representation learning of channel state information (CSI)--the pivotal data in wireless systems. Leveraging the revealed physical insights of channel tensors, Coupler decomposes representation learning into individual domains on a layer-by-layer basis, and then couples the learned domain-specific features through a dimension-staggered cascade. This full-domain interleaved learning architecture enables superior parameter efficiency and fine-grained multi-domain feature fusion. Based on this backbone, we use the complex-domain multilayer perceptrons (CMLPs) as spatial and frequency domain learners, while employing three optional mechanisms--convolution, attention, or gating--to capture temporal dependencies. This results in a series of efficient channel learning schemes with diverse functionalities and extreme lightweights, showcasing the compactness, versatility and flexibility of Coupler. We evaluate these schemes on channel deduction, a general representation task encompassing channel estimation, interpolation, prediction, and feedback. Extensive experimental evaluations validate their significant performance gains and robust applicability even for real-world measured data, demonstrating the potential of Coupler as a promising basic architecture in the design of wireless foundation models.

eess.SP↗
arXiv2026

Advances in Wavelet Denoising for Communication Signals: From Parameter Selection Toward Data-Driven Optimization

Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but efficacy depends on coupled choices of transform, mother wavelet, decomposition level, thresholding rule, and shrinkage function. Pairing communication experiments with a PRISMA-aligned synthesis of 2020-2025 studies from ten sources, coded by parameter focus and domain (biomedical, industrial, geophysical, communication), this review traces a shift from defaults to similarity-, sparsity-, entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, but finds communication evidence scarce. We benchmark DWT, SWT, and WPT on an impulsive-noise OFDM channel under SNR gain, MSE, BER, EVM, and real-time feasibility, with Friedman and Wilcoxon tests, efficiency-index ranking, and assessment of embedded DSP/FPGA constraints. As waveform-fidelity gains need not improve hard-decision performance, the Unified Decision Framework prioritises receiver-level validation over fidelity surrogates, validated on synthetic pilot-aided OFDM and measured IEEE 802.11n channels. On the synthetic link, the configuration attains the lowest channel-estimation NMSE, significantly outperforms a fixed-parameter wavelet $(p < 10^{-11})$, and is statistically indistinguishable from the strongest classical baseline in BER; on measured channels it attains the lowest BER and EVM. The supported claim is the lowest estimation error at statistically equivalent bit-error performance, and it is conditional on the disturbance being structured or impulsive, since under AWGN alone wavelet thresholding yields no systematic BER improvement. Within these limits the framework generalises to held-out data and adapts to the observed channel, recasting wavelet denoising into a reproducible, communication-aware design methodology extensible to deep-unfolding architectures.

eess.SP↗