Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.33672

Gain Before Tracking: Compensating Power-Amplifier Nonlinearity and Phase Noise in AFDM Uplinks

Mohammad Tasin·Sadman Sakib Rahman
View original source ↗Read source document ↗Export metadata ↓

Abstract

Affine frequency division multiplexing (AFDM) is a candidate waveform for fast-moving vehicles and satellites, but real transmitters add power-amplifier (PA) compression and oscillator phase noise, and no AFDM receiver corrects both. We show that AFDM's transform treats every component equally in magnitude, so the PA's effect reduces to one gain shared by all components and phase noise to a common rotation that OFDM's standard tracker removes. The order matters: the tracker relies on symbol decisions that PA compression corrupts, so the gain should be corrected first. On a six-path, fractional-Doppler channel with 256 subcarriers and a standard-compliant PA back-off, doing so lowers the uncoded bit-error rate by 7.5-12% for AFDM and about 5% for OFDM, and saves 0.5 dB at 10% block error with a rate-3/4 code; the effect is not significant at light back-off. A closed-form gain matches a blind estimate and, without pilots, performs comparably to a pilot-based joint gain estimate that costs 6% overhead. Controls show that AFDM's edge over OFDM is mostly spreading across the channel, not Doppler robustness: single-carrier DFT-s-OFDM matches it at equal back-off.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mohammad Tasin, Sadman Sakib Rahman. 2026-10-24. Gain Before Tracking: Compensating Power-Amplifier Nonlinearity and Phase Noise in AFDM Uplinks. https://arxiv.org/abs/2610.33672

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

KEEP EXPLORING

Related papers

arXiv2026

Hardware-Accelerated Geometrical Simulation of Biological and Engineered In-Air Ultrasonic Systems

The deployment of in-air acoustic sensors for industrial monitoring and autonomous robotics has grown significantly, often drawing inspiration from biological echolocation. However, developing and validating these systems in existing simulation frameworks remains challenging due to the computational cost of simulating high-frequency wave propagation in large, dynamic, and complex environments. While wave-based methods offer high accuracy, they scale poorly with frequency and volume. Conversely, existing geometric acoustic solvers often lack support for dynamic scenes, complex diffraction, or closed-loop robotic integration. In this work, we introduce SonoTraceUE, a high-fidelity acoustic simulation framework built as a plugin for Unreal Engine. By using a hardware-accelerated ray tracing-based specular reflection model, and a curvature-based Monte Carlo diffraction model, the system enables near real-time simulation of active and passive acoustic sensing in dynamic, multi-material environments. We validate the framework through two distinct experimental domains: a bioacoustic study and a robotics experiment. Our results demonstrate that SonoTraceUE achieves high correlation with real-world spectral and spatial data. The framework provides a versatile platform for synthetic data generation, hypothesis testing in bioacoustics, and the rapid prototyping of closed-loop robotic systems that use acoustic sensing.

eess.SP↗
arXiv2026

Active MIMO Sensing: Optimality Conditions and Exploration-Exploitation Tradeoff

This paper develops an active sensing framework for designing the transmit and receive beamformers of a multiple-input multiple-output (MIMO) radar system with a limited number of radio-frequency (RF) chains. In the proposed framework, the transmit and receive beamformers are adaptively designed in each sensing stage based on the measurements made in the previous sensing stages, and the optimization of the beamformers are based on the minimization of the Bayesian Cram{é}r-Rao bound (BCRB) for estimating the unknown sensing parameters. To address the exploration-exploitation tradeoff inherent in such an adaptive procedure, this paper proposes a two-phase design involving two variants of the BCRB optimization problem: an exploration-centric variant that requires multiple orthogonal beamforming directions to be probed in each sensing stage, followed by an exploitation-centric variant that optimizes the per-stage BCRB without such requirement. For both variants, the transmit and receive beamformers are optimized as two subproblems in an alternating fashion in each sensing stage. The alternating optimization is shown to converge to a stationary point of the per-stage BCRB optimization provided that each subproblem is solved to global optimality. A main contribution of this paper is to determine sufficient conditions under which global optimality can be guaranteed---despite that the two subproblems are nonconvex (due to the limited number of RF chains), by studying the structures of their optimal solutions via Lagrangian dual analysis. The conditions pertain to the multiplicity of the eigenvalues of specific direction matrices that can be analytically written in terms of the optimal dual variables. Simulation results demonstrate the advantages of the proposed design framework as compared to state-of-the-art adaptive beamforming strategies.

eess.SP↗
arXiv2026

BFLA: Block-Filtered Long-Context Attention Mechanism

We propose Block-Filtered Long-Context Attention (BFLA), a training-free sparse prefill method that combines deterministic Lightning-style block indexing, query-tile sharing, and length-adaptive Ratio-$K$ selection with block-sparse execution. BFLA evaluates attention using the original Q/K/V, requires no retraining or per-model parameter retuning, and integrates with vLLM while preserving native decoding. For fixed Q/K/V, we bound local attention-output error in terms of the omitted attention mass, indexer-score distortion, retention budget, and query-tile sharing. Evaluations on Gemma4-E4B-IT, Qwen3.6-27B, Qwen3.8-27B, and Qwen3.8-Flash-Next across LongBench-v2, RULER, InfiniteBench, and Terminal Bench 2.1 show BFLA matches or even outperforms dense baselines. BFLA shows up to approximately $4\times$ time-to-first-token and $3.72\times$ end-to-end request speedups over the corresponding dense-prefill baselines. Comparisons with state-of-the-art FlashPrefill V2 and Qwen Sparse Attention also demonstrate that BFLA has better performance and efficiency trade-offs. These findings highlight the value of BFLA for the acceleration of long-context serving. Project website: https://github.com/Alicewithrabbit/BFLA.

eess.SP↗