Search arXiv⌕ Search

arXiv · 2609.36745

Efficient Transmit Waveform Design for MIMO-OFDM DFRC Systems with 1-Bit DACS

Abstract

This paper studies efficient waveform design for MIMO-OFDM dual-functional radar-communication (DFRC) systems with 1-bit digital-to-analog converters (DACs), which remains underexplored in the literature. We formulate the DFRC waveform design problem by maximizing the trace of the target angular Fisher information matrix (FIM), subject to symbol-error probability constraints realized via constructive interference (CI) and a time-domain 1-bit transmit alphabet constraint. The resulting problem is a large-scale nonlinear integer programming problem. We develop an inexact alternating direction method of multipliers (ADMM) algorithm with alternating time- and frequency-domain updates. The proposed scheme enables subcarrier-wise optimization in the frequency domain and closed-form primal and dual updates in the time domain. In particular, the CI constraints are deliberately retained in both the time- and frequency-domain subproblems to promote the feasibility of intermediate iterates and improve the convergence behavior of ADMM. Simulation results demonstrate promising DFRC performance.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chenfei Huang, Mingjie Shao, Ya-Feng Liu. 2026-09-29. Efficient Transmit Waveform Design for MIMO-OFDM DFRC Systems with 1-Bit DACS. https://arxiv.org/abs/2609.36745

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

KEEP EXPLORING

Related papers

The Symmetric Location Problem: a Song of Efficiency and Robustness

The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter $\boldsymbolθ \in Θ\subseteq \mathbb{R}^p$ in the presence of an \textit{infinite-dimensional nuisance parameter} $g \in \mathcal{S}$ (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.

eess.SP↗

Single-Voxel Wireless NeRF for Spatial Spectrum Prediction

Wireless channel measurements across multiple spatial directions are crucial for AI-driven applications, such as RF digital twins and integrated communication and sensing. However, collecting channel data across large scenes is labor-intensive. Wireless NeRFs address this challenge by learning propagation behavior from sparse measurements and synthesizing channel spatial spectrum magnitude at unseen locations. However, existing wireless NeRFs inherit dense volumetric sampling from vision NeRFs, which requires substantial computation. This paper asks whether such dense sampling is necessary for predicting magnitudes of the wireless spatial spectrum. We empirically show that wireless NeRFs are over-parameterized for this task and introduce SV-INGP, a sparse volumetric sampling variant of Instant Neural Graphics Primitives (INGP). Across real-world and simulated datasets, SV-INGP matches the median Structural Similarity Index Measure (SSIM) of the NeRF2 baseline while reducing training time by 184x. These results generalize across LoS and NLoS scenes, sub-6 and millimeter-wave frequencies, and antenna array tapering configurations, suggesting a simpler and more efficient design path for RF digital twins

eess.SP↗

Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts, volume conduction, low signal-to-noise ratio, and substantial inter-subject variability. This chapter provides a practical and methodological guide to modern EEG analysis, spanning EEG preprocessing, artifact removal, filtering, bad-channel detection and interpolation, re-referencing, independent component analysis (ICA), and preprocessing of simultaneous EEG-fMRI recordings. We review major approaches for computational EEG analysis, including event-related potentials (ERPs), time-frequency analysis, functional and effective connectivity, source localization, multivariate decoding, permutation testing, and multiple-comparison correction. We then examine machine-learning methods for EEG, from feature-based classifiers to deep learning and emerging EEG foundation models, with emphasis on cross-subject generalization, limited-data regimes, data leakage, evaluation metrics, and fair benchmarking. Reproducibility is treated as a core requirement throughout, including transparent preprocessing, BIDS-EEG data organization, standardized derivatives, preservation of raw data, and FAIR data practices. The chapter is intended as a practical reference for researchers developing reliable, interpretable, and reproducible EEG analysis and machine-learning pipelines.

eess.SP↗