Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2504.13363

A Tutorial on AI-Empowered Integrated Sensing and Communications

Mojtaba Vaezi·Gayan Aruma Baduge·Esa Ollila·Sergiy A. Vorobyov
View original source ↗Read source document ↗Export metadata ↓

Abstract

Integrating sensing and communication (ISAC) can help overcome the challenges of limited spectrum and expensive hardware, leading to improved energy and cost efficiency. While full cooperation between sensing and communication can result in significant performance gains, achieving optimal performance requires efficient designs of unified waveforms and beamformers for joint sensing and communication. Sophisticated statistical signal processing and multi-objective optimization techniques are necessary to balance the competing design requirements of joint sensing and communication tasks. As model-based approaches can be suboptimal or too complex, deep learning offers a powerful data-driven alternative, especially when optimal algorithms are unknown or impractical for real-time use. Unified waveform and beamformer design problems for ISAC fall into this category, where fundamental design trade-offs exist between sensing and communication performance metrics, and the underlying models may be inadequate or incomplete. This tutorial paper explores the application of artificial intelligence (AI) to enhance efficiency or reduce complexity in ISAC designs. We emphasize the integration benefits through AI-driven ISAC designs, prioritizing the development of unified waveforms, constellations, and beamforming strategies for both sensing and communication. To illustrate the practical potential of AI-driven ISAC, we present three case studies on waveform, beamforming, and constellation design, demonstrating how unsupervised learning and neural network-based optimization can effectively balance performance, complexity, and implementation constraints.

Explore related subjects

cs.ITeess.SPmath.IT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mojtaba Vaezi, Gayan Aruma Baduge, Esa Ollila, Sergiy A. Vorobyov. 2026-02-13. A Tutorial on AI-Empowered Integrated Sensing and Communications. https://arxiv.org/abs/2504.13363

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

KEEP EXPLORING

Related papers

arXiv2026

Beyond the "G" Frontier: A Time Traveler's Century-Long Vision for Wireless Intelligence

This article travels one century into the future--from 2025 to 2125--through the analytical lens of the Information--Curvature Efficiency Law (ICEL), an organizing ansatz that reframes wireless capacity around the curvature of the information manifold. It contends that wireless evolution will not proceed through incremental generations such as 6G or 7G, but through a curvature-managed integration of electromagnetics, biology, and thermodynamics. The technical instantiation of ICEL for phase-coded continuous apertures--where curvature is realized as the affine-quotient second derivative of the aperture phase, with a compact synthesis operator and a Fredholm-determinant capacity--is developed rigorously in a companion theory paper and stress-tested against SVD, Fourier, Zernike-like, matched-focus, and RIS baselines in a companion benchmark paper. The present essay supplies the physical intuition, the century-scale narrative, and a set of cross-domain extensions (biology, thermodynamics, ecology) that are explicitly labeled as illustrative extrapolations, not independent derivations.

cs.IT↗
arXiv2026

New lower bounds for kissing numbers in dimensions $25$--$31$

The kissing number in dimension $d$ is the largest number of non-overlapping congruent spheres that can simultaneously touch a central sphere of the same size. We study dimensions $25$-$31$, where the best previous constructions are based on Leech lifting from the optimal kissing configuration in dimension $24$. Our method exploits the absence of contacts between the unlifted bulk and the block consisting of lifted and auxiliary vectors. Rotating this block while keeping the bulk fixed creates room for two antipodal points in dimensions $26$, $27$, and $28$, and one point in dimension $29$. Three further modifications yield improvements in dimensions $25$, $30$ and $31$: (a) a nonorthogonal diagonal linear deformation of the lifted block admits two antipodal points in dimension $25$; (b) rotating the additional coordinates of the lifted vectors and then applying a small orthogonal transformation to the resulting lifted block as a whole admits two antipodal points in dimension $30$; (c) rotating only the additional coordinates of the lifted vectors admits four nonantipodal points in dimension $31$. Together, these constructions yield the new lower bounds $τ_{25}\geq 197058$, $τ_{26}\geq 198552$, $τ_{27}\geq 200046$, $τ_{28}\geq 204522$, $τ_{29}\geq 209497$, $τ_{30}\ge 220442$, and $τ_{31}\geq 238354$.

cs.IT↗
arXiv2026

Minimum distances of primitive narrow-sense BCH codes via good zero-sets

Determining the exact minimum distances of BCH codes remains a open problem. We establish the minimum distances of several families of primitive narrow-sense BCH codes, showing that they attain their designed distances. Our approach centers on $\mathbb{F}_q$-good zero-sets, which we introduce through a derivative condition on their vanishing polynomials. We show that a $q$-ary primitive narrow-sense BCH code of length $q^m-1$ and designed distance $2\leqδ\leq q^m-1$ has minimum distance $δ$ if and only if there exists an $\mathbb{F}_q$-good zero-set of cardinality $δ+1$ in the finite field $\mathbb{F}_{q^m}$ with $q^m$ elements. To construct $\mathbb{F}_q$-good zero-sets, we develop several methods based on polynomial substitutions, power maps, and shifted inverses, as well as direct constructions using polynomials of special forms. Together with suitable initial $\mathbb{F}_q$-good zero-sets, including those arising from known minimum-distance results, these methods yield new good zero-sets of various cardinalities and hence families of primitive narrow-sense BCH codes whose minimum distances equal their designed distances. These families cover a broad range of designed distances, with several known minimum-distance results recovered as special cases.

cs.IT↗