Auto-correlation Function Keying

Computer Science > Information Theory [Submitted on 16 Jun 2026] Title:Auto-correlation Function Keying View PDFAbstract:Communication-centric ISAC is a promising paradigm for future 6G networks, in which data payload signals are expected to be reused for sensing to enhance time-frequency resource efficiency. For random payload signals, existing studies have mainly characterized the expected sidelobe level (ESL) of the periodic auto-correlation function (P-ACF). However, ESL only captures the average sidelobe behavior and does not control large spurious sidelobe peaks in individual payload realizations, which may deteriorate weak-target detection performance. This motivates the design of information-bearing signals whose random P-ACF satisfies stringent peak sidelobe level (PSL) constraints. In this paper, we formulate a mutual information maximization problem under PSL constraints and a power budget. For quasi-static frequency-flat channels, we show that a continuous auto-correlation function (ACF)-domain uniform construction provides an asymptotically optimal high-SNR design principle. Motivated by this principle, we propose auto-correlation function keying (ACFK), a finite-constellation modulation architecture that embeds data symbols directly onto the ACF-domain sidelobes. ACFK enables exact control of the nominal P-ACF, which coincides with the actual P-ACF when a spectral non-negativity constraint is met. When this is not the case, we quantify the non-negativity violation probability and bound the resulting peak sidelobe level ratio (PSLR) degradation. We further provide a reference ISAC transceiver design for ACFK over quasi-static multipath channels, together with high-SNR approximate BER analysis. Numerical results validate the theoretical analysis and show that, compared with a generalized probabilistic amplitude shaping (PAS) baseline, ACFK provides substantially stronger PSLR control and improved weak-target detection performance under comparable sensing and communication settings. Current browse context: cs.IT References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.