RRAM circuit-enabled nonlinear precoding and bit precision analysis

[2608.15216] RRAM circuit-enabled nonlinear precoding and bit precision analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.15216 (eess) [Submitted on 15 Aug 2026] Title: RRAM circuit-enabled nonlinear precoding and bit precision analysis Authors: Yuhao Zhang , Haifan Yin , Tao Wang , Jindiao Huang , Kewei Zhu View a PDF of the paper titled RRAM circuit-enabled nonlinear precoding and bit precision analysis, by Yuhao Zhang and 4 other authors View PDF HTML (experimental) Abstract: The rising number of users and antennas imposes exponentially growing computational loads on future communication systems. Yet conventional processors are facing a bottleneck for their nature of memory-computing separation. In-memory computing (IMC) emerges as a promising solution leveraging its intrinsic high parallelism. This work proposes an IMC architecture that employs resistive random access memory (RRAM) to reduce the computational complexity of the nonlinear Tomlinson-Harashima precoding (THP) to a linear scale. We present a computation-constraint principle for designing RRAM circuits to perform nonlinear matrix operations and construct an LQ decomposition RRAM circuit. Since the conductance of memristor is generally quantized, we perform the bit precision analysis and derive the lower bound of the Signal-to-Interference-plus-Noise Ratio (SINR) and achievable rate. Our analysis indicates that at a high Signal-to-Noise Ratio (SNR) or with a large number of antennas, each 1-bit precision increase yields 6 dB SINR gain and linear rate growth. For practical implementation, we derive the optimal bit precision to sustain SINR performance under varying system configurations. Simulation demonstrates the feasibility and accuracy of the RRAM-based circuit and our theoretical analysis. Our work proves that RRAM-based IMC holds significant potential for high-complexity nonlinear precoding, addressing the escalating computational demands for future communications. Comments: 15 pages, 13 figures. Accepted for publication in IEEE Transactions on Communications Subjects: Signal Processing (eess.SP) Cite as: arXiv:2608.15216 [eess.SP] (or arXiv:2608.15216v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.15216 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuhao Zhang [ view email ] [v1] Sat, 15 Aug 2026 13:07:39 UTC (990 KB) Full-text links: Access Paper: View a PDF of the paper titled RRAM circuit-enabled nonlinear precoding and bit precision analysis, by Yuhao Zhang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs 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 . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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