{
  "abstract": "Short-reach optical interconnects are evolving toward data rates beyond 400 Gb/s per lane, driven by the bandwidth and energy-efficiency requirements of AI-enabled datacenter networks. At these operating speeds, channel impairments, device nonlinearities, and hardware constraints limit the effectiveness of conventional transceiver design and digital signal processing (DSP). This paper presents a unified framework for the optimization of direct-detection optical interconnects, encompassing digital surrogate modeling, receiver-side DSP optimization, and end-to-end (E2E) transceiver learning. The proposed formulation provides a common perspective for model-based and machine-learning-based approaches, including linear and nonlinear equalization, lookup tables, decision trees, neural-network receivers, and E2E optimization. Their performance is discussed together with computational complexity and hardware implementation aspects, highlighting the associated trade-offs. We show, through simulations and experimental validations, that for a 40~Gb/s 10~km link, decision trees can outperform by 0.5--1~dB conventional linear equalization, with negligible hardware requirements. Moreover, we show that an E2E technique based on transformers can provide a gain up to 6~dB, highlighting the potential of joint transceiver optimization to improve the performance of next-generation short-reach optical links.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.28129",
  "author_total": 14,
  "authors": [
    {
      "name": "Luca Poì"
    },
    {
      "name": "Stella Civelli"
    },
    {
      "name": "Marco Secondini"
    },
    {
      "name": "Li Zhang"
    },
    {
      "name": "Dario Cellini"
    },
    {
      "name": "Asfand Nizamani"
    },
    {
      "name": "Ramin Solaimani"
    },
    {
      "name": "Lorenzo De Marinis"
    },
    {
      "name": "Mareli Rodigheri"
    },
    {
      "name": "Pantea Nadimi Goki"
    },
    {
      "name": "Muhammad A. Naz"
    },
    {
      "name": "Giampiero Contestabile"
    },
    {
      "name": "Enrico Forestieri"
    },
    {
      "name": "Fabio Cavaliere"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/22rwcy80dwf2z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.28129",
  "fulltext_url": null,
  "id": "22rwcy80dwf2z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.28129",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "2208|Optical Network Technologies"
    ]
  ],
  "openalex": "W7204819263",
  "pdf_url": "https://arxiv.org/pdf/2608.28129",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/22rwcy80dwf2z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "2608.28129",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.28129"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Performance Optimization of Short Reach Optical Interconnects based on Direct Detection",
  "title_text": "Performance Optimization of Short Reach Optical Interconnects based on Direct Detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/22rwcy80dwf2z",
  "venue": "arXiv",
  "versions": []
}