Performance Optimization of Short Reach Optical Interconnects based on Direct Detection

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.

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Cite

BibTeX RIS

BibTeX

@misc{poi2026performance,
  title = {{Performance Optimization of Short Reach Optical Interconnects based on Direct Detection}},
  author = {Luca Poì and Stella Civelli and Marco Secondini and Li Zhang and Dario Cellini and Asfand Nizamani and Ramin Solaimani and Lorenzo De Marinis and Mareli Rodigheri and Pantea Nadimi Goki and Muhammad A. Naz and Giampiero Contestabile and Enrico Forestieri and Fabio Cavaliere},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.28129},
  eprint = {2608.28129},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.28129},
  note = {CC0},
}

RIS

TY  - UNPB
TI  - Performance Optimization of Short Reach Optical Interconnects based on Direct Detection
AU  - Luca Poì
AU  - Stella Civelli
AU  - Marco Secondini
AU  - Li Zhang
AU  - Dario Cellini
AU  - Asfand Nizamani
AU  - Ramin Solaimani
AU  - Lorenzo De Marinis
AU  - Mareli Rodigheri
AU  - Pantea Nadimi Goki
AU  - Muhammad A. Naz
AU  - Giampiero Contestabile
AU  - Enrico Forestieri
AU  - Fabio Cavaliere
PY  - 2026
DA  - 2026/08/28
PB  - arXiv
DO  - 10.48550/arxiv.2608.28129
UR  - https://arxiv.org/abs/2608.28129
AB  - 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.
ER  -

Abstract

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