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Spectrally efficient digitized radio-over-fiber system with k-means clustering-based multidimensional quantization.
KTH. Shanghai Jiao Tong University, Shanghai, China.
KTH. RISE Acreo AB, Networking & Transmiss Lab, S-16425 Kista, Sweden.ORCID iD: 0000-0003-4906-1704
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2018 (English)In: Optics Letters, ISSN 0146-9592, E-ISSN 1539-4794, Vol. 43, no 7, p. 1546-1549Article in journal (Refereed) Published
Abstract [en]

We propose a spectrally efficient digitized radio-over-fiber (D-RoF) system by grouping highly correlated neighboring samples of the analog signals into multidimensional vectors, where the k-means clustering algorithm is adopted for adaptive quantization. A 30  Gbit/s D-RoF system is experimentally demonstrated to validate the proposed scheme, reporting a carrier aggregation of up to 40 100 MHz orthogonal frequency division multiplexing (OFDM) channels with quadrate amplitude modulation (QAM) order of 4 and an aggregation of 10 100 MHz OFDM channels with a QAM order of 16384. The equivalent common public radio interface rates from 37 to 150  Gbit/s are supported. Besides, the error vector magnitude (EVM) of 8% is achieved with the number of quantization bits of 4, and the EVM can be further reduced to 1% by increasing the number of quantization bits to 7. Compared with conventional pulse coding modulation-based D-RoF systems, the proposed D-RoF system improves the signal-to-noise-ratio up to ∼9  dB and greatly reduces the EVM, given the same number of quantization bits.

Place, publisher, year, edition, pages
Optical Society of America, 2018. Vol. 43, no 7, p. 1546-1549
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-226866DOI: 10.1364/OL.43.001546ISI: 000428941300034PubMedID: 29601026Scopus ID: 2-s2.0-85044969907OAI: oai:DiVA.org:kth-226866DiVA, id: diva2:1201665
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research VINNOVA, C2015/3-5 SENDATEFICUS
Note

QC 20180507

Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2024-03-15Bibliographically approved

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Zhang, LuPang, XiaodanPopov, SergeiChen, Jiajia

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CiteExportLink to record
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  • apa
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