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Record W3086857118 · doi:10.1364/cleo_si.2020.sth3l.2

Experimental Demonstration of Hybrid OFDM-Digital Filter Multiple Access PONs for 5G and Beyond Networks

2020· article· en· W3086857118 on OpenAlexaff
Wei Jin, Zhuqiang Zhong, Yixian Dong, Jiaxiang He, Leyan Li, Abdulai Sankoh, Su Hu, R. P. Giddings, Yanhua Hong, Maurice O’Sullivan, J. Lee, Tim Durrant, Jianming Tang

Bibliographic record

VenueConference on Lasers and Electro-Optics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsCiena (Canada)
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingPassive optical networkElectronic engineeringComputer scienceOptical line terminationDigital signal processingMultiplexingAccess networkRobustness (evolution)EngineeringTelecommunicationsWavelength-division multiplexingChannel (broadcasting)WavelengthOptics

Abstract

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Aggregated 16.6Gbit/s@26km upstream hybrid OFDM-DFMA IMDD PONs utilizing low-cost optical/electrical devices are demonstrated, for the first time, which significantly improve the performance robustness and differential ONU optical launch power dynamic range, compared to published work. © 2020 The Author(s) OCIS code: (060.0060) Fiber optics and optical communications; (060.4250) Networks; 1. Introduction To realize the seamless convergence of the existing optical metro/access networks and mobile front-haul/mid- haul/back-haul networks for 5G and beyond networks, recently, a hybrid orthogonal frequency division multiplexing-digital filter multiple access (OFDM-DFMA) PON has been proposed and theoretically investigated [1,2], where for upstream signal transmission, various digital in-phase (I) shaping filters (SFs) are employed in the ONUs to dynamically locate their OFDM signals at the required sub-wavelengths. Whilst in the OLT, the OFDM signals from various ONUs are de-multiplexed and demodulated simultaneously by a single FFT operation without utilizing digital matching filters. Compared with the previously reported DFMA PON [3-5], its unique advantages are summarized as follows: 1) >100-fold reduction in the overall OLT DSP complexity when accommodating 36 ONUs [6], 2) considerable relaxation of ONU-embedded digital SF DSP complexity requirements for achieving specific transmission performances, 3) significant improvements in upstream system power budget and upstream performance robustness against both component/system impairments and channel interferences, 4) inherent transparency to existing OFDM-based 4G networks, and 5) excellent transparency to ONU design parameters. In this paper, experimental explorations of the more challenging upstream signal transmission performance of the hybrid OFDM-DFMA PONs are reported, for the first time, utilizing off-the-shelf low-cost optical/electrical devices. The experimentally measured results confirm our numerical predictions, and more importantly, reveal that the proposed PON not only has improved upstream performance robustness against transmission system impairments and channel interferences, but also possesses a large differential ONU optical launch power dynamic range (Diff- ONU OLPDR), compared to the previously published work [4]. 2. Experimental setup and measured upstream transmission performances Based on the hybrid OFDM-DFMA PON operating principle explicitly described in [1], the experimental setup of a representative two-channel upstream hybrid OFDM-DFMA PON is illustrated in Fig. 1. In the transmitter, two independent digitally-filtered analogue OFDM signals are produced by a dual-channel AWG (Keysight M8195A) operating at 30GS/s@8-bit. In the AWG-embedded Matlab program, two real OFDM signals are first generated and Fig. 1 Experimental upstream setup of the IMDD hybrid OFDM-DFMA PON without utilizing digital matching filters. Fig. 2 (a) Back-to-back and 26km SSMF transmission performance, (b) impact of channel interference on transmission performance and (c) differential ONU optical launch power dynamic range. then digitally up-sampled by a factor of M=4 (4 1), and finally digitally filtered by two in-phase SFs. The adopted SFs are constructed by using a Hilbert-pair approach [1]. The key parameters of the OFDM signals and the digital filters are listed in Table I. Prior to digital-to-analogue conversion of each OFDM signal, a digital-domain time delay operation is applied to adjust the ONU signal timing for synchronization and an extra 1.5* oversampling operation is also performed for each digitally-filtered OFDM signal. As such, for each analogue signal produced, its signal bandwidth/bitrate is 5GHz/8.3Gbit/s, which gives rise to an overall upstream signal transmission bandwidth of 10GHz and an aggregated upstream signal bitrate of 16.6Gbit/s. To perform optical intensity modulation, a 10GHz EML and a 20GHz MZM are used for the low frequency channel (CH-1) and high frequency channel (CH-2) respectively. To alleviate the optical beating interference effect in the OLT, a minimum wavelength space of 0.28nm between these two ONUs is employed. After a 50:50 passive OC, the fiber launch power is 4.7dBm. After 26km SSMF transmission, a 25GHz linear PIN and an electrical amplifier, a digital sampling oscilloscope (DSO) captures and digitalizes the received electrical signal at a sampling speed of 25GS/s, and finally a signal demodulation process is performed off-line, which includes signal resampling [5], signal synchronization, serial-to- parallel conversion (S/P), CP removal, a single 128 (32*4) point FFT operation, signal sideband identification, sideband processing and data recovery. In the signal sideband identification process after the FFT, the 64 subcarriers in the positive frequency bin are classified into two groups each containing 32 subcarriers (corresponding to one OFDM signal). In each group, its 16 low/high frequency subcarriers occupy the lower sideband (LSB)/the upper sideband (USB) of the signal. As the LSB and USB of a specific OFDM signal convey identical data, for each identified OFDM signal, the sideband processing is then applied to improve signal transmission performance, where a conjugate operation is first performed for the USB subcarriers and subsequently a phase compensation operation is then implemented for both the LSB and USB subcarriers, and finally the subcarriers in the LSB and USB of the same signal are directly summed. The upstream transmission performances and the received signal spectra of the considered hybrid OFDM-DFMA PONs before and after upstream transmitting the aggregated 16.6Gbit/s over 26km SSMF are plotted in Fig. 2(a). The results show that the fiber transmission-induced power penalties at BERs of 1*10-3 are < 0.5dB for both channels, indicating that fiber transmission nonlinearities have negligible impacts on the BER performances of different channels. This agrees very well with our theoretical predictions [1]. The channel interference-induced performance degradations are explored in Fig. 2(b), where the BER performances of each channel with the other channel present/omitted in the optical domain are illustrated. As expected from our numerical results [1], the channel interference-induced power penalties are <1dB. Furthermore, by utilizing the system parameter setting similar to that adopted in Fig. 2(a), the Diff-ONU OLPDR [1] over 26km SSMF transmission is examined in Fig. 2(c), where the received optical power is fixed at -3.5dBm. Fig. 2(c) shows that the Diff-ONU OLPDR as large as 6.7dB is obtainable. In addition, it is also worth highlighting the following two aspects: 1) in Fig. 2, the similarities of the observed ONU OLPDRs and the BER performances between these two considered ONUs with different optical intensity modulators suggest that the hybrid OFDM-DFMA PON has excellent robustness against variations in ONU transceiver designs, and 2) all the subcarriers in each channel have very similar EVM performances, as shown in Fig. 2(b), this implies the effectiveness of the sideband processing adopted in the OLT.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.263
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2020
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