Experimental Demonstrations of Hybrid OFDM-Digital Filter Multiple Access PONs

Jin, Wei; Zhong, Zhuqiang; He, Jiaxiang; Sankoh, Abdulai; Giddings, R. P.; Hong, Yanhua; Pierce, I.; O’Sullivan, Maurice; Laperle, Charles; Lee, J.; Mariani, Giordano; Durrant, Tim; Tang, J. M.

doi:10.1109/lpt.2020.2995072

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…From results, it can be observed that due to the robustness of the deep neural network it can tackle the ISI effectively and it can easily obtain the channel state information based on the available training data. [28][29][30] The deep neural network's performance is like the performance of the maximum likelihood estimator when the channel conditions are ideal, i.e., perfectly estimated channel and no ISI. However, the system performance is not acceptable for fading channel conditions and it indicates performance degradation encountered by the deep neural network during the calculation of the phase shift.…”

Section: Simulation Resultsmentioning

confidence: 99%

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Pandya

Wakchaure²,

Shankar³

et al. 2021

Journal of Defense Modeling & Simulation

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In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.

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Section: Simulation Resultsmentioning

confidence: 99%

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Pandya

Wakchaure²,

Shankar³

et al. 2021

Journal of Defense Modeling & Simulation

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show abstract

“…7. It can be observed that for all the transmission channels, when the digital filter lengths are is less than 32, the BERs of for most of the channels, their BERs are larger than the FEC limit, because of short digital filter length-induced increase in digital filter frequency response ripples and power leakages [28]. While the BERs performances become almost flat when the digital filter length varies from 64 to 128.…”

Section: A Experimental Setup and Parametersmentioning

confidence: 95%

“…Following the AWGs, two transmitters (Thorlabs MX35D) each incorporating with a 15kHz linewidth laser sources and a 35GHz lithium niobate (LiNbO3) Mach-Zehnder modulators inside are employed to perform realize electrical-to-optical conversion. The ONU central wavelength spacing is set to be ~100GHz to minimize the optical beating interference (OBI) effect [28]. After passing through the EDFAs, 0.8nm bandwidth tunable optical filters (TOF), optical circulators and 1km standard single mode fibers (SSMFs), the two ONUs' optical signals with similar optical powers of ~6dBm are passively coupled in the remote node.…”

Section: A Experimental Setup and Parametersmentioning

confidence: 99%

Concurrent Inter-ONU Communications for Next Generation Mobile Fronthauls based on IMDD Hybrid SSB OFDM-DFMA PONs

Zhong¹,

Jin²,

Jiang³

et al. 2021

J. Lightwave Technol.

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In PON-based mobile fronthauls, direct inter-ONU communications without passing end-user traffic to the OLT offer a promising solution for fulfilling thethe stringent latency and bandwidth requirements of of 5G and beyond networks. In this paper, with slight modifications to at the PON remote node, a concurrent inter-ONU and upstream communication technique is proposed and experimentally demonstrated in 101.6Gbit/s multipoint-to-point hybrid SSB OFDM -digital filter multiple access (DFMA) IMDD PONs over at a distance of 25km SSMFs. Multiple gapless inter-ONU and upstream SSB signals are aggregated by digital orthogonal filtering in each ONU transmitter. A single FFT operation is applied for demultiplex and demodulation in the OLT/ONU receivers. The results show that for both the inter-ONU and upstream transmissions, the optimum length of optimal digital filters is is 32, based on which the power penalties due to the fiber transmission and ONU channel interference are <1dB and <2dB, respectively. For the inter-ONU communications, adaptive RF spectral assignments can effectively mitigate the Rayleigh and Brillouin backscattering effects and the upstream channel fading effect, thus giving rise to >30% improvements in aggregated signal transmission capacity. In addition, detailed experimental investigations are also undertaken of the trade-off between differential ONU optical launch power dynamic range and aggregated signal transmission capacity. An approximately 1dB increase in ONU launch power dynamic range is achievable by reducing the aggregated signal transmission capacity by 5Gbit/s.

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“…Similar to the treatment adopted in [ 7 ], the utilization of IM as the preferred light source in the numerical simulations is to completely eliminate the signal–signal beating interference (SSBI) effect [ 23 ]. It is noteworthy that, for practical implementation of the proposed PONs, different ONUs can use different wavelengths to transmit their OFDM sub-bands, provided that every two adjacent wavelengths have a minimum wavelength space of ~0.28 nm to effectively mitigate the SSBI effects [ 24 ].…”

Section: Principle Of Dft-spread Spectrally Overlapped Hybrid Ofdm–dfma Ponsmentioning

confidence: 99%

“…transmit their OFDM sub-bands, provided that every two adjacent wavelengths have a minimum wavelength space of ~0.28 nm to effectively mitigate the SSBI effects [24].…”

Section: Principle Of Dft-spread Spectrally Overlapped Hybrid Ofdm-dfma Ponsmentioning

confidence: 99%

DFT-Spread Spectrally Overlapped Hybrid OFDM–Digital Filter Multiple Access IMDD PONs

Sankoh¹,

Jin²,

Zhong³

et al. 2021

Sensors

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A novel transmission technique—namely, a DFT-spread spectrally overlapped hybrid OFDM–digital filter multiple access (DFMA) PON based on intensity modulation and direct detection (IMDD)—is here proposed by employing the discrete Fourier transform (DFT)-spread technique in each optical network unit (ONU) and the optical line terminal (OLT). Detailed numerical simulations are carried out to identify optimal ONU transceiver parameters and explore their maximum achievable upstream transmission performances on the IMDD PON systems. The results show that the DFT-spread technique in the proposed PON is effective in enhancing the upstream transmission performance to its maximum potential, whilst still maintaining all of the salient features associated with previously reported PONs. Compared with previously reported PONs excluding DFT-spread, a significant peak-to-average power ratio (PAPR) reduction of over 2 dB is achieved, leading to a 1 dB reduction in the optimal signal clipping ratio (CR). As a direct consequence of the PAPR reduction, the proposed PON has excellent tolerance to reduced digital-to-analogue converter/analogue-to-digital converter (DAC/ADC) bit resolution, and can therefore ensure the utilization of a minimum DAC/ADC resolution of only 6 bits at the forward error correction (FEC) limit (1 × 10−3). In addition, the proposed PON can improve the upstream power budget by >1.4 dB and increase the aggregate upstream signal transmission rate by up to 10% without degrading nonlinearity tolerances.

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Experimental Demonstrations of Hybrid OFDM-Digital Filter Multiple Access PONs

Cited by 11 publications

References 13 publications

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Analysis of NOMA-OFDM 5G wireless system using deep neural network

Concurrent Inter-ONU Communications for Next Generation Mobile Fronthauls based on IMDD Hybrid SSB OFDM-DFMA PONs

DFT-Spread Spectrally Overlapped Hybrid OFDM–Digital Filter Multiple Access IMDD PONs

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