Deep-learning-enabled high-performance full-field direct detection with dispersion diversity.

Data center interconnects require cost-effective photonic integrated optical transceivers to meet the ever-increasing capacity demands. Compared with a coherent transmission system, a complex-valued double-sideband (CV-DSB) direct detection (DD) system can minimize the cost of the photonic circuit, since it replaces two stable narrow-linewidth lasers with only a low-cost un-cooled laser in the transmitter while maintaining a similar spectral efficiency. In the carrier-assisted DD system, the carrier power accounts for a large proportion of the total optical signal power. Reducing the carrier to signal power ratio (CSPR) can improve the information-bearing signal power and thus the achievable system performance. To date, the minimum required CSPR is ∼7 dB for all the reported CV-DSB DD systems having electrical bandwidths of approximately half of baud rates. In this paper, we propose a deep-learning-enabled DD (DLEDD) scheme to recover the full optical field of the transmitted signal at a low CSPR of 2 dB in experiment. Our proposal is based on a dispersion-diversity receiver with an electrical bandwidth of ∼61.0% baud rate and a high tolerance to laser wavelength drift. A deep convolutional neural network enables accurate signal recovery in the presence of a strong signal-signal beat interference. Compared with the conventional method, the proposed DLEDD scheme can reduce the optimum CSPR by ∼8 dB, leading to a significant signal-to-noise ratio improvement of ∼5.8 dB according to simulation results. We experimentally demonstrate the optical field reconstruction for a 28-GBaud 16-ary quadrature amplitude modulation signal after 80-km single-mode fiber transmission based on the proposed DLEDD scheme with a 2-dB optimum CSPR. The results show that the proposed DLEDD scheme could offer a high-performance solution for cost-sensitive applications such as data center interconnects, metro networks, and mobile fronthaul systems.

Carrier-assisted differential detection with reduced guard band and high electrical spectral efficiency.

For high-capacity and short-reach applications, carrier-assisted differential detection (CADD) has been proposed, in which the optical field of a complex-valued double sideband (DSB) signal is reconstructed without using a sharp-edge optical bandpass filter or local oscillator laser. The CADD receiver features a transfer function with periodical nulls in the frequency domain, while the signal-signal beat interference (SSBI) is severely amplified around the frequency nulls of the transfer function. Since the null magnitude at the zero frequency is inevitable, a guard band is required between the carrier and the signal, leading to a higher receiver bandwidth and implementation cost. To reduce the needed guard band, we propose a parallel dual delay-based CADD (PDD-CADD), in which an additional delay is placed parallel to the original delay in the conventional CADD. By this means, the modified transfer function has a sharper roll-off edge around the zero frequency. Consequently, the requirement on the guard band can be relaxed, which maximizes the bandwidth utilization of the system. The parallel delay is first optimized through numerical simulation. We then perform a proof-of-concept experiment to transmit a 100-Gb/s orthogonal frequency division multiplexing (OFDM) 16-ary quadrature amplitude modulation (16-QAM) signal over an 80-km single-mode fiber (SMF). After the fiber transmission, the proposed PDD-CADD can reduce the required guard band from 3 to about 1.2 GHz compared with the single delay-based conventional CADD. To our best knowledge, for the direct detection of a single polarization complex-valued DSB signal without using a sharp-roll-off optical filter, we achieve a record electrical spectral efficiency of 5.9 b/s/Hz.

Zero-guard band dual-SSB PAM4 signal transmission with joint equalization scheme.

We propose and experimentally demonstrate a joint equalization scheme to recover a zero-guard band dual-single sideband (dual-SSB) four-level pulse amplitude modulation (PAM4) signal in a system with moderate bandwidth limitation. In the joint equalization scheme, a multiple-input multiple-output feedforward equalizer (MIMO-FFE) is first used to mitigate the residual crosstalk resulting from non-ideal optical filtering. Then, a modified post filter (PF) is placed after the MIMO-FFE to suppress the MIMO-FFE-enhanced low-frequency noise, whereas the known inter-symbol interference introduced by the modified PF is further eliminated with the maximum likelihood sequence estimation algorithm. Based on the proposed scheme, we experimentally demonstrate a 112-Gb/s zero-guard band dual-SSB PAM4 signal transmission over an 80-km single mode fiber with the averaged bit error ratio of two sidebands below 3.8×10-3. We also achieve, to the best of our knowledge, a record electrical spectral efficiency of 7.1 b/s/Hz for single polarization direct detection systems using a dual-SSB PAM4 or dual-SSB 16-quadrature amplitude modulation format.

112-Gb/s SSB 16-QAM signal transmission over 120-km SMF with direct detection using a MIMO-ANN nonlinear equalizer.

We propose and experimentally demonstrate a multiple input multiple output - artificial neural network (MIMO-ANN) nonlinear equalizer (NLE) to process the complex quadrature amplitude modulation (QAM) signal in a single-sideband (SSB) self-coherent detection (SCD) system. In the proposed scheme, a 2-by-2 MIMO structure with two ANNs is employed to effectively mitigate the signal distortions induced by in-phase and quadrature (IQ) imbalance and fiber nonlinear effects. By using the proposed MIMO-ANN NLE, we successfully transmit a 112-Gb/s SSB 16-QAM signal over a single-span 120-km single mode fiber (SMF) in a direct detection (DD) system with a bit error rate (BER) lower than 3.8 × 10-3. We also conduct a comparative study between the proposed MIMO-ANN NLE, a feedforward equalizer (FFE), a NLE consisting of two independent real-valued Volterra filters, and a MIMO-Volterra filter. The proposed MIMO-ANN NLE outperforms other equalizers with the longer fiber length and thus stronger nonlinearities, since it can easily approximate a complicated nonlinear function. To the best of our knowledge, this is the first experimental demonstration of an ANN-based equalizer in an SSB SCD system.

Immersive Virtual Reality Decreases Work Rate and Manipulates Attentional Focus During Self-Regulated Vigorous Exercise.

To determine the effect of immersive virtual reality use on finishing time of a vigorous-intensity self-regulated exercise task, and on relevant psychological variables. Healthy untrained adults (N = 21; 10 men/11 women; age = 22.9 ± 7.2 years; BMI = 24.0 ± 4.5 kg/m2) completed 1500-m exercise bouts on a rowing ergometer in a counterbalanced and randomized order, with and without use of a headset-delivered virtual reality fitness program. Heart rate, rating of perceived exertion, affective valence, and attentional focus were collected every 300 m, in addition to finishing time. Data were analyzed with repeated measures as appropriate. Intensity of both exercise bouts was considered vigorous according to heart rate results (>77% maximal heart rate). Finishing time was faster in the control condition (449.57 ± 82.39 s) than in the virtual reality condition (463.00 ± 91.78 s), p = .007. Compared to the control condition, the virtual reality condition was characterized by a more external attentional focus (52.38 ± 18.22 vs. 38.76 ± 17.81, p < .001). No differences were observed for remaining variables as a result of condition (p > .05 for all). When a headset-delivered VR program was used during a self-regulated vigorous-intensity exercise task, participants were 13.6 seconds (~3%) slower than in a control condition. Attentional focus was manipulated to be more external with VR use, which may have ultimately distracted from the exercise objective. Recommendations for selecting an appropriate virtual reality experience for a given exercise task are discussed.