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Quality of Transmission (QoT) estimation, where the received signal quality is predicted before deployment, plays a significant role in efficient resource utilization, such as determining the optimal transmission configuration. Traditionally, it is implemented with analytical models, and often accompanied by large link margins to account for the low estimation accuracy. Machine learning (ML) based methods have been recently demonstrated as an alternative solution with high accuracy. However, they require a large number of training data, which is often expensive to obtain in the context of QoT estimation. In this paper, we use active learning (AL) to achieve data efficient QoT estimation. A learner actively selects the training data to be labeled by applying the strategy of uncertainty sampling which favors data with high model uncertainty. A data selection algorithm compatible with the widely studied artificial neural network (ANN)-based QoT estimator is proposed and experimentally demonstrated in a fiber-wireless integrated testbed. Monte Carlo dropout (MC dropout) is utilized to calculate model uncertainty. To achieve a mean squared error (MSE) of 0.055, the number of training data can be reduced by more than 25% compared with the conventional passive ML. The algorithm is also investigated under different sampling settings and the impact of hyperparameters is discussed.

Because of the various earth return pathways in practical projects, calculating the wide-band earth return impedance of submarine cable is problematic. Using the dynamic Hertz magnetic potential, the formula for estimating the earth return impedance has been obtained based on the three-layer structure of air, sea water, and seafloor. Furthermore, in the frequency range of 50 [Formula: see text] 10 MHz, the enhanced complex image approach and numerical integration method have been developed to compute the earth return impedance. The suggested iterative complex image method outperforms the classic complex image method in terms of accuracy and computation speed, with a global fitting error of less than 10−6. Finally, the impact of various models on the real and imaginary parts of submarine cable wide-band earth return impedance has been investigated. The results demonstrate that the actual component has a more complicated variation, whereas the imaginary part is clearly affected by the skin effect, with a bigger difference in the high-frequency range. The method presented in this research can be utilized as a supplement to existing methods, as well as a reference methodology for analyzing the influence of over-voltage on submarine cable engineering.

An efficient method for simultaneous nonlinear self-interference (SI) cancellation and signal-of-interest (SOI) recovery is proposed and experimentally verified in a mmWave over fiber testbed, based on a specially designed dual-input deep neural network (DI-DNN). The mmWave band has been adopted in the U.S. to provide 5G wireless access services due to its wide-bandwidth, light-of-sight propagation, and inherent compatibility with small cell architecture. To further meet the uprising bandwidth-demanding services, the in-band full-duplex (IBFD) scheme is introduced to magnify the mmWave channel capacity for the 5G and beyond wireless communications. Fortunately, the mmWave band is more realistic to implement IBFD compared with the conventional sub-6GHz band, as it operates as a highly directional beam and shorter transmission distance resulting in a higher SOI to SI ratio. In our proposed solution, a DI-DNN is, for the first time, proposed and implemented to simultaneously realize the SI cancellation and the SOI recovery. Moreover, we firstly mitigate the nonlinearity arising from the transmitters of both the SI and SOI, as well as the nonlinear crosstalk from the SI to the SOI after detection by a nonlinear receiver. The demonstration of the DI-DNN joint with the experimental implementation in a mmWave over fiber system, promise spectral efficient, high-capacity signal transport in the 5G and beyond wireless communications.

Detection of vaccine (adjuvant and antigen) is crucial for the fundamental studies of immunotherapy. In this work, the catechol-containing glycopolymer obtained by sunlight-induced RAFT polymerization was first designed to generate glycoadjuvant@AuNPs. Then, a simple and general self-assembled technique, catechol-driven self-assembly (CDSA), was developed to fabricate AuNP-based glycoadjuvant patterns, regardless of the size, shape and synthetic method of AuNPs. More importantly, highly ordered glycoadjuvant patterns could be easily formed by catechol-driven self-assembly under confinement, which exhibit a higher SERS signal amplification ability for the detection of carbohydrates (glycoadjuvant).

The biotransformation of 2D nanomaterials is still poorly understood, although their environmental fates are becoming an increasing concern with their broad applications. Here, we found that Ti

Orthogonal multiple access (OMA) such as orthogonal frequency division multiple access (OFDMA) has been the predominant access technology adopted by the current generation of mobile communication. Representing an evolutionary step in radio access networks, non-orthogonal multiple access (NOMA) is considered to be a promising technology to meet the goals of increased capacity, enhanced flexibility, and reduced latency. Pattern division multiple access (PDMA) is a NOMA scheme using resource-element mapping as the multiple-access signature in the symbol level. This article introduces PDMA in a millimeter-wave (mmWave) radio access system with systematic experimental demonstration. One key advantage of PDMA is that it can facilitate advanced parallel interference cancellation (PIC) in the decoding process. A message passing algorithm (MPA) is developed and implemented in this article to enhance the decoding performance of PDMA. A novel PDMA system integrated with MPA is experimentally demonstrated in a multi-user mmWave radio access system that uses radio-over-fiber (RoF) mobile fronthaul, achieving reduced sensitivity penalty and improved modulation spectral efficiency compared to conventional power-domain NOMA (PD-NOMA) with successive interference cancellation (SIC). Experimental results show that PDMA is capable of supporting disparate channel conditions, adapting to various patterns, and providing improved system flexibility.

Machine learning technique is employed to design a microwave photonic filter (MPF) consisting of an optical comb generator and a phase modulator (PM), to realize centralized management of radio signal delivery in optical networks. The proposed comb generator successfully generates up to 103 optical carriers with adjustable wavelength spacing and number of optical carriers. It exhibits large tunability, flat band response and high tone-to-noise ratio. Thus, the proposed optical comb-based filter is capable to provide flexible tunability with respect to its center frequency and 3-dB bandwidth. In our experimental setup, different wavelength spacing settings and number of optical carriers are fed into the PM for modulation and their corresponding frequency responses are measured by a network analyzer. The experimental data have been analyzed to correlate with the simulation results and theoretical predictions. Besides, a subcarrier multiplexing (SCM) technique can be applied to a multi-user optical system incorporating the proposed filter since its frequency response is varied by the accumulated dispersion. After measuring the characteristic of the proposed filter, a set of frequency responses is collected and fed into the convolutional neural network (CNN) model to obtain the inverse mapping between frequency response to the wavelength spacing and fiber length. As a result, the well-trained model can successfully predict the wavelength spacing and fiber length with high accuracy.

In this article, a novel scheme to effectively mitigate the nonlinear impairments in a PAM-8 radio-over-fiber (ROF) delivery is proposed by a joint deep neuron network (J-DNN) equalizer, which has more superiority in terms of good training accuracy, satisfactory tracking speed, and over-fitting suppression compared with a typical deep neuron network (DNN) equalizer. Our proposed J-DNN equalization scheme is mainly based upon back-propagation (BP) algorithm and blind cascaded multi-modulus algorithm (CMMA), which can be trained via two steps including DNN initialization and DNN optimization. By using the proposed J-DNN equalizer, 60-Gbps PAM-8 signal generation and transmission over 10-km SMF and 3-m wireless link at 135-GHz can be achieved. For the digital signal processing (DSP) at receiver, comparisons between CMMA equalizer, DNN equalizer, and J-DNN equalizer are demonstrated. The results indicate that J-DNN equalizer has a much better BER performance in receiver sensitivity than the traditional CMMA, and an improvement of receiver sensitivity can be achieved as much as 1 dB compared with a DNN equalizer at the BER of 3.8 × 10−3. To the best of our knowledge, this is the first time to propose a novel joint DNN equalizer, which is promising for the development in integrated microwave photonics and microwave/millimeter-wave photonics for 5G applications and beyond.

To efficiently support multiple services with diverse requirements in data capacity, latency, and connectivity in the fifth-generation (5G) communication system and beyond, various mobile fronthaul solutions, including digital and analog radio-over-fiber (RoF), will be considered. We propose a non-orthogonal multiplexing scheme for multiservice uplink transmissions in mobile fronthaul, where in-band analog RoF (A-RoF) signals can be transmitted with digital RoF (D-RoF) signals simultaneously, resulting in high spectrum resource utilization. The demultiplexing is fulfilled by exploiting different characteristics of the D-RoF and A-RoF signals in the time-domain and it can be implemented with analog signal processing, providing low processing complexity and processing delay. The proposed scheme is experimentally demonstrated to achieve the joint transmission of the D-RoF and A-RoF signals over a shared mobile fronthaul through 20-km standard single mode fiber (SSMF). Experimental results show that in-band A-RoF signals from 2–10 GHz can be co-transmitted with D-RoF 10 GBaud NRZ-OOK signals, with the signal-to-interference-plus-noise ratio (SINR) enhanced by the demultiplexing module. Both received D-RoF and A-RoF signals can achieve bit error ratio (BER) lower than the 7% hard-decision forward-error correction (FEC) threshold.

We experimentally demonstrate the transmission of orthogonal frequency division multiplexing (OFDM) signals with polar codes FEC in a W-band MMW system. The BER performances of the M-QAM OFDM signal with FEC and without FEC are investigated, respectively. The experimental results show that the polar codes have a net gain of 7.9 dB compare with that of the LDPC code (7.4 dB). To the best of our knowledge, this is the first demonstration that the polar codes FEC are used in a W-band MMW system with OFDM modulation.

SPSS is the general name of a series of software products and related services used for statistical analysis operations, data mining, predictive analysis and decision support tasks. This paper uses SPSS, and establishes a comprehensive evaluation index system of urban complex development ability based on the urban scale, urban economy, urban transportation, urban market and urban consumption situation. The study believes these provide scientific reference for urban decision makers to develop urban complex. Finally, taking Shanghai and Beijing as examples, the case analysis is carried out to verify the feasibility of the models, and thus result in reference suggestions for the development of urban complex in the two cities and even the whole country.

Performance of digital signal processing (DSP) aided sub-band spreading orthogonal frequency division multiplexing (S-OFDM) implemented with the well-defined, time-located window is evaluated and compared with the cyclic prefix (CP) OFDM signal under severe interference circumstances. A four-quadrature amplitude modulation (QAM) mobile fronthaul over 20 km fiber link and 1 m millimeter wave 60-GHz wireless channel is demonstrated experimentally. The bit-error rate performance of applying time windowing on OFDM and S-OFDM are measured in the single channel scenario. In such simplified system, CP-OFDM is found to be a widely adopted waveform. While, as it suffers from the unexpected external interference, the received error vector magnitude (EVM) would be degraded dramatically for typical OFDM signals. By applying the Hanning window, the impact on the EVM performance can be alleviated as the interference is at the edge of the desired signal band and resulting a 2.5% EVM enhancement. However, an irrecoverable 20% data loss is obtained if the interference is totally immersed in the victim signal. In contrast, S-OFDM exhibits a frequency diversity property by grouping OFDM subcarriers into several sub-bands and spreading them into whole available bandwidth. Thus, as S-OFDM suffers from strong interference inside the signal band, about 12.61% and 43.53% EVM improvements of the average and the worst subcarrier performance with respect to the ordinary OFDM can be achieved via applying the spreading codes orthogonality restoring equalizer. All subcarriers in the S-OFDM are generated to meet the forward error correction threshold with reasonably flat performance. Therefore, the S-OFDM shows its superiority in an MMW mobile fronthaul for better reliability in data transmission.