An improved neural network-based equalization method is proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of four level pulse amplitude modulation (PAM-4) signals with 25 G class 850 nm optical devices is achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation is conducted regarding the impact of delayed taps and spans on equalization performance. A performance comparison of the proposed method with the traditional maximum likelihood sequence estimation (MLSE) and decision feedback equalization (DFE) is also undertaken. For the bit rate from 80 to 100 Gb/s, the proposed method achieves an adopted hard-decision forward error correction (HD-FEC) requirement at a received optical power (RoP) of −9 and −8 dBm, while DFE and MLSE cannot meet the HD-FEC requirement. When the bit rate increases from 120 to 137 Gb/s, the proposed equalization method still successfully maintains the acceptable system performance at an RoP of −4 and −2.5 dBm. Furthermore, the specific bit error rate (BER) performances for varied maximum achievable bit rate under different RoPs by applying MLSE and the proposed method are also analyzed. This provides an important potential solution to realize the future data centers.

In this study, a joint modulation format identification and optical signal-to-noise ratio (OSNR) monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor algorithm for intensity modulation and direct detection (IMDD) orthogonal frequency division multiplexing (OFDM) systems. A modified amplitude histogram of received signal is employed to serve as the classification feature to simplify the computation. Experimental results show that five common quadrature amplitude modulation (QAM) modulation formats, including 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM, can be identified under 100% accurate estimation at the received optical power of −11 dBm. Robustness of the proposed scheme to constellation rotation is also experimentally assessed. At the same time, system OSNR monitoring also can be achieved and the average prediction mean square error (MSE) is 0.69 dB², which is similar to that using an artificial neural network. Computational complexity assessment demonstrated that similar performance but less computing resource consumption can be achieved by using the proposed scheme rather than the artificial neural network-based scheme.

The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.

We introduce recently developed space division multiplexing (SDM)-associated technologies into optical wireless communications (OWC) and propose to use incoherent and low-coherence spatial modes to realize optical broadcasting with reliable performance by mitigating coherent interference. We experimentally demonstrate reconfigurable optical beam steering using a programmable multi-plane light conversion (MPLC)-based mode multiplexer (MMUX) and realize point-to-multi-point optical wireless communications with low-loss up-link optical beam combining and reach extension using multimode fiber (MMF). Both incoherent and low-coherence spatial modes can be generated employing the cost-efficient components such as multimode vertical cavity surface emitting laser (MM-VCSEL) and broadband amplified spontaneous emission (ASE) noise source.

The antenna scan period is a core technical parameter of radar, and its robust, real-time, and high-precision measurement is of great engineering significance. Based on filed-measured full-pulse radar data, two measurement methods for the antenna scan period are proposed in this paper: the smoothing-fitting method and the smoothing-correlation method. Verified by filed-measured data, the measurement error fluctuation of the smoothing-fitting method is no more than 2 pulse repetition intervals (PRIs), and that of the smoothing-correlation method is no more than 1 PRI, both meeting the requirements of engineering applications. Adopting a point-by-point sliding processing flow without batch fitting, the smoothing-correlation method is more suitable for streaming and real-time data processing compared with the smoothing-fitting method and has a distinct engineering transformation value. Compared with batch processing methods such as the cepstrum method, which need to buffer data of at least one complete scanning period, the smoothing-correlation method avoids full-sequence FFT/IFFT operations. It can significantly reduce memory occupation and processing time and achieve superior real-time performance.