Increasing bandwidth requirements have posed significant challenges for traditional access networks. It is difficult for intensity modulation/direct detection to meet the power budget and flexibility requirements of the next-generation passive optical network (PON) at 100G and beyond considering the new requirements. This is driving researchers to develop novel optical access technologies. Low-cost, wide-coverage, and high-flexibility coherent PON is emerging as a strong contender in the competition. In this article, we will review technologies that reduce the complexity of coherent PON (CPON), enabling it to meet the commercial requirements. Also, advanced algorithms and architectures that can enhance system coverage and flexibility are also discussed.

This paper experimentally demonstrates a distributed photonics-based W-band integrated sensing and communication (ISAC) system, in which radar sensing can aid the communication links in alignment and data rate estimation. As a proof-of-concept, the ISAC system locates the users, guides the alignment, and sets a communication link with the estimated highest data rate. A peak net data rate of 68.6 Gbit/s and a target sensing with a less-than-1-cm error and a sub-2-cm resolution have been tested over a 10-km fiber and a 1.15-m free space transmission in the photonics-based W-band ISAC system. The achievable net data rates of the users at different locations estimated by sensing are experimentally verified.

In this Letter, we propose and experimentally demonstrate a lens-free wavefront shaping method that utilizes synchronized signal block beam alignment and a genetic algorithm (SSBGA) for a diffuse non-line-of-sight (NLOS) visible light communication (VLC) system. The proposed method effectively controls the position and mobility of visible light beams by partitioning spatial light modulator pixels and manipulating beams to converge at distinct spatial positions, thereby enhancing wavefront shaping efficiency, which achieves a significant 23.9 dB optical power enhancement at +2mm offset, surpassing the lens-based continuous sequence (CS) scheme by 21.7 dB. At +40° angle, the improvement reaches up to 11.8 dB and 16.8 dB compared to the results with and without lens-based CS, respectively. A maximum rate of 5.16 Gbps is successfully achieved using bit-power loading discrete multi-tone (DMT) modulation and the proposed SSBGA in an NLOS VLC system, which outperforms the lens-based CS by 1.07 Gbps and obtains a power saving of 55.6% during the transmission at 4 Gbps. To the best of our knowledge, this is the first time that high-speed communication has been realized in an NLOS VLC system without a lens.

针对全双工水下无线光通信（FD-UWOC）系统中存在的自干扰限制系统通信性能的问题，文章提出了一种双波长收发分离的方法。基于Monte Carlo数值仿真方法，在Jerlov I、Jerlov IB和Jerlov II 3种典型水质下，分析了自干扰对450 nm单波长、525 nm单波长和双波长FD-UWOC系统性能的影响情况。仿真结果表明，水质越差，自干扰越强，并且自干扰的增强会显著降低FD-UWOC的光信噪比（OSNR），进而限制了系统的最远传输距离；采用双波长收发分离方法的FD-UWOC系统相较于单波长FD-UWOC系统能带来约60 dB的OSNR提升，从而可以实现更远的传输距离，其中，在Jerlov II类水质下传输距离提升了80 m，并能在Jerlov I和Jerlov IB类水质下实现大于100 m的有效传输距离。这些结果在FD-UWOC系统设计中具有一定的参考意义。

水下可见光通信（UVLC）具有高传输速率、大容量、低延时和低成本等优势，已成为水下通信领域可行且很有吸引力的替代方案，应用前景广阔。然而，UVLC性能受到带宽、各种线性或非线性效应等瓶颈问题的局限。为了缓解这些问题，文章研究了基于几何整形的振幅移相键控（APSK）调制和编码映射，提出了基于双向循环神经网络（BRNN）的波形级后均衡器。此外，文章还提出了用基于深度神经网络（DNN）的波形到符号接收机来代替传统的匹配滤波、下采样和后均衡等操作。实验表明，与传统接收机相比，基于BRNN后均衡电压动态范围提升了170 mV（69%），基于DNN的波形到符号接收机电压动态范围提升了245 mV（100%）。实验结果证明，基于BRNN后均衡和基于DNN的波形到符号接收机能有效提升系统性能，是未来UVLC中很有前景的技术方案。

We propose an encryption technique for underwater visible light communication (UVLC) based on chaotic phase scrambling (PS) and conjugate frequency hopping (CFH). The technique is experimentally tested using an 8-level pulse amplitude modulation (PAM-8) and a 1.2 m underwater link. The security key of the phase scrambling code is generated according to a logistic map, and the frequency hopping is achieved by adding the same zero frequency points to the signal spectrum. The maximum transmission rate of 2.1 Gbit/s is measured with bit-error-rate (BER) below 7% the hard-decision forward error correction (HD-FEC) threshold of 3.8×10-3.

为满足新时代通信需求，新一代通信网络应具有速率更快、容量更大和覆盖范围更广等特点。通过在通信网络中混合应用不同调制格式可以满足新一代通信网络的要求。面对未来通信网络中多种调制格式混用且多变的情况，正确识别信号调制格式可以为后续使用数字算法提升性能提供基础。机器学习因其在特征提取和分类能力上较传统方法具有明显优势，因此将机器学习技术应用于调制格式识别领域成为当下热点。文章介绍了几种应用于基于特征提取分类方案的机器学习方法，并对其应用做出了分析、比较与总结。