In this paper, we propose the energy-tier template insertion shaping (ETTIS) probabilistic shaping algorithm, which performs shaping and deshaping through preset templates and simple bit insertion/deletion operations, thereby significantly reducing the algorithmic complexity. The ETTIS algorithm effectively mitigates the Hamming distance shrinkage problem commonly observed in traditional probabilistic shaping algorithms and exhibits excellent compatibility, allowing seamless integration with standard forward error correction coding and interleaving algorithms. Moreover, the ETTIS framework utilizes predefined symbol components in the shaping templates to implicitly introduce pilot symbols, enabling real-time estimation of channel gain and noise variance without additional bandwidth overhead.. Experimental and simulation results show that, at the same bit rate, the proposed scheme achieves 0.4–0.6 dB performance gains under 16-quadrature amplitude modulation (QAM) and 64-QAM modulation formats, respectively. Compared with other state-ofthe- art algorithms, ETTIS attains comparable performance while maintaining significantly lower complexity. During decoding, the absolute deviation of the estimated noise bit error rate remains below 0.75%. These features make ETTIS a promising solution for high-throughput and cost-sensitive optical interconnect systems.

We demonstrate a probabilistic shaping (PS) four-dimensional (4D) modulation in self-homodyne coherent transmission system. The 4D modulation is based on inter-symbol amplitude translation (AT) to perform set partitioning. The distribution of constellation points after AT is optimized by de-DC. The parity bits produced by the AT are transmitted with the pilot tone by remapping. In addition, a soft decision for this 4D-PS signal is proposed. An experiment of self-homodyne coherent ultra-high order 4D signal transmission based on two cores of a 7-core fiber is demonstrated with a spectral efficiency of 16.37 bit/s/Hz. The 4D signals with soft decision can provide up to 0.78 bit/symbol and 1.85 bit/symbol gain compared to normal polarization division multiplexing signals and hard-decision 4D signals.

Efficient and green transmission of communication and sensing (C&S) signals is a vital problem in Internet of Things (IoT) networks. In this article, we propose a variational autoencoder (VAE)-empowered deep learning (DL) network to fuse and reconstruct the integrated C&S signals. Specifically, we present a convolutional neural network to fuse the input communication data and SAR images into a combined representation, which can then be transmitted to other nodes in space–air–ground–ocean-integrated IoT networks. Instead of directly transmitting C&S data, the transmission of a fused feature vector can greatly save network resources and reduce network burden. Then, a mirrored deconvolutional network is constructed to recover C&S data from the transmitted feature representation. An end-to-end unsupervised training strategy is considered to train the proposed DL network without any label information and human labor. Qualitative and quantitative experiments demonstrate the feasibility of our proposed approach for transmitting and reconstructing integrated C&S signals. Further analysis on hyperparameter sensitivity and loss functions verifies the necessity and efficiency of the components in the proposed DL model. Note that the proposed efficient fusion and reconstruction schemes for C&S signals may provide the convenience to information sharing under the distributed scenarios.

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.