In the non-line of sight (NLOS) ultraviolet (UV) scattering communication, the received signals exhibit the characteristics of discrete photoelectrons due to the extremely large path loss. We design and demonstrate an NLOS UV scattering communication system in this work, where the receiver-side signal detection is designed based on a discrete-time Poisson channel model. In our system, a laser and multiple photomultiplier tubes are employed as the optical transmitter and detector, respectively. Furthermore, we design algorithms for pulse-counting, synchronization, channel estimation and LLR computation for hardware realization in FPGA board. Simulation results are provided to evaluate the proposed system design and specify the system key parameters. We perform field tests for real-time communication with the transmission range over 1km, where the system throughput reaches 1Mbps.

Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i.e., message passing) behind GNNs inducing unfairness issue remains unknown. In this work, we theoretically and experimentally demonstrate that representative aggregation in message passing schemes accumulates bias in node representation due to topology bias induced by graph topology. Thus, a Fair Message Passing (FMP) scheme is proposed to aggregate useful information from neighbors but minimize the effect of topology bias in a unified framework considering graph smoothness and fairness objectives. The proposed FMP is effective, transparent, and compatible with back-propagation training. An acceleration approach on gradient calculation is also adopted to improve algorithm efficiency. Experiments on node classification tasks demonstrate that the proposed FMP outperforms the state-ofthe-art baselines in effectively and efficiently mitigating bias on three real-world datasets.

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We investigate the multi-layer superimposed transmission for optical wireless scattering communication where the symbol boundaries on different signal layers are not necessarily aligned in the time domain. We characterize the multi-layer transmission based on a hidden markov model. Then, we obtain the achievable rates of all signal layers and a single layer, and provide a numerical solution. Furthermore, we propose approaches on the channel estimation as well as joint symbol detection and decoding. Finally, both simulations and experiments are conducted to evaluate the performance of the proposed approaches, and validate the feasibility of the proposed transmission and signal detection approaches. Index TermsNLOS scattering communication, superimposed transmission, hidden markov model, achievable rate, joint detection and decoding.

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