Clock Synchronized Transmission of 51.2GBd Optical Packets for Optically Switched Data Center Interconnects

Optical switching has attracted significant attention in recent research on data center networks (DCNs) as it is a promising viable route for the further scaling of hyper scale data centers, so that DCNs can keep pace with the rapid growth of machine-to-machine traffic. It has been shown that optical clock synchronization enables sub-nanosecond clock and data recovery time and is crucial to high performance optically switched DCN. Moreover, the interconnect data rate is expected to increase from the current 100 Gb/s per fiber to scale to 800 Gb/s and beyond, requiring high baud rate signaling at >50 GBd. Thus, future optically switched DCN should support >50GBd data transmission with optical clock synchronization. Here, we demonstrate the clock- synchronized transmission of 128-byte optical packets at 51.2 GBd and study the impact of reference clock phase noise on system performance, focusing on the tolerance to the clock phase misalignment that affects the system scalability and reliability. By comparing the tolerable sampling clock phase offsets using different reference clocks, we show that a clock phase offset window of about 8ps could be achieved with a <0.2ps source clock. Furthermore, we model and numerically study the de- correlation of clock phase noise. This allows the total jitter to be estimated, and thereby, the estimation of the transmission performance for future generations of high baud rate, clock synchronized DC interconnects

Experimental Demonstration of Multiband Comb-Enabled mm-Wave Transmission

A novel system architecture to realize multiple synchronized sources for multiband millimeter-wave (mm-Wave) transmission has been designed and experimentally demonstrated in two of the W -band subbands at 100 and 112.5 GHz. The technique converts a distributed electro-optic comb, generated off-site, to a local electrical comb. The higher frequency tones in the resulting comb are extracted and used as mm-Wave oscillator sources. Thus, the architecture provides a method to generate multiple frequency-synchronized sources, using only a single electronic oscillator, with exceptionally low phase noise for simultaneous multiband mm-Wave transmission. Additionally, a lower frequency tone at 6.25 GHz is broadcasted over the air, providing synchronization reference between the mm-Wave transmitters and receivers

Reinforcement Learning for Self-exploration in Narrow Spaces

In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises. Additionally, it is disabled when mapless. To tackle these problems, we leverage deep reinforcement learning which is verified to be effective in self-decision-making, to self-explore in narrow spaces without a map while avoiding collisions. Specifically, based on our Ackermann-steering rectangular-shaped ZebraT robot and its Gazebo simulator, we propose the rectangular safety region to represent states and detect collisions for rectangular-shaped robots, and a carefully crafted reward function for reinforcement learning that does not require the destination information. Then we benchmark five reinforcement learning algorithms including DDPG, DQN, SAC, PPO, and PPO-discrete, in a simulated narrow track. After training, the well-performed DDPG and DQN models can be transferred to three brand new simulated tracks, and furthermore to three real-world tracks

Impact of Laser Phase Noise on Ranging Precision Within and Beyond Laser Coherence Length in FMCW LiDAR

We study the impact of laser phase noise on ranging precision in a frequency modulated continuous-wave (FMCW) LiDAR system, demonstrating ranging of 384.72 m with ∼15 cm precision at 7× intrinsic laser coherence length

Revisiting Discrete Soft Actor-Critic

We study the adaption of soft actor-critic (SAC) from continuous action space to discrete action space. We revisit vanilla SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at:https://github.com/coldsummerday/Revisiting-Discrete-SAC

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by a greedy strategy, which may lose the local patch details outside the chosen candidate. In this paper, we propose a novel spatiotemporal graph neural network (STG-Net) to reconstruct more accurate masks for video object segmentation, which captures the local contexts by utilizing all proposals. In the spatial graph, we treat object proposals of a frame as nodes and represent their correlations with an edge weight strategy for mask context aggregation. To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph. The joint use of both local patch details and temporal relationships allow us to better address the challenges such as object occlusion and missing. Without online learning and fine-tuning, our STG-Net achieves state-of-the-art performance on four large benchmarks (DAVIS, YouTube-VOS, SegTrack-v2, and YouTube-Objects), demonstrating the effectiveness of the proposed approach.Comment: Accepted by AAAI 202