Pilot-Based Key Distribution and Encryption for Secure Coherent Passive Optical Networks

The security issues of passive optical networks (PONs) have always been a concern due to broadcast transmission. Physical-layer security enhancement for the coherent PON should be as significant as improving transmission performance. In this paper, we propose the advanced encryption standard (AES) algorithm and geometric constellation shaping four-level pulse amplitude modulation (GCS-PAM4) pilot-based key distribution for secure coherent PON. The first bit of the GCS-PAM4 pilot is used for the hardware-efficient carrier phase recovery (CPR), while the second bit is utilized for key distribution without occupying the additional overhead. The key bits are encoded by the polar code to ensure error-free distribution. Frequent key updates are permitted for every codeword to improve the security of coherent PON. The experimental results of the 200-Gbps secure coherent PON using digital subcarrier multiplexing show that the GCS-PAM4 pilot-based key distribution could be error-free at upstream transmission without occupying the additional overhead and the eavesdropping would be prevented by AES algorithm at downstream transmission. Moreover, there is almost no performance penalty on the CPR using the GCS-PAM4 pilot compared to the binary phase shift keying pilot.

Capacity Limitation and Optimization Strategy for Flexible Point-to-Multi-Point Optical Networks

Point-to-multi-point (PtMP) optical networks become the main solutions for network-edge applications such as passive optical networks and radio access networks. Entropy-loading digital subcarrier multiplexing (DSCM) is the core technology to achieve low latency and approach high capacity for flexible PtMP optical networks. However, the high peak-to-average power ratio of the entropy-loading DSCM signal limits the power budget and restricts the capacity, which can be reduced effectively by clipping operation. In this paper, we derive the theoretical capacity limitation of the flexible PtMP optical networks based on the entropy-loading DSCM signal. Meanwhile, an optimal clipping ratio for the clipping operation is acquired to approach the highest capacity limitation. Based on an accurate clipping-noise model under the optimal clipping ratio, we establish a three-dimensional look-up table for bit-error ratio, spectral efficiency, and link loss. Based on the three-dimensional look-up table, an optimization strategy is proposed to acquire optimal spectral efficiencies for achieving a higher capacity of the flexible PtMP optical networks.

Non-Integer-Oversampling Digital Signal Processing for Coherent Passive Optical Networks

Beyond 100G passive optical networks (PONs) will be required to meet the ever-increasing traffic demand in the future. Coherent optical technologies are the competitive solutions for the future beyond 100G PON but also face challenges such as the high computational complexity of digital signal processing (DSP). A high oversampling rate in coherent optical technologies results in the high computational complexity of DSP. Therefore, DSP running in a non-integer-oversampling below 2 samples-per-symbol (sps) is preferred, which can not only reduce computational complexity but also obviously lower the requirement for the analog-to-digital converter. In this paper, we propose a non-integer-oversampling DSP for meeting the requirements of coherent PON. The proposed DSP working at 9/8-sps and 5/4-sps oversampling rates can be reduced by 44.04% and 40.78% computational complexity compared to that working at the 2-sps oversampling rate, respectively. Moreover, a 400-Gb/s-net-rate coherent PON based on digital subcarrier multiplexing was demonstrated to verify the feasibility of the non-integer-oversampling DSP. There is almost no penalty on the receiver sensitivity when the non-integer-oversampling DSP is adopted. In conclusion, the non-integer-oversampling DSP shows great potential in the future coherent PON.

Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.

Multi-Rate Nyquist-SCM for C-Band 100Gbit/s Signal over 50km Dispersion-Uncompensated Link

In this paper, to the best of our knowledge, we propose the first multi-rate Nyquist-subcarriers modulation (SCM) for C-band 100Gbit/s signal transmission over 50km dispersion-uncompensated link. Chromatic dispersion (CD) introduces severe spectral nulls on optical double-sideband signal, which greatly degrades the performance of intensity-modulation and direct-detection systems. In the previous works, high-complexity digital signal processing (DSP) is required to resist the CD-caused spectral nulls. Based on the characteristics of dispersive channel, Nyquist-SCM with multi-rate subcarriers is proposed to keep away from the CD-caused spectral nulls flexibly. Signal on each subcarrier can be individually recovered by a DSP with an acceptable complexity, including the feed-forward equalizer with no more than 31 taps, a two-tap post filter, and maximum likelihood sequence estimation with one memory length. Combining with entropy loading based on probabilistic constellation shaping to maximize the capacity-reach, the C-band 100Gbit/s multi-rate Nyquist-SCM signal over 50km dispersion-uncompensated link can achieve 7% hard-decision forward error correction limit and average normalized generalized mutual information of 0.967. In conclusion, the multi-rate Nyquist-SCM shows great potentials in solving the CD-caused spectral distortions.

Content-Aware Unsupervised Deep Homography Estimation

Robust homography estimation between two images is a fundamental task which has been widely applied to various vision applications. Traditional feature based methods often detect image features and fit a homography according to matched features with RANSAC outlier removal. However, the quality of homography heavily relies on the quality of image features, which are prone to errors with respect to low light and low texture images. On the other hand, previous deep homography approaches either synthesize images for supervised learning or adopt aerial images for unsupervised learning, both ignoring the importance of depth disparities in homography estimation. Moreover, they treat the image content equally, including regions of dynamic objects and near-range foregrounds, which further decreases the quality of estimation. In this work, to overcome such problems, we propose an unsupervised deep homography method with a new architecture design. We learn a mask during the estimation to reject outlier regions. In addition, we calculate loss with respect to our learned deep features instead of directly comparing the image contents as did previously. Moreover, a comprehensive dataset is presented, covering both regular and challenging cases, such as poor textures and non-planar interferences. The effectiveness of our method is validated through comparisons with both feature-based and previous deep-based methods. Code will be soon available at Github.