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This paper reports the design, fabrication, and experimental demonstration of a monolithic silicon photonic (SiPh) 32×32 Thin-CLOS arrayed waveguide grating router (AWGR) for scalable SiPh all-to-all interconnection fabrics. The 32×32 Thin-CLOS makes use of four 16-port silicon nitride AWGRs, which are compactly integrated and interconnected by a multi-layer waveguide routing method. The fabricated Thin-CLOS has 4 dB insertion loss, < −15 dB adjacent channel crosstalk, and < −20 dB non-adjacent channel crosstalk. System experiments operated on the 32×32 SiPh Thin-CLOS demonstrate error-free communication at 25 Gb/s.

We experimentally demonstrate quantum channel monitoring by wavelength-time multiplexing of classical wrapper bits with quantum payloads. Bit-error-rate measurements of 5 Gb/s classical bits infer the coincidence-to-accidental ratio of the quantum channel up to 13.3 dB.

This paper presents a cognitive flexible-bandwidth optical interconnect architecture for datacom networks. The proposed architecture leverages silicon photonic reconfigurable all-to-all switch fabrics interconnecting top-of-rack switches arranged in a Hyper-X-like topology with a cognitive control plane for optical reconfiguration by self-supervised learning. The proposed approach makes use of a clustering algorithm to learn the traffic patterns from historical traces. We developed a heuristic algorithm for optimizing the intra-pod connectivity graph for each identified traffic pattern. Further, to mitigate the scalability issue induced by frequent clustering operations, we parameterized the learned traffic patterns by a support vector machine classifier. The classifier is trained offline by self-labeled data to enable the classification of traffic matrices during online operations, thereby facilitating cognitive reconfiguration decision making. The simulation results show that compared with a static all-to-all interconnection, the proposed approach can improve the throughput by up to$1.62\times <\#comment/>$while reducing the end-to-end packet latency and flow completion time by up to$3.84\times <\#comment/>$and$20\times <\#comment/>$, respectively.

This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to$13\times <\#comment/>$while achieving an estimation accuracy above 95%.

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ($<<\#comment/>9\mathrm{\%<\#comment/>}$error) and demonstrate up to$5\times <\#comment/>$throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

This paper proposes an elastic RF-optical networking (ERON) architecture solution for millimeter wave (mmWave) 5G radio access networks. The ERON architecture achieves energy efficiency and throughput elasticity using photonic-enhanced multibeam mmWave spatial multiplexing capability at the radio units. The centralization of the hardware resources and the converged management of the RF and optical resources in the data units offer high resource pooling gain. A numerical study on the energy efficiency of ERON’s photonic-enabled mmWave 5G system reveals that ERON is 5× more energy efficient than both conventional digital and hybrid RF beam-forming implementations. We also conducted a user mobility-aware network resources study, and the results show a 10 dB network resource pooling gain when compared to classic radio access network implementations.