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There are increasing requirements for data center interconnection (DCI) services, which use fiber to connect any DC distributed in a metro area and quickly establish high-capacity optical paths between cloud services and mobile edge computing and the users. In such networks, coherent transceivers with various optical frequency ranges, modulators, and modulation formats installed at each connection point must be used to meet service requirements such as fast-varying traffic requests between user computing resources. This requires technology and architectures that enable users and DCI operators to cooperate to achieve fast provisioning of WDM links and flexible route switching in a short time, independent of the transceiver’s implementation and characteristics. We propose an approach to estimate the end-to-end (EtE) generalized signal-to-noise ratio (GSNR) accurately in a short time, not by measuring the GSNR at the operational route and wavelength for the EtE optical path but by simply applying a quality of transmission probe channel link by link, at a wavelength/modulation-format convenient for measurement. Assuming connections between transceivers of various frequency ranges, modulators, and modulation formats, we propose a device software architecture in which the DCI operator optimizes the transmission mode between user transceivers with high accuracy using only common parameters such as the bit error rate. In this paper, we first implement software libraries for fast WDM provisioning and experimentally build different routes to verify the accuracy of this approach. For the operational EtE GSNR measurements, the accuracy estimated from the sum of the measurements for each link was 0.6 dB, and the wavelength-dependent error was about 0.2 dB. Then, using field fibers deployed in the NSF COSMOS testbed, a Linux-based transmission device software architecture, and transceivers with different optical frequency ranges, modulators, and modulation formats, the fast WDM provisioning of an optical path was completed within 6 min. 

Outdoor-to-indoor signal propagation poses significant challenges to millimeter-wave link budgets. To gain insight into outdoor-to-indoor millimeter-wave at 28GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all measured links shows an average of 30dB excess loss over free space at distances beyond 50m. We find the type of glass to be the dominant factor in outdoor-to-indoor loss, with 20dB observed difference between grouped scenarios with low- and high-loss glass. Other factors such as the presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are also found to have a 5–10dB impact. We show that for urban buildings with high-loss glass, outdoor-toindoor downlink capacity up to 400Mb/s is supported for 90% of indoor customer premises equipment by a base station up to 40m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, downlink capacity over 2.8/1.4Gb/s is possible from a base station 57/133m away within line-of-sight. We expect these results to help inform the planning of millimeter-wave networks targeting outdoor-toindoor deployments in dense urban environments, as well as provide insight into the development of scheduling and beam management algorithms. Index Terms—Millimeter-wave wireless, 28 GHz measurements, path loss models, wireless network planning, 5G-andbeyond networks. 

Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM.