Ever-increasing intra-datacenter traffic will spur the introduction of high-baud rates and high-order modulation formats. Increasing symbol rates and modulation levels decreases tolerance against transmission impairment that includes chromatic dispersion. Transmission distance in warehouse-scale datacenters can be several kilometers, and then management of chromatic dispersion is necessary. Dispersion-compensating fibers are widely deployed in backbone networks, however, applying them in datacenters is not cost-effective since wavelength channels are coarsely multiplexed. In digital coherent systems, signal distortion due to chromatic dispersion can be resolved in digital domain; however, it will take long time before coherent systems can be introduced in datacenter networks because of their high cost. In this paper, we propose a novel impairment mitigation method employing machine learning. The proposed method is effective even after non-coherent detection and hence it can be applied to cost-sensitive intra-datacenter networks. The machine learns optimum symbol-decision criteria from a sequence of dispersed training signals, and it discriminates payload signals in accordance with the established decision criteria. With the scheme, the received signals can be demodulated in the presence of large chromatic dispersion. The transmission distance thus can be extended without relying on costly optical dispersion compensation. Since information of transmission links is not a priori required, the proposed scheme can easily be applied to any datacenter network. We conduct transmission experiments using 400-Gbps channels each of which comprises 8-subcarrier 28-Gbaud 4-ary pulse-amplitude-modulation (PAM-4) signals, and confirm the effectiveness of the proposed scheme.
Intra-datacenter traffic is growing more than 20% a year. In typical datacenters, many racks/pods including servers are interconnected via multi-tier electrical switches. The electrical switches necessitate power-consuming optical-to- electrical (OE) and electrical-to-optical (EO) conversion, the power consumption of which increases with traffic. To overcome this problem, optical switches that eliminate costly OE and EO conversion and enable low power consumption switching are being investigated. There are two major requirements for the optical switch. First, it must have a high port count to construct reduced tier intra-datacenter networks. Second, switching speed must be short enough that most of the traffic load can be offloaded from electrical switches. Among various optical switches, we focus on those based on arrayed-waveguide gratings (AWGs), since the AWG is a passive device with minimal power consumption. We previously proposed a high-port-count optical switch architecture that utilizes tunable lasers, route-and-combine switches, and wavelength-routing switches comprised of couplers, erbium-doped fiber amplifiers (EDFAs), and AWGs. We employed conventional external cavity lasers whose wavelength-tuning speed was slower than 100 ms. In this paper, we demonstrate a large-scale optical switch that offers fast wavelength routing. We construct a 720×720 optical switch using recently developed lasers whose wavelength-tuning period is below 460 μs. We evaluate the switching time via bit-error-ratio measurements and achieve 470-μs switching time (includes 10-μs guard time to handle EDFA surge). To best of our knowledge, this is the first demonstration of such a large-scale optical switch with practical switching