We present results from the first demonstration of a fully integrated SDN-controlled bandwidth-flexible and programmable SDM optical network utilizing sliceable self-homodyne spatial superchannels to support dynamic bandwidth and QoT provisioning, infrastructure slicing and isolation. Results show that SDN is a suitable control plane solution for the high-capacity flexible SDM network. It is able to provision end-to-end bandwidth and QoT requests according to user requirements, considering the unique characteristics of the underlying SDM infrastructure.

This paper describes the problematic filter narrowing effect in the context of next-generation elastic optical networks. First, three possible scenarios are introduced: the transition from an actual fixed-grid to a flexi-grid network, the generic full flexi-grid network, and a proposal for a filterless optical network. Next, we investigate different transmission techniques and evaluate the penalty introduced by the filtering effect when considering Nyquist wavelength division multiplexing, single side-band direct-detection orthogonal frequency division multiplexing, and symbol-rate variable dual polarization quadrature amplitude modulation. Also, different approaches to compensate for the filter narrowing effect are discussed. Results show that the specific needs per each scenario can be fulfilled by the aforementioned technologies and techniques or a combination of them, when balancing performance, network reach, and cost.

Multi-access edge computing (MEC) has been regarded as one of the essential technologies for mobile networks, by providing computing resources and services close to users, thereby, avoiding extra energy consumption and fitting the low-latency ultra-reliable requirements for emerging 5G applications. Task offloading policy plays a pivotal role in handling offloading requests and maximizing the network computing performance. Most recently developed offloading solutions are designed for instant rewards, therefore, neglecting the long-term computing resource optimization at the edge, which fail to deliver optimized network performance when a significant increase of computing requests appears. In this paper, with the objective of maximizing long-term offloading benefits on delay and energy consumption, task offloading policies are proposed to firstly avoid resource over-distribution through deep reinforcement learning (DRL) based resource reservation and server cooperation, and secondly maximize the average instant reward and the utilization of reserved resources by an optimization-based joint policy consisting of offloading decision, transmission power allocation and resource distribution. The DRL-based joint policy is evaluated in a simulated multi-server edge computing network. Compared to previous solutions, the DRL-based algorithms achieve higher and more reliable overall rewards. Of the implemented three DRL-based algorithms, fully cooperative multi-agent DRL accounts for cooperation between servers, achieving a 70.5% reduction in reward variance and a 13.4% increase in average rewards over 500 continuous operations. Resource balanced policies on long-term rewards help edge networks handle the explosive growth of 5G computing-intensive applications in the future.

A low-complexity feed-forward carrier phase estimation (CPE) technique is presented for dual-polarization (DP)-16-QAM transmission systems. By combining QPSK partitioning, maximum likelihood (ML) detection and phase offset estimation between signals in different polarizations, simulation and experimental results for a 200Gb/s DP-16-QAM system demonstrate similar linewidth tolerance to the best feed-forward CPE reported to date while the computational complexity is at least three times lower compared with other simplified feed-forward CPE techniques.

We propose a sliceable bandwidth variable transceiver (S-BVT) architecture suitable for metro/regional elastic networks and highly scalable data center applications. It adopts multicarrier modulation, either OFDM or DMT, and a cost-effective optoelectronic front-end. The high-capacity S-BVT is programmable, adaptive, and reconfigurable by an SDN controller for efficient resource usage, enabling unique granularity, flexibility, and grid adaptation, even in the conventional fixed-grid networks. We experimentally demonstrate its multiple advanced functionalities in a four-node photonic mesh network. This includes SDN-enabled rate/distance adaptive multiflow generation and routing/switching, slice-ability, flexibility, and adaptability for the mitigation of spectrum fragmentation, as well as for a soft migration toward the flexi-grid paradigm.

This paper reports all-optical, function programmable, transparent, intra- and inter-data center networking (DCN) using space and time-division multiplexing (SDM/TDM) within data centers and wavelength division multiplexing (WDM) between data centers. A multielement fiber is used for SDM transmission to provide a large quantity of optical links between the top-of-racks (ToRs) and the function programmable cluster switch. Beam-steering large-port-count fiber switches, used as central cluster switches and intercluster switch, provide a single hop optical circuit switching solution, and also enable network function programmability for DCN to support variable traffic patterns and different network functions. A TDM switch as a plug-in function provides intra-cluster communication with variable capacity and low latency. The flat-structured intra data center architecture, with a circuit-switched SDM and TDM hybrid network enables scalable, large-capacity and low-latency DCN communication. In addition, all-optical ToR-to-ToR inter-DCN is realized through metro/core networks. A highly-nonlinear fiber based all-optical SDM-to-WDM converter transfers three SDM signals to three-carrier spectral superchannel signals, which are transmitted to the destination DCN, through the metro/core networks. The all-optical ToR-ToR cross-DCN connections enable the geographically distributed DCNs to appear as one big data center.