No abstract

This paper, for the first time, investigates the nonlinear degradation of 40 Gbaud single-and dual polarization RZ-DQPSK/D8PSK signals caused by SPM and XPM-induced crosstalk from neighboring 10 Gbit/s NRZ-OOK channels in an WDM upgrade scenario. The investigations were numerically and experimentally conducted over a 320 km transmission link with three different wavelength configurations to address the impact of the walk-off length. The paper also presents the first numerical analysis of the XPM dependence on the relative state of polarization of the D8PSK with respect to the neighbors.

In this paper, transmission of Polarization-Multiplexed (PM), RZ-DQPSK is analyzed numerically with copropagation of intensity modulated non return to zero on off keying (NRZ OOK) channels. The dependence of nonlinear crosstalk on baud rate (10 Gbaud, 28 Gbaud and 56 Gbaud) and relative state of polarization (SOPs) of PM RZ DQPSK and neighbouring NRZ OOK channels is analyzed in terms of required OSNR for the BER of 10 -3 versus launch power. From the results we observe that non-linear impairments are highly dependent on relative SOPs and less dependent on baud rates of the PM RZ DQPSK test channel.

In this paper, 40 Gbaud transmission of single polarization (SP) and Polarization-Multiplexed (PM), RZ-DQPSK and RZ-D8PSK signals is analyzed numerically. The impact of nonlinear crosstalk arising from the presence of neighbouring intensity-modulated channels is analyzed in terms of required OSNR for the BER of 10 -3 versus launch power.

The Flexible Ethernet (FlexE) is envisioned for the provisioning of different services and hard slicing of the Xhaul in 5G and beyond networks. For efficient bandwidth utilization in the Xhaul, traffic prediction for slot allocation in FlexE calendars is required. Further, if coordinated multipoint (CoMP) is used, the allocation of users to remote units (RUs) with an Xhaul path of lower latency to the distributed unit/central unit will increase the achievable user bit rate. In this paper, the use of multi-agent deep reinforcement learning (DRL) for optimal slot allocations in a FlexE-enabled Xhaul, for traffic generated through CoMP, and for offloading users among different RUs is explored. In simulation results, the DRL agent can learn to predict input traffic patterns and allocate slots with the necessary granularity of 5 Gbps in the FlexE calendar. The resulting gains are expressed in terms of the reduction of mean over-allocation of slots in the FlexE calendar in comparison to the prediction obtained from an autoregressive integrated moving average (ARIMA) model. Simulations indicate that DRL outperforms ARIMA-based prediction by up to 11.6%