Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Feedback-Based Channel Frequency Optimization in Superchannels (2305.17961v1)

Published 29 May 2023 in cs.NI

Abstract: Superchannels leverage the flexibility of elastic optical networks and pave the way to higher capacity channels in space division multiplexing (SDM) networks. A superchannel consists of subchannels to which continuous spectral grid slots are assigned. To guarantee superchannel operation, we need to account for soft failures, e.g., laser drifts causing interference between subchannels, wavelength-dependent performance variations, and filter misalignments affecting the edge subchannels. This is achieved by reserving spectral guardband between subchannels or by employing a lower modulation format. We propose a process that dynamically retunes the subchannel transmitter (TX) lasers to compensate for soft failures during operation and optimizes the total capacity or the minimum subchannel quality of transmission (QoT) performance. We use an iterative stochastic subgradient method that at each iteration probes the network and leverages monitoring information, particularly subchannels signal-to-noise ratio (SNR) values, to optimize the TX frequencies. Our results indicate that our proposed method always approaches the optima found with an exhaustive search technique, unsuitable for operating networks, irrespective of the subchannel number, modulation format, roll-off factor, filters bandwidth, and starting frequencies. Considering a four-subchannel superchannel, the proposed method achieves 2.47 dB and 3.73 dB improvements for a typical soft failure of +/- 2 GHz subchannel frequency drifts around the optimum, for the two examined objectives.

Summary

We haven't generated a summary for this paper yet.