Autonomous and Energy Efficient Lightpath Operation Based on Digital Subcarrier Multiplexing

被引:28
|
作者
Velasco, Luis [1 ]
Barzegar, Sima [1 ]
Sequeira, Diogo [1 ]
Ferrari, Alessio [2 ]
Costa, Nelson [3 ]
Curri, Vittorio [2 ]
Pedro, Joao [3 ,4 ]
Napoli, Antonio [5 ]
Ruiz, Marc [1 ]
机构
[1] Univ Politecn Cataluna, Opt Commun Grp, Barcelona 08034, Spain
[2] Politecn Torino, Dept Elect & Telecommun DET, I-10135 Turin, Italy
[3] Infinera, P-2790078 Lisbon, Portugal
[4] Inst Telecomunicacoes, P-3810193 Aveiro, Portugal
[5] Infinera, D-81377 Munich, Germany
关键词
Transponders; Optical transmitters; Optical fiber networks; Multiplexing; Phase shift keying; Automation; Optical receivers; Network automation; digital subcarrier multiplexing; energy savings; intent-based networking; reinforcement learning; OPTICAL NETWORKS; SPECTRUM ALLOCATION; PERFORMANCE; GENERATION; MITIGATION;
D O I
10.1109/JSAC.2021.3064698
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented.
引用
收藏
页码:2864 / 2877
页数:14
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