Optical network scaling using spatial multiplexing
- Spurred by information technologies, worldwide data traffic is growing exponentially. Optical fiber networks form the backbone of the global data connectivity, and scaling to higher throughput and lower cost per bit is essential to ensure continued growth of information technologies. As the throughput of single-mode fibers approaches information-theoretic limits, throughput may be increased by employing multi-input multi-output transmission to multiplex information in the plurality of modes of multi-mode fibers. Chapters 2-5 address spatial multiplexing in long-haul systems. After providing an overview of networking architectures, the benefits of spatial multiplexing for communication and switching are demonstrated. Receiver-based multi-input multi-output signal processing methods enabling spatial multiplexing in multi-mode fibers are described. Two efficient algorithms for multi-input multi-output signal processing -- least-mean squares and recursive least-squares -- are studied. Their performance and complexity are analyzed. Two fundamental mode-dependent propagation effects in long-haul systems are (i) distortion caused by the disparity between modal group velocities, and (ii) fading caused by the disparity between modal gains. Modeling approaches for (i) are developed, and the corresponding statistics are derived. Methods and devices for controlling the end-to-end group delays of a system are studied. The statistics of (ii) are presented, and the implications on information-theoretic capacity limits are studied. Chapter 6 addresses spatial multiplexing in short-reach systems. Canonical architectures to enable spatial multiplexing, based on transmitter- and received-based multi-input multi-output signal processing, are presented. Short-reach systems use noncoherent direct detection because of its low complexity and low cost. However, direct detection yields information on received intensity but not received phase. To enable multi-input multi-output signal processing, we propose to use adaptive learning algorithms based on phase-retrieval for channel estimation. Three fundamental approaches for phase retrieval -- sparse training sequences, alternating minimization and convex optimization -- are proposed, and compared in terms of their performance.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Arik, Sercan O
|Stanford University, Department of Electrical Engineering.
|Ho, Keang-Po, 1968-
|Ho, Keang-Po, 1968-
|Statement of responsibility
|Sercan O. Arik.
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Sercan Omer Arik
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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