Design of Large, MEMS-Based Photonic Switches

Helkey, Roger; Bowers, John E.; Jerphagnon, O.; Kaman, V.; Keating, Adrian; Liu, Bin; Pusarla, C.; Xu, Daniel; Yuan, Shuai; Zheng, Xuezhe; Adams, Scott G.; Davis, Timothy J.

doi:10.1364/opn.13.5.000040

Cited by 18 publications

(10 citation statements)

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“…The SRAF [3] with a matrix input/output connection architecture is used to implement a sparse tapped delay line (TDL) with a large number of adaptive weights. It has a wide range of time delays and is based on micro-electromechanical systems (MEMS) technology [4] . The adaptive weights that connect the input and output are updated by the least-mean-square (LMS) algo rithm [5], and the SRAF chooses the proper time delays in order to satisfy the connection constraint.…”

Section: Introductionmentioning

confidence: 99%

A connection-constraint algorithm for a sparse adaptive photonic filter

Hwang

Shynk

Pyun

et al. 2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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A sparse reconfigurable adaptive filter (SRAF) for a photonic switch consists of a large number of input and output delays, sparse reeonfigurable conneetions, and adaptive weights. Recently, it was shown that a modified system-based (MSB) algorithm for the SRAF is more efficient than conventional algorithms such as previously introduced cross-eorrelation-based (CCB) and system-based (SB) approaehes. In this paper, we propose a eonnection constraint for the MSB algorithm that chooses the most effective elements among the entire connection matrix. The proposed method allows any input to be connected to any output with an arbitrary weight, and is more effieient than the approaches mentioned above due to its reduced computa tional eomplexity. We provide a eomputer simulation example to demonstrate the performanee of the eonnection-constraint algorithm for a system identification application.

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Section: Introductionmentioning

confidence: 99%

A connection-constraint algorithm for a sparse adaptive photonic filter

Hwang

Shynk

Pyun

et al. 2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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“…The SRAF [1] was developed for a non-blocking optical switch that is based on micro-electromechanical systems (MEMS) technology [2]- [5] and with an input/output connection architecture that can be used to implement a sparse tapped delay line (TDL) with a large number of adaptive weights and a wide range of time delays. The input and desired signals in the system are used to choose the optimal connections, and the weights are updated using the least-mean-square (LMS) algorithm [6]- [7].…”

Section: Introductionmentioning

confidence: 99%

A modified system-based adaptive algorithm for a sparse reconfigurable photonic filter

Hwang

Chang

Shynk

2011

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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Adaptive algorithms can be an important component of a sparse reconfigurable adaptive filter (SRAF) for photonic switches. In this paper, we propose a modified systembased (MSB) algorithm that not only has good performance for white and non-white input signals, but also has a reduced computational complexity compared with conventional approaches such as the previous cross-correlation-based (CCB) and system-based (SB) algorithms. In order to improve the convergence rate of the system, the MSB algorithm separately updates each row or column of the switch weight matrix. We also consider a specific structure for the intermediate desired signals, and present a computer simulation example to demonstrate the performance of the proposed SRAF algorithm for a system identification application.

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“…The SRAF [1] considered here was developed as a new application for a non-blocking optical switch that is based on micro-electromechanical systems (MEMS) technology [2], consisting of a large number of input and output delays connected together by weights that are computed by an adaptive algorithm. These tap weights can be represented by a sparse matrix (size N N  ) with the constraint that at most only one element in each row and column is nonzero; the nonzero weights combine the input and output delays so that up to 2 N different time delays are possible.…”

Section: Introductionmentioning

confidence: 99%

“…In previous work [2], a cross-correlation-based (CCB) algorithm was investigated for selecting the specific switch connections and a system-based (SB) algorithm [8] which employs a system identification formulation was also be presented. The previous connection algorithm [9] based on sequentially choosing the maximum elements might not work well when the same values exist as computing the summation of the weight values.…”

Section: Introductionmentioning

confidence: 99%

Sparse Reconfigurable Adaptive Filter with an Upgraded Connection Constraint Algorithm

Chang¹,

Hwang²

2011

International Journal of Fuzzy Logic and Intelligent Systems

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A sparse reconfigurable adaptive filter (SRAF) based on a photonic switch determines the appropriate time delays and weight values for an optical switch implementation of tapped-delay-line (TDL) systems. It is well known that the choice of switch delays is significantly important for efficiently implementing the SRAF. If the same values exist as calculating the sum of weight magnitudes for implementing the connection constraint required by the SRAF, conventional connection algorithm based on sequentially selection the maximum elements might not work perfectly. In an effort to increase the effectiveness of system identification, an upgraded connection algorithm used progressive calculation to obtain the better solution is considered in this paper. The performance of the proposed connection constraint algorithm is illustrated by computer simulation for a system identification application.

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Design of Large, MEMS-Based Photonic Switches

Cited by 18 publications

References 0 publications

A connection-constraint algorithm for a sparse adaptive photonic filter

A connection-constraint algorithm for a sparse adaptive photonic filter

A modified system-based adaptive algorithm for a sparse reconfigurable photonic filter

Sparse Reconfigurable Adaptive Filter with an Upgraded Connection Constraint Algorithm

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