Information Capacity of Optical Fiber Channels with Zero Average Dispersion

Turitsyn, Konstantin; Derevyanko, Stanislav A.; Yurkevich, Igor V.; Turitsyn, Sergei K.

doi:10.1103/physrevlett.91.203901

Cited by 149 publications

(229 citation statements)

References 11 publications

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“…= − ue w sin φ +ũew sinφ +i ue w cos φ +ũew cosφ (32) As seen above, an exact solution to (19) is not available, but we can still obtain the first terms of an expansion of the stationary probability distribution in powers of e −w : (33) with an identical expansion for the distribution ofw and φ. The constants α 1 , α 2 depend on ξ/D and η/D, but, being related to the normalization, they cannot be determined without a knowledge of the full solution.…”

Section: B Real Gaussian U(t)mentioning

confidence: 99%

“…Its statistical median will be zero by symmetry, coming from the even parity of the Gaussian distribution of u,ũ. The phase of b does not get contributions from the first term in (33) because of the delta function in this term. For large T , the second term in the expansion dominates, making it uniform over [0, 2π), in the same manner we saw in the case of complex u.…”

Section: B Real Gaussian U(t)mentioning

confidence: 99%

“…In fact, they can both be seen as playing the role of "action" variables changing adiabatically in the presence of non-integrable perturbations. Therefore, the problem of light propagation in the fiber becomes easier to analyze in terms of the scattering data of the Zakharov-Shabat eigenproblem, especially in the presence of perturbations to (1), such as noise due to amplification or phase conjugation, which will ultimately determine the optical fiber capacity [31,32,33,34]. As a result, the description of the scattering data as a function of the input signal u(t) may provide a framework for understanding the ultimate limits of information transfer through optical fibers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Schrödinger equation with random Gaussian input: Distribution of inverse scattering data and eigenvalues

Kazakopoulos

Moustakas

2008

Phys. Rev. E

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We calculate the Lyapunov exponent for the non-Hermitian Zakharov-Shabat eigenvalue problem corresponding to the attractive non-linear Schrödinger equation with a Gaussian random pulse as initial value function. Using an extension of the Thouless formula to non-Hermitian random operators, we calculate the corresponding average density of states. We also calculate the distribution of a set of scattering data of the Zakharov-Shabat operator that determine the asymptotics of the eigenfunctions. We analyze two cases, one with circularly symmetric complex Gaussian pulses and the other with real Gaussian pulses. We discuss the implications in the context of information transmission through non-linear optical fibers.

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Section: B Real Gaussian U(t)mentioning

confidence: 99%

Section: B Real Gaussian U(t)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Schrödinger equation with random Gaussian input: Distribution of inverse scattering data and eigenvalues

Kazakopoulos

Moustakas

2008

Phys. Rev. E

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“…The channel properties are described by the conditional probability density f (r|θ), which satisfies the regularity conditions (8). The input, described by r.v.…”

Section: A Small Input Amplitude Limitmentioning

confidence: 99%

“…The theory has been applied successfully to a wide range of problems [2], including, e.g., classical and quantum computation and communication [3][4][5], optical communication [6][7][8] or quantification of different aspects of information processing in real neurons and neuronal models [9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Information capacity in the weak-signal approximation

Kostal

2010

Phys. Rev. E

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We derive an approximate expression for mutual information in a broad class of discrete-time stationary channels with continuous input, under the constraint of vanishing input amplitude or power. The approximation describes the input by its covariance matrix, while the channel properties are described by the Fisher information matrix. This separation of input and channel properties allows us to analyze the optimality conditions in a convenient way. We show that input correlations in memoryless channels do not affect channel capacity since their effect decreases fast with vanishing input amplitude or power. On the other hand, for channels with memory, properly matching the input covariances to the dependence structure of the noise may lead to almost noiseless information transfer, even for intermediate values of the noise correlations. Since many model systems described in mathematical neuroscience and biophysics operate in the high noise regime and weak-signal conditions, we believe, that the described results are of potential interest also to researchers in these areas.

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Advanced Lightwave Systems

Agrawal¹

2010

Fiber‐Optic Communication Systems

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Lightwave systems discussed so far are based on a simple digital modulation scheme in which an electrical binary bit stream modulates the intensity of an optical carrier inside an optical transmitter (on-off keying or OOK). The resulting optical signal, after its transmission through the fiber link, falls directly on an optical receiver that converts it to the original digital signal in the electrical domain. Such a scheme is referred to as intensity modulation with direct detection (IM/DD). Many alternative schemes, well known in the context of radio and microwave communication systems [l]-[3], transmit information by modulating both the amplitude and the phase of a carrier wave. Although the use of such modulation formats for optical systems was considered in the 1980s [4]-[9], it was only after the year 2000 that phase modulation of optical carriers attracted renewed attention, motivated mainly by its potential for improving the spectral efficiency of WDM systems [10]- [16]. Depending on the receiver design, such systems can be classified into two categories. In coherent lightwave systems [14], transmitted signal is detected using homodyne or heterodyne detection requiring a local oscillator. In the so-called self-coherent systems [16], the received signal is first processed optically to transfer phase information into intensity modulations and then sent to a direct-detection receiver.The motivation behind using phase encoding is two-fold. First, the sensitivity of optical receivers can be improved with a suitable design compared with that of direct detection. Second, phase-based modulation techniques allow a more efficient use of fiber bandwidth by increasing the spectral efficiency of WDM systems. This chapter pays attention to both aspects. Section 10.1 introduces new modulation formats and the transmitter and receiver designs used to implement them. Section 10.2 focuses on demodulation techniques employed at the receiver end. The bit-error rate (BER) is considered in Section 10.3 for various modulation formats and demodulation schemes. Section 10.4 focuses on the degradation of receiver sensitivity through mechanisms such as phase noise, intensity noise, polarization mismatch, and fiber dispersion. Nonlinear phase noise is discussed in Section 10.5 together with the techniques used for its compensation. Section 10.6 reviews the recent progress realized with emphasis on improvements in the spectral efficiency. The topic of ultimate channel capacity is the focus of Section 10.7.

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Information Capacity of Optical Fiber Channels with Zero Average Dispersion

Cited by 149 publications

References 11 publications

Nonlinear Schrödinger equation with random Gaussian input: Distribution of inverse scattering data and eigenvalues

Nonlinear Schrödinger equation with random Gaussian input: Distribution of inverse scattering data and eigenvalues

Information capacity in the weak-signal approximation

Advanced Lightwave Systems

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