1. Introduction

It has recently been shown that, in high bit-rate coherent optical links with no dispersion management (NDM), the nonlinear interference (NLI) is a zero-mean signal-independent additive circular complex-Gaussian noise already after a few spans [1,2]. Based on such a key observation, a nonlinear Gaussian model for NDM coherent communications has been proposed [3–5] and experimentally validated [6–8]. In wavelength division multiplexing (WDM), the nonlinear noise comes both from intrachannel nonlinearity and from interchannel nonlinearity. As we increase propagation distance, intrachannel nonlinearity eventually becomes dominant at baudrates in the range of tens of Gigabaud, such as those typically envisaged for modern coherent optical communications [9], unless some form of nonlinear equalization is employed [10–13]. However up to date such nonlinear equalization techniques at long distance and high baudrate have unmanageable hardware implementation complexity.

Building on a well-established time-domain perturbation approach [14–17], in this paper we extend the study of the nonlinear Gaussian model to the regime in which single-channel intrachannel four wave mixing (IFWM) is the dominant nonlinearity. Such a regime applies to both NDM and dispersion-managed (DM) long links at sufficiently large baudrates.

The paper, which is an extended version of [18], is organized as follows. Section 2 introduces the basics of the nonlinear Gaussian model, in which a single parameter, the NLI parameter a_NL, completely determines nonlinearity. Section 3 proves that, within the applicability of the nonlinear Gaussian model, we can analytically derive the nonlinear threshold (NLT) at a target bit error rate (BER). Section 4 derives explicit analytical expressions of a_NL for dual-polarization modulation both for NDM and for DM links under the assumption that IFWM is dominant. Section 5 finally compares the analytical NLT to the NLT derived by time-consuming Monte-Carlo simulations in [9]. The five appendices contain all the analytical derivations needed to support the results summarized in the main body of the paper. Table 1 provides a list of the main symbols used in the paper.

Table 1. List of Main Symbols Used in the Paper

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2. Nonlinear Gaussian Model

Consider a single-channel long-haul optical link with dual polarization (DP) coherent reception. Assume that both the amplified spontaneous emission (ASE) and the NLI are independent additive complex-Gaussian noises. After coherent reception with polarization demultiplexing and ideal linear electrical equalization, followed by matched filtering and ideal carrier estimation, the 2-dimensional sampled received complex field vector at sampling time t is: $\mathit{r}(t)=\sqrt{P}\mathit{U}(t)+{\mathit{n}}_L(t)+{\mathit{n}}_{NL}(t)$, where P [W] is the average signal power, U the normalized 2x1 complex signal vector, n_L the ASE, and n_NL the NLI vectors. The electrical signal-noise ratio (SNR) at the decision gate is

The main goal of this paper is to provide an approximate analytical expression of the NLI coefficient a_NL, valid for dominant IFWM, in any DM or NDM link. Such an expression will be used to analytically cross-validate recent simulation results on nonlinear threshold in DP coherent transmissions [9].

3. Nonlinear Threshold

We define the constrained NLT at reference bit error rate BER₀ (i.e., at its corresponding format-dependent SNR S₀) as the transmitted power P̂_NLT yielding the maximum of the “bell-curve” S versus P, where the maximum value is constrained to S₀. Maximization of Eq. (1) with ASE noise adjusted such that the top value is S = S₀ yields [3]

4. Nonlinear Interference coefficient

We now describe a procedure to derive closed-form analytical expressions of the NLI coefficient a_NL. Generalizing the work in [16, 17] to DP, Appendix 2 shows that, in absence of polarization mode-dispersion (PMD), the NLI field vector can be obtained from a first-order regular perturbation (RP1) as:

For a linear digital modulation we have $\mathit{U}(t)={\sum }_{k=-\infty }^{\infty }{\mathit{s}}_{k}p(t-k)$, where s_k = [X_k,Y_k]^T is the vector of constellation symbols on polarizations X and Y at time k (^T stands for transpose), and p(t) is the real, scalar common supporting pulse [19]. We are interested in the NLI at the sampling time of interest, say t = 0. We assume a Nyquist pulse p(0) = 1 and p(k) = 0 at any other integer k. Thus assuming uncorrelated symbols we have E[|U(0)|²] = E[|s_k|²] = 1. When the time-kernel is much broader than the symbol time, a regime we call IFWM dominated, then we can approximate the supporting pulse with a delta function in each field term in the double integral of Eq. (3), and the NLI term simplifies to n_NL(0) = c_NLP^3/2, with [16, 17]

Expression (5) is simple, yet it requires the explicit evaluation of the time-kernel, which is analytically known only for lossless links [16] or for a single lossy span of infinite length [20]. For practical lossy links of interest the time-kernel evaluation is a challenging numerical problem, and in this paper we seek an alternative procedure able to avoid its direct numerical computation. The idea is the following. We first approximate the double sum as

Since the time-kernel magnitude decreases for increasing τ and eventually vanishes after an effective time duration τ_M, we may then upper-bound the double sum as

What we need are expressions of both the kernel duration τ_M and of the above integral of the kernel magnitude that do not need the explicit time-kernel evaluation. We may choose τ_M ≜ μτ_rms for some positive multiplier μ of the rms width ${\tau }_{\mathit{rms}}^2={\int }_{-\infty }^{\infty }{\tau }^2{|\eta (\tau )|}^2\text{d}\tau /{{\int }_{-\infty }^{\infty }|\eta (u)|}^2\text{d}u$. If the parameter μ is chosen too large such that μτ_rms exceeds the actual time-kernel duration, then we just make the upper-bound of Eq. (7) looser. We will discuss the choice of parameter μ in the results Section. Now, for every optical link, both with and without dispersion management, a physically meaningful function is the power-weighted dispersion distribution (PWDD) J(c), representing signal power versus cumulated dispersion c [16]. Appendix 4 shows that

Thus a_NL in Eq. (5) can be upper-bounded by the following expression depending solely on integrals of J(c), which are easy to evaluate for practical links:

The derivation in Appendix 3 clearly shows that this bound is valid for any zero-mean DP modulation format with independent polarization tributaries, at a given baudrate. In other terms, in the IFWM dominated regime both constant amplitude formats such as quadrature phase shift keying (QPSK), and variable amplitude formats such as quadrature amplitude modulation (QAM) at the same average power P do generate the same nonlinear power a_NLP³. Appendix 5 derives closed-form expressions of the a_NL upper-bound of Eq. (10) for several links of interest. For instance, for NDM links we obtain for N ≳ 5:

5. Results

Figure 1 (left) shows a plot of the a_NL formula (10) versus number of spans N (solid), and numerically simulated values (symbols), for a single-channel 28 Gbaud DP-QPSK coherent transmission over single mode fiber (SMF, β₂ = −21 ps²/km, α = 0.2 dB/km, γ = 1.26 W⁻¹km⁻¹) for an Nx100 km link, both NDM and DM with 30 ps/nm (DM30) of residual dispersion per span (RDPS) and no pre-compensation. For the NDM link we used the formula (11), while for the DM case the formula (10) and Eqs. (35)–(36). In the theoretical curves we used the value μ = 6, which roughly matches the actual duration τ_M ≅ 6τ_rms of the time-kernel and gives the best fit of the shape of a_NL versus N for both links, although for NDM links the dependence of a_NL on μ is rather weak. Instead of the theoretical DP value η_p = 3/8, a smaller fitting factor η_p = 3/50 was used for DM, and η_p = 3/88 for NDM, in order to compensate for the upper-bounding in Eq. (7). We appreciate the match of theory and simulation, as well as the announced Nlog(kN) scaling law in the NDM case. The perceived NDM slope over a 50 span range is ∼ 1.25 dB/dB as in [1], although restricting the range to the first 15 spans gives ∼ 1.35 dB/dB, as we experimentally verified in a companion study [6, 7]. NLI grows faster in the DM case: a_NL has an initial slope of ∼ 2 dB/dB and then bends at larger N.

 

Fig. 1 (Left) a_NL [mW⁻²] versus spans N from Eq. (10) (solid) and simulations (symbols). DP-QPSK on Nx100 km SMF links, R=28 Gbaud. (Right) 1dB NLT vs. symbol rate R for: theory P̂₁ = P̂_NLT – 1.05 dBm, with P̂_NLT as in Eq. (2) (solid lines); simulations from [9] (symbols). DM30 = DM with 30 ps/nm RDPS.

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Figure 1 (right) shows the 1dB NLT at BER₀ = 10⁻³ versus baudrate for a DP-QPSK format for both NDM, and a DM30 link with straight-line rule (SLR) pre-compensation [20], both at 20x100 km and at 120x50 km distance. Symbols refer to single-channel simulations taken from [9], solid lines to the formula P̂₁ = P̂_NLT – 1.05 [dBm] using Eq. (2) and the same (η_p,μ) fitting factors as in Fig. 1 (left). While for DM links theory only captures the general trend versus R with obviously major discrepancies at lower R where IFWM is not dominant, the match in NDM links (optimized at 28 Gbaud through the fitting factors η_p as in Fig. 1 (left)) seems more reasonable. Notwithstanding the numerical discrepancies observed in Fig. 1 (left), which may be large for practical design purposes, the analytical NLT curves are of great theoretical importance, as they provide a first model able to confirm the general trends of NLT observed in simulations, and quickly predict the NLT qualitative trends as we vary the main system parameters.

Of course, one may play with the two fitting parameters to improve the prediction of a_NL (and thus NLT) versus symbol rate. Focusing for instance on the NDM link, Fig. 2 (left) shows a_NLversus R for a 20x100 km link. Symbols represent simulations, while the red line the theoretical a_NL of Eq. (11) with the same $({\eta }_p,\mu )=(\frac{3}{88},6)$ parameters as in Fig. 1. We can decrease the gap to simulations by using the “optimized” parameters $({\eta }_p,\mu )=(\frac{3}{31},0.02)$ as shown by the magenta line, i.e., by pretending the time-kernel duration is smaller than its actual value. However this comes at the price of a reduced accuracy of the a_NL versus spans N as shown in Fig. 2 (right).

 

Fig. 2 (Left) a_NL versus symbol rate R for DP-QPSK 20x100 km NDM link. Symbols: simulations. Red line: theory (11) with $({\eta }_p,\mu )=(\frac{3}{88},6)$ as in Fig. 1. Magenta line: theory (10) with optimized $({\eta }_p,\mu )=(\frac{3}{31},0.02)$. (Right) a_NL versus spans N for 28 Gbaud DP-QPSK NDM link. Symbols: simulations. Red and Magenta lines: theory.

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We verified that the main reason of the inability of the model to correctly predict the shape of a_NL versus symbol rate R (hence NLT versus R) over the wide range shown Figs. 1 and 2 stems from the key approximation [16]

6. Conclusions

We provided a time-domain model of NLI in IFWM dominated links, which reasonably models NDM links, as well as high baudrate DM links. Such a model provides a quick qualitative tool to compare transmission link parameters in terms of their impact on received SNR. The model has two fitting parameters which may be optimized to best fit simulations, although it has difficulties in reproducing the correct behavior of a_NL versus symbol rate over the wide range shown in Figs. 1–2. More work is needed on this issue to improve its accuracy.

Appendix 1: NLT at fixed distance N and fixed SNR

In [3,5] it is shown that the power that maximizes the SNR, called the unconstrained NLT, is obtained when ASE power is twice the nonlinear noise power. Hence explicitly the unconstrained NLT is

(12)$$P_{NLT}={\left(\frac{N_A}{2a_{NL}}\right)}^{\frac{1}{3}}$$

and the corresponding maximum SNR value at NLT is

(13)$$S_{NLT}\equiv \frac{P_{NLT}}{\frac{3}{2}{N}_A}={\left(3^3{a}_{NL}{(\frac{N_A}{2})}^2\right)}^{-\frac{1}{3}}$$

with an SNR penalty with respect to linear propagation of $SP=10\text{Log}\frac{3}{2}\sim 1.76\text{dB}$.

Figure 3 (left) shows an example of the “bell curve” S versus P, where a reference SNR S₀ was fixed, and the smallest and largest intersections of the SNR vs. P curve with the line at level S₀ occur at power P_m and P_M, respectively. The corresponding penalties are marked as SP_m and SP_M in the figure. The two intersections coincide at a specific value of ASE noise Ñ_A, and the corresponding power value P̂_NLT is called the NLT at S₀, or the constrained NLT [21]. Clearly, SP = 1.76 dB also at the constrained NLT. At N_A > Ñ_A no intersections are found, i.e., the target SNR S₀ is unachievable. Figure 3 (right) reports the sensitivity penalty values SP_m and SP_M at their respective powers P_m and P_M as we vary the ASE noise over all achievable values N_A ≤ Ñ_A. The graph in Fig. 3 (right) is routinely used in system design [21]. Since both N and S₀ are fixed, we stress that the SP vs P points are actually obtained by using varying amounts of ASE noise. For each N_A, the two corresponding (P_m, SP_m) and (P_M, SP_M) points are found at the intersection of the SP curve with the unit slope straight line $S_L^{dB}=P^{dB}-N_A^{dB}$, as shown in Fig. 3 (right).

 

Fig. 3 (Left) example of SNR [dB] vs. P [dBm] “Bell” curve and its roots at S₀ = 12 dB, along with graphical definition of constrained NLT P̂_NLT at 1.76 dB of penalty; (Right) SNR penalty $SP=S^{dB}-S_0^{dB}$ at reference S₀ vs. P [dBm]. It is shown in Eq. (18) that the constrained NLT at 1dB of penalty is below P̂_NLT by 1.05 dB.

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Objective of this Appendix is to provide explicit expressions of SP_m, SP_M, P̂_NLT, and the NLT P̂₁ at SP = 1 dB.

i) Expressions of SP_m, SP_M and P̂_NLT at N_A ≤ Ñ_A

Inverting Eq. (13) at S_NLT = S₀ we get

(14)$${\widehat{N}}_A=\frac{2}{{(3S_0)}^{3/2}{a}_{NL}^{1/2}}.$$

From Eq. (1)P_m and P_M are seen to solve the cubic equation $P^3-\frac{1}{S_0{a}_{NL}}P+\frac{N_A}{a_{NL}}=0$. Cardan’s solutions ([22], p. 23) of the cubic equation y³ + py+q = 0 are discriminated by the value of the discriminant $Q={(\frac{p}{3})}^3+{(\frac{q}{2})}^2$. When Q < 0 the cubic has 3 real roots, which can be expressed in trigonometric form as

(15)$$y_1=2\sqrt{-\frac{p}{3}}\text{cos}(\frac{\alpha }{3})\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{y}_{2,3}=-2\sqrt{-\frac{p}{3}}\text{cos}(\frac{\alpha }{3}\pm \frac{\pi }{3})$$

with $\alpha =\text{arcos}(-\sqrt{\frac{{(q/2)}^2}{-{(\frac{p}{3})}^3}})$. In our case $\frac{p}{3}=-\frac{1}{3S_0{a}_{NL}}$, $\frac{q}{2}=\frac{N_A}{2a_{NL}}$, so $Q=-\frac{1}{{(3S_0{a}_{NL})}^3}+\frac{{(N_A/2)}^2}{a_{NL}^2}$, and using Eq. (14) $Q=-\frac{{({\widehat{N}}_A/2)}^2}{a_{NL}^2}+\frac{{(N_A/2)}^2}{a_{NL}^2}<0$ for all N_A ≤ Ñ_A. In such a case, α = arcos(−N_A/Ñ_A), with 90° < α ≤ 180°, and thus $30°<\frac{\alpha }{3}\le 60°$, so that $-\text{cos}(\frac{\alpha }{3}+60°)>0$, i.e. y₁ is the largest positive solution, while y₂ the smallest positive solution corresponds to the + sign in Eq. (15): $P_M=\frac{2}{\sqrt{3S_0{a}_{NL}}}\text{cos}(\frac{\alpha }{3})$ and $P_m=\frac{2}{\sqrt{3S_0{a}_{NL}}}\text{cos}(\frac{2\pi -\alpha }{3})$. Using Eq. (14), we rewrite the solutions explicitly as:

(16)$$P_M=3S_0{\widehat{N}}_A\text{cos}(\frac{\text{arcos}(-N_A/{\widehat{N}}_A)}{3})\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{P}_m=3S_0{\widehat{N}}_A\text{cos}(\frac{2\pi -\text{arcos}(-N_A/{\widehat{N}}_A)}{3}).$$

From Eq. (1), the sensitivity penalty is the ratio of the linear SNR P/N_A and the nonlinear SNR S₀: $S{P}_{m,M}=\frac{P_{m,M}/N_A}{S_0}$, hence finally the sought SP values are

(17)$$S{P}_M=3\frac{{\widehat{N}}_A}{N_A}\text{cos}(\frac{\text{arcos}(-N_A/{\widehat{N}}_A)}{3})\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}S{P}_m=3\frac{{\widehat{N}}_A}{N_A}\text{cos}(\frac{2\pi -\text{arcos}(-N_A/{\widehat{N}}_A)}{3}).$$

As a check, when N_A = Ñ_A the angle α = π, cos(π/3) = 1/2 and we obtain the known value $S{P}_{m,M}=\frac{3}{2}$, and the NLT explicit value is ${\widehat{P}}_{NLT}=\frac{3}{2}{S}_0{\widehat{N}}_A=\frac{1}{{(3S_0{a}_{NL})}^{1/2}}$, which can more directly be obtained by substituting Eq. (14) into Eq. (12).

ii) Expression of P̂ ₁

We are now ready to answer the following question: at which power P̂₁ does the SP w.r.t. S₀ reach a value of 1 dB? From Eq. (17), letting SP_m = 10^0.1 ≅ 1.26 and x = N_A/Ñ_A, we look for the solution of equation ${10}^{0.1}=\frac{3}{x}\text{cos}(\frac{2\pi -\text{arcos}(-x)}{3})$, which is x₁ ≅ 0.936. Hence

(18)$$\frac{{\widehat{P}}_{NLT}}{{\widehat{P}}_1}=\frac{\frac{3}{2}{S}_0{\widehat{N}}_A}{3S_0{\widehat{N}}_A\text{cos}(\frac{2\pi -\text{arcos}(-x_1)}{3})}\cong 1.273$$

which means P̂₁ is 10log₁₀(1.273) ≅ 1.05 dB below the NLT at 1.76 dB penalty. This result is also sketched in Fig. 3 (right).

Appendix 2: Regular Perturbation Solution

We will generalize here the scalar case time-domain analysis presented in [16, 17], which extends previous analytical work based on Gaussian supporting pulses [14, 15]. The propagation equation of a DP single channel in the retarded normalized time frame t (physical time normalized to the symbol interval T = 1/R), can be described in absence of PMD by the Manakov-Nonlinear Schroedinger equation (M-NLSE) in engineering notation as ([23], Eq. (73))

(19)$$\frac{\partial \mathit{A}(z,t)}{\partial z}=\frac{g(z)}{2}\mathit{A}(z,t)+\frac{j{\beta }_2(z)R^2}{2}\frac{{\partial }^2\mathit{A}(z,t)}{\partial t^2}-j\gamma (z)|{\mathit{A}}^2|\mathit{A}(z,t)$$

where A = [A_x, A_y]^T is the signal field envelope on the two polarizations (in $\sqrt{W}$), γ(z) is 8/9 times the nonlinear coefficient, g(z) is the net gain/attenuation coefficient per unit length, β₂(z) is the dispersion coefficient, and such parameters are z–varying functions, with span k ending at coordinate kz_A, k = 1,..., N. The function $G(z)={\text{e}}^{{\int }_0^{z}g(s)ds}$ is the power gain from 0 to z. Since the nonlinear term may be also written as |A|²A = A(A^†A), by taking the Fourier transform of Eq. (19) we get:

(20)$$\begin{array}{l}\frac{\partial \tilde{\text{A}}(z,\omega )}{\partial z}=\frac{g(z)-j{\omega }^2{\beta }_2(z)R^2}{2}\tilde{\text{A}}(z,\omega )-\\ j\gamma (z){\int \int }_{-\infty }^{\infty }\tilde{\text{A}}(z,\omega +{\omega }_1){\tilde{\text{A}}}^{†}(z,\omega +{\omega }_1+{\omega }_2)\tilde{\text{A}}(z,\omega +{\omega }_2)\frac{d{\omega }_1}{2\pi }\frac{d{\omega }_2}{2\pi }\end{array}$$

where for any function g(t) we define its Fourier transform (with engineering sign) as $\tilde{g}(\omega )={\int }_{-\infty }^{\infty }g(t){\text{e}}^{-j\omega t}dt$, where ω = 2πf, and f is the frequency normalized to the baud rate. The input modulated field Ã₀(ω) may be pre-chirped to give $\tilde{\text{A}}(0,\omega )\equiv {\tilde{\text{A}}}_0(\omega ){\text{e}}^{j\frac{{\omega }^2}{2}{\xi }_{\mathit{pre}}}$, where ξ_pre ≜ −L_preβ_preR² is the normalized cumulated dispersion in the pre-compensation fiber of dispersion coefficient β_pre and length L_pre. Now make the change of variable

(21)$$\tilde{\text{A}}(z,\omega )=\sqrt{P_0}{e}^{\frac{\text{ln}G(z)+jC(z){\omega }^2}{2}}\tilde{\text{U}}(z,\omega )$$

where P₀ is a reference normalizing power, and $C(z)\triangleq {\xi }_{\mathit{pre}}-R^2{\int }_0^z{\beta }_2(s)\underset{\dot{}}{\text{s}}$ is the normalized cumulated dispersion up to z. Differentiating Eq. (21) and substituting into Eq. (20) one gets:

(22)$$\begin{array}{l}\frac{\partial \tilde{\text{U}}(z,\omega )}{\partial z}=-j\gamma (z)P_{0}G(z){\int \int }_{-\infty }^{\infty }{\text{e}}^{-jC(z){\omega }_1{\omega }_2}\tilde{\text{U}}(z,\omega +{\omega }_1).\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cdot {\tilde{\text{U}}}^{†}(z,\omega +{\omega }_1+{\omega }_2)\tilde{\text{U}}(z,\omega +{\omega }_2)\frac{d{\omega }_1}{2\pi }\frac{d{\omega }_2}{2\pi }.\end{array}$$

Now define the nonlinear phase (referred to nominal power P₀) as Φ_NL(P₀) ≜ P₀L < γG >, where L = Nz_A is the total link length, and

(23)$$<\gamma G>\triangleq \frac{1}{L}{\int }_0^L\gamma (s)G(s)ds.$$

Using such definitions, multiply and divide Eq. (22) by L < γG >, thus finally obtaining the Manakov dispersion-managed NLSE (M-DM-NLSE) in the form:

(24)$$\begin{array}{l}\frac{\partial \tilde{\text{U}}(z,\omega )}{\partial z}=-j{\mathrm{\Phi }}_{NL}(P_0){\int \int }_{-\infty }^{\infty }\frac{\gamma (z)G(z){\text{e}}^{-jC(z){\omega }_1{\omega }_2}}{L<\gamma G>}\tilde{\text{U}}(z,\omega +{\omega }_1).\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cdot {\tilde{\text{U}}}^{†}(z,\omega +{\omega }_1+{\omega }_2)\tilde{\text{U}}(z,\omega +{\omega }_2)\frac{d{\omega }_1}{2\pi }\frac{d{\omega }_2}{2\pi }.\end{array}$$

If the field terms in the integrand in Eq. (24) are approximated as z-independent, then Eq. (24) can be integrated on the link [0,L] to yield the first-order regular perturbation (RP1) solution:

$$\begin{array}{l}\tilde{\text{U}}(L,\omega )=\tilde{\text{U}}(0,\omega )-j{\mathrm{\Phi }}_{NL}(P_0){\int \int }_{-\infty }^{\infty }\frac{{\int }_0^L\gamma (s)G(s){\text{e}}^{-jC(s){\omega }_1{\omega }_2}ds}{L<\gamma G>}.\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\cdot \tilde{\text{U}}(0,\omega +{\omega }_1){\tilde{\text{U}}}^{†}(0,\omega +{\omega }_1+{\omega }_2)\tilde{\text{U}}(0,\omega +{\omega }_2)\frac{d{\omega }_1}{2\pi }\frac{d{\omega }_2}{2\pi }\end{array}$$

with $\tilde{\text{U}}(0,\omega )\equiv {\tilde{\text{A}}}_0(\omega )/\sqrt{P_0}$ the initial condition. Define now the (scalar) frequency-kernel as

(25)$$\tilde{\eta }(w)\triangleq \frac{{\int }_0^L\gamma (s)G(s){\text{e}}^{-jC(s)w}ds}{{\int }_0^L\gamma (s)G(s)ds}$$

so that the RP1 solution writes as Ũ(L,ω) = Ũ(0,ω) + Ũ_NL(ω), with

(26)$${\tilde{\text{U}}}_{NL}(\omega )\triangleq -j{\mathrm{\Phi }}_{NL}(P_0){\int \int }_{-\infty }^{\infty }\tilde{\eta }({\omega }_1{\omega }_2)\tilde{\text{U}}(0,\omega +{\omega }_1){\tilde{\text{U}}}^{†}(0,\omega +{\omega }_1+{\omega }_2)\tilde{\text{U}}(0,\omega +{\omega }_2)\frac{d{\omega }_1}{2\pi }\frac{d{\omega }_2}{2\pi }.$$

If one adds at the receiver a post-compensating fiber with accumulated normalized dispersion ξ_post, one finally has the RP1 field at the receiver as:

(27)$$\tilde{\text{r}}(\omega )=\sqrt{P_0}{e}^{\frac{\text{ln}G(L)+j{\xi }_{\mathit{tot}}{\omega }^2}{2}}[\tilde{\text{U}}(0,\omega )+{\tilde{\text{U}}}_{NL}(\omega )]$$

where ξ_tot ≜ C(L) +ξ_post. For a “power-transparent” line G(L) = 1, and typically for coherent systems ξ_tot = 0, so that after chromatic dispersion compensation at the receiver we have $\tilde{\text{r}}(\omega )=\sqrt{P_0}[\tilde{\text{U}}(0,\omega )+{\tilde{\text{U}}}_{NL}(\omega )]$, and in the time domain $\mathit{r}(t)=\sqrt{P_0}\mathit{U}(0,t)+{\mathit{n}}_{NL}(t)$ where n_NL(t) is the inverse Fourier transform of $\sqrt{P_0}{\tilde{\text{U}}}_{NL}(\omega )$ and thus has the expression reported in Eq. (3). Note that the reference power P₀ can be freely chosen to simplify the analysis.

Appendix 3: Power of NL term

We need to evaluate a_NL = E[|c_NL|²], where we rewrite c_NL in Eq. (4) as:

(28)$$\begin{array}{lll}{\mathit{c}}_{NL}\hfill & =\hfill & -jL<\gamma G>\sum _{m,n}{\mathit{s}}_m{\mathit{s}}_{m+n}^{†}{\mathit{s}}_n\eta (mn)\hfill \\ \hfill & =\hfill & -jL<\gamma G>\sum _{m,n}\left(\begin{array}{c}{X}_m(X_n{X}_{m+n}^{*}+Y_n{Y}_{m+n}^{*})\\ Y_m(X_n{X}_{m+n}^{*}+Y_n{Y}_{m+n}^{*})\end{array}\right)\eta (nm)\hfill \end{array}$$

and the summation runs over all signed non-zero m,n integer pairs, which we visualize as points on the (m,n) plane. Each point corresponds to a pair of RVs, one per polarization, as given by the big parenthesis in Eq. (28). When we swap m ↔ n the constituent random variables (RV) $X_n{X}_m{X}_{n+m}^{*}$ and $Y_n{Y}_m{Y}_{n+m}^{*}$ (Type I) remain unchanged: they represent the same RV, which in the double summation in Eq. (28) must be counted d_f = 2 times, and the double summation for them then runs on half the (m,n) plane, i.e., for instance on the pairs (m,n) below and on the bisectrix m = n (actually the points at which m = n have degeneracy d_f = 1, but we will disregard this subtlety for very broad time-kernels, and use d_f = 2 even for them), except the axes m = 0 and n = 0 which collect the IXPM and pure SPM terms. The situation for Type I RVs is summarized in Fig. 4. On the contrary, when we swap m ↔ n, the RVs $X_m{Y}_n{Y}_{m+n}^{*}$ and $Y_m{X}_n{X}_{m+n}^{*}$ (Type II) do change into new RVs.

 

Fig. 4 Colored dots represent IFWM points on (m,n) plane when infinitely many precursors and postcursors are taken into account. They all have degeneracy factor d_f = 2, except those on the m = n line (partially degenerate) which have degeneracy d_f = 1. Points on axes (IXPM) and (0,0) point (pure SPM) should not be included in IFWM count.

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We will restrict the analysis to common DP coherent modulation formats, for which E[s_k] = 0. Moreover, we choose E[|s_k|²] = 1 to be the unit power of the normalized constellation symbols s_k = [X_k,Y_k]^T. Symbols are assumed to be uncorrelated in time. Tributary symbols X_k,Y_k are also zero-mean uncorrelated and have the same power E[|X_k|²] = E[|Y_k|²] = 1/2. Then the RV ${\mathit{S}}_{mn}\triangleq {\mathit{s}}_m{\mathit{s}}_{m+n}^{†}{\mathit{s}}_n$ is zero-mean, and so is c_NL. Therefore

$$a_{NL}=\mathit{Var}\left[{\mathit{c}}_{NL}\right]={(L<\gamma G>)}^2\sum _{m,n}\mathit{Var}\left[{\mathit{S}}_{mn}\right]{|\eta (mn)|}^2$$

since the RVs adding up to build c_NL are uncorrelated. Taking into account the degeneracy factor d_f we thus get

$$\frac{a_{NL}}{{(L<\gamma G>)}^2}=2\left(\sum _{\begin{array}{l}(m,n):\\ m\le n\end{array}}\frac{1}{8}{d}_f^2{|\eta (mn)|}^2+\sum _{(m,n)}\frac{1}{8}{|\eta (mn)|}^2\right)$$

where the first sum is on Type I RVs and accounts for the “self-polarization” variance, while the second sum is on Type II RVs and accounts for the variance due to cross-polarization crosstalk between X and Y. The factor 2 accounts for the contribution to NL variance from the two polarizations, and 1/8 = (E[|X|²])³ = E[|X|²](E[|Y|²])² is the variance of both Types of RVs. Using the fact that the magnitude square of the kernel |η(mn)|² is the same on the 4 quadrants of the (m,n) plane, the above further simplifies to

(29)$$\frac{a_{NL}^{DP}}{{(L<\gamma G>)}^2}=2\left(\frac{2}{8}{d}_f^2+\frac{4}{8}\right)\sum _{m=1}^{\infty }\sum _{n=1}^{\infty }{|\eta (mn)|}^2$$

where we added the superscript DP for clarity. The per-component $a_{NL}^{pc}$ in DP (such that $a_{NL}^{pc}{P}_0^3$ is the NL variance on each component and P₀ = P/2 is the per-component power) is obtained using E[|X|²] = 1, E[|Y|²] = 1 (i.e., normalizing the M-DM-NLSE of Eq. (24) to P₀):

(30)$$\frac{a_{NL}^{pc}}{{(L<\gamma G>)}^2}=(2d_f^2+4)\sum _{m=1}^{\infty }\sum _{n=1}^{\infty }{|\eta (mn)|}^2.$$

The result for SP is obtained by using E[|X|²] = 1 and keeping only the Type I RV, hence

$$\frac{a_{NL}^{SP}}{{(L<\gamma G>)}^2}=(2d_f^2)\sum _{m=1}^{\infty }\sum _{n=1}^{\infty }{|\eta (mn)|}^2.$$

We clearly see from Eqs. (29) and (30) that variance coming from Type I RVs (the one present also in SP transmission) is twice that due to cross-polarization, hence $a_{NL}^{pc}=\frac{3}{2}{a}_{NL}^{SP}$, and therefore $a_{NL}^{DP}=\frac{3}{8}{a}_{NL}^{SP}$. Figure 5 shows both $a_{NL}^{SP}$ estimated from SP transmission, and $a_{NL}^{pc}$ from DP transmission of a single QPSK modulated channel in a 20x100 km single-mode fiber (SMF) NDM link. We observe the convergence of the gap to the value 3/2 predicted by theory already at 28 Gbaud. When convergence is reached, we are in the “IFWM dominated regime”. Note that $a_{NL}^{pc}$ is 6 dB larger than $a_{NL}^{DP}$, as confirmed by the simulated a_NL in Fig. 2 (left).

 

Fig. 5 a_NL versus baudrate in 20x100 km SMF NDM coherent link with SP- and DP-QPSK single channel transmission. $a_{NL}^{pc}$ is the per-component NLI coefficient in DP, with $a_{NL}^{pc}=4a_{NL}^{DP}$. The figure shows convergence of $a_{NL}^{pc}$ to the value $\frac{3}{2}{a}_{NL}^{SP}$ theoretically predicted when IFWM is dominant.

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Appendix 4

In this Appendix we prove formulas (8)–(9).

We start by recalling two important results that can easily be derived from [16]:

1. the PWDD J(c) is the inverse 1D Fourier transform of the frequency kernel considered as a function of the single variable w = ω₁ω₂: $J(c)={𝒡}^{-1}\left[\tilde{\eta }(w)\right]\equiv {\int }_{-\infty }^{\infty }\tilde{\eta }(w){\text{e}}^{jwc}\frac{dw}{d\pi }$. Since η̃(w) is Hermitian, as per Eq. (25), then J(c) is real.
2. the time-kernel η(τ) seen as a function of the single variable τ = t₁t₂ can be obtained as the following inverse 1D Fourier transform: $\eta (\tau )={𝒡}^{-1}\left[\frac{1}{|\omega |}J(\frac{1}{\omega })\right]$.

We thus can prove the following two results:

1. (31)$${\int }_{-\infty }^{\infty }{|\eta (\tau )|}^{2}d\tau ={\int }_{-\infty }^{\infty }{J}^2(c)\frac{dc}{2\pi }$$

   Proof: from Parseval’s theorem for Fourier pairs we have

   $${\int }_{-\infty }^{\infty }{|\eta (\tau )|}^{2}d\tau ={\int }_{-\infty }^{\infty }{\left|\frac{1}{|{\omega }_1|}J(\frac{1}{\omega })\right|}^2\frac{d\omega }{2\pi }={\int }_{-\infty }^{\infty }{J}^2(\frac{1}{\omega })\frac{d\omega }{{\omega }^2}\frac{1}{2\pi }$$
   and after the change of variable c = 1/ω we finally get Eq. (31). Since the Fourier transform of η(τ) is real, then η(−τ) = η(τ)^*, and thus |η(τ)|² is even. Hence ${\int }_0^{\infty }{|\eta (\tau )|}^{2}d\tau =\frac{1}{2}{\int }_{-\infty }^{\infty }{J}^2(c)\frac{dc}{2\pi }$.

2. (32)$${\int }_{-\infty }^{\infty }{\tau }^2{|\eta (\tau )|}^{2}d\tau ={\int }_{-\infty }^{\infty }{c}^2{[J(c)+c{J}^{\prime }(c)]}^2\frac{dc}{2\pi }.$$

   Proof: we know that time function η(τ) has real Fourier transform $V(\omega )=\frac{1}{|\omega |}J(\frac{1}{\omega })$, since J(c) is real. Hence τη(τ) has transform $j\frac{dV(\omega )}{d\omega }$ and by Parseval’s theorem then ${\int }_{-\infty }^{\infty }{\tau }^2{|\eta (\tau )|}^{2}d\tau ={\int }_{-\infty }^{\infty }{(\frac{dV(\omega )}{d\omega })}^2\frac{d\omega }{2\pi }$.

Now, for ω > 0, $\frac{dV(\omega )}{d\omega }=\frac{d}{d\omega }(\frac{1}{\omega }J(\frac{1}{\omega }))$ and by the change c = 1/ω: $\frac{dV(\omega )}{d\omega }=\frac{d}{dc}(cJ(c))\cdot \frac{dc}{d\omega }=\left[J(c)+c{J}^{\prime }(c)\right](\frac{-1}{{\omega }^2})$, where $J^{\prime }(c)\triangleq \frac{d}{dc}J(c)$. Similarly, for ω < 0, $\frac{dV(\omega )}{d\omega }=-\left[J(c)+c{J}^{\prime }(c)\right](\frac{-1}{{\omega }^2})$. Hence in general $\frac{dV(\omega )}{d\omega }=\left[J(\frac{1}{\omega })+\frac{1}{\omega }{J}^{\prime }(\frac{1}{\omega })\right](\frac{-\text{sgn}(\omega )}{{\omega }^2})$ so ${(\frac{dV(\omega )}{d\omega })}^2={[J(\frac{1}{\omega })+\frac{1}{\omega }{J}^{\prime }(\frac{1}{\omega })]}^2\frac{1}{{\omega }^4}$, and thus by the change c = 1/ω we get Eq. (32).

Appendix 5

We compute in this Appendix the closed-form expression of the upper-bound Eq. (10) for NDM and DM links. In these calculations, the term $NUM={\int }_{-\infty }^{\infty }{c}^2{[J(c)+c{J}^{\prime }(c)]}^2\frac{dc}{d\pi }$ is the most critical: if the PWDD J(c) has a discontinuity at c₀ ≠ 0, then J′(c) has a term δ(c – c₀) which causes NUM (and thus the kernel width ${\tau }_{rms}=\sqrt{NUM/DEN}$) to diverge to infinity. In such a case, which occurs in most links of interest, the double sum A_lim in Eq. (6) still converges, and we verified that the value ${\tau }_{rms}^{\prime }=\sqrt{NU{M}^{\prime }/DEN}$ obtained by neglecting the Dirac deltas in J′(c) still provides a meaningful time scale for measuring the width of the time-kernel.

NDM link without pre-compensation

Consider an NDM link composed of N identical spans of length $z_A\gg \frac{1}{\alpha }$, with fiber dispersion parameter β₂ [ps²/km]. The fiber strength is defined as $𝒮=-\frac{{\beta }_2}{\alpha }{R}^2$ [20]. Assuming 𝒮 > 0, the PWDD is [17]:

(33)$$J_{NDM}(c)=\frac{1}{N}\sum _{k=0}^{N-1}{J}_s(c-k{\xi }_s)$$

where $J_s(c)=\frac{1}{𝒮}{\text{e}}^{-\frac{c}{𝒮}}U(c)$, with U(c) the unit step function, and the normalized cumulated dispersion per span is ξ_s = −β₂z_AR² = αz_A𝒮. For instance, Fig. 6 (left) shows J_NDM(c) for a 20x100 km SMF NDM system such as the one whose NLT is reported in Fig. 1 (right). Dropping the subscript NDM for brevity, we get $J^{\prime }(c)=\frac{1}{N}{\sum }_{k=0}^{N-1}{J}_s^{\prime }(c-k{\xi }_s)$ and ${(J(c)+c{J}^{\prime }(c))}^2=\frac{1}{N^2}{\sum }_{k=0}^{N-1}{(J_s(c-k{\xi }_s)+c{J}_s^{\prime }(c-k{\xi }_s))}^2$ because the PWDDs of the various spans practically do not overlap. Hence

$$NUM=\frac{{\sum }_{k=0}^{N-1}{\int }_0^{\infty }{c}^2{(J_s(c-k{\xi }_s)+c{J}_s^{\prime }(c-k{\xi }_s))}^{2}dc}{2\pi N^2}=\frac{2{\sum }_4{\xi }_s^4+2{\sum }_2{\xi }_s^2{𝒮}^2+2{\sum }_1{\xi }_s{𝒮}^3+{𝒮}^4}{8\pi N^2{𝒮}^3}$$

where ${\sum }_1={\sum }_{k=0}^{N-1}k=\frac{(N-1)N}{2}$, ${\sum }_2={\sum }_{k=0}^{N-1}{k}^2=\frac{(N-1)N(2N-1)}{6}$, and ${\sum }_4={\sum }_{k=0}^{N-1}{k}^4=\frac{(N-1)N(2N-1)(3N^2-3N-1)}{30}$. Similarly,

$$DEN=\frac{{\sum }_{k=0}^{N-1}{\int }_0^{\infty }{(J_s(c-k{\xi }_s))}^{2}dc}{2\pi N^2}=\frac{1}{4\pi N𝒮}.$$

 

Fig. 6 PWDD versus normalized cumulated dispersion on SMF fiber link (D = 17 ps/nm/km, α = 0.2 dB/km) and transmission at R = 28 Gbaud, corresponding to strength 𝒮 = 0.35, for (Left) N = 20 span NDM system with span length z_A = 100 km (Right) N = 120 span DM system at 30 ps/nm RDPS and span length z_A = 50 km.

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For large N, we get ${\sum }_k\cong \frac{N^{k+1}}{k+1}$, hence

$${\tau }_{\mathit{rms}}^{NDM}\cong {\left(\frac{2\frac{N^4}{5}{\xi }_s^4+2\frac{N^2}{3}{\xi }_s^2{𝒮}^2+2\frac{N}{2}{\xi }_s{𝒮}^3}{2{𝒮}^2}\right)}^{\frac{1}{2}}=\frac{1}{\sqrt{5}}{(\alpha z_{A}N)}^2𝒮$$

and the time-kernel width is seen to scale with N². Note that we ignored the delta terms in J′(c).

DM link with small RDPS

Assuming RDPS≪ D/α (D is fiber dispersion) we can neglect the ripples in the PWDD [16,17] and use the smooth approximation (we assume 𝒮 > 0)

(34)$$J_{DM}(c)=\{\begin{array}{ll}\frac{1-{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}}{𝒮}}}{{\xi }_{\mathit{in}}}\hfill & \text{if}\hspace{0.17em}{\xi }_{\mathit{pre}}<c<{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}\hfill \\ \frac{{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}-{\xi }_{\mathit{in}}}{𝒮}}-{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}}{𝒮}}}{{\xi }_{\mathit{in}}}\hfill & \text{if}\hspace{0.17em}c>{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}\hfill \end{array}$$

with normalized pre-compensation ${\xi }_{\mathit{pre}}=|{\beta }_{\mathit{pre}}^{\prime }|R^2=\frac{D_{\mathit{pre}}}{D/\alpha }𝒮$ (where β′_pre [ps²] and D_pre [ps/nm] are the pre-compensation parameters) and total in-line dispersion ξ_in = Nξ_s, with normalized per-span residual dispersion ${\xi }_s=|{\beta }_s|R^2=\frac{RDPS}{D/\alpha }𝒮$ (where β_s [ps²] and RDPS [ps/nm] are the residual dispersion parameters per span). For instance, Fig. 6 (right) shows the true J(c) for a 120x50 km SMF DM system with 30 ps/nm RDPS and SLR pre-compensation, such as the one whose NLT is reported in Fig. 1 (right); J_NDM(c) in Eq. (34) represents the smooth average of the true J(c). Dropping for brevity the subscript DM:

$${(J+c{J}^{\prime })}^2=\{\begin{array}{ll}\frac{{\left[1-{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}}{𝒮}}(1-\frac{c}{𝒮})\right]}^2}{{\xi }_{\mathit{in}}^2}\hfill & \text{if}\hspace{0.17em}{\xi }_{\mathit{pre}}<c<{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}\hfill \\ \frac{{(1-\frac{c}{𝒮})}^2{\text{e}}^{-\frac{2(c-{\xi }_{\mathit{pre}})}{𝒮}}{(1-{\text{e}}^{-\frac{{\xi }_{\mathit{in}}}{𝒮}})}^2}{{\xi }_{\mathit{in}}^2}\hfill & \text{if}\hspace{0.17em}c>{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}\hfill \end{array}$$

and thus $NUM=\frac{1}{{\xi }_{\mathit{in}}^2}\{{{\int }_{{\xi }_{\mathit{pre}}}^{{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}}{c}^2{\left[1-{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}}{𝒮}}(1-\frac{c}{𝒮})\right]}^2\frac{dc}{2\pi }+\left(1-{\text{e}}^{-\frac{{\xi }_{\mathit{in}}}{𝒮}}\right)}^2{\int }_{{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}}^{\infty }{c}^2{(1-\frac{c}{𝒮})}^2{\text{e}}^{-\frac{2(c-{\xi }_{\mathit{pre}})}{𝒮}}\frac{dc}{2\pi }\}$. Long calculations lead to

(35)$$\begin{array}{l}NUM=\frac{1}{2\pi {\xi }_{\mathit{in}}^{2}6𝒮}\{\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}-{\text{e}}^{-\frac{{\xi }_s}{𝒮}}[6{({\xi }_{\mathit{in}}+{\xi }_{\mathit{pre}})}^4+12{({\xi }_{\mathit{in}}+{\xi }_{\mathit{pre}})}^3𝒮+30{({\xi }_{\mathit{in}}+{\xi }_{\mathit{pre}})}^2{𝒮}^2+54({\xi }_{\mathit{in}}+{\xi }_{\mathit{pre}}){𝒮}^3+\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}+51{𝒮}^4]+[3{\xi }_{\mathit{in}}^4+6{\xi }_{\mathit{pre}}^4+12{\xi }_{\mathit{pre}}^3𝒮+30{\xi }_{\mathit{pre}}^2{𝒮}^2+54{\xi }_{\mathit{pre}}{𝒮}^3+51{𝒮}^4+2{\xi }_{\mathit{in}}^3(6{\xi }_{\mathit{pre}}+𝒮)+\\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}+3{\xi }_{\mathit{in}}(2{\xi }_{\mathit{pre}}+𝒮)(2{\xi }_{\mathit{pre}}^2+{𝒮}^2)+3{\xi }_{\mathit{in}}^2(6{\xi }_{\mathit{pre}}^2+2{\xi }_{\mathit{pre}}𝒮+{𝒮}^2)]\}.\end{array}$$

Similarly, $DEN=\frac{1}{2\pi {\xi }_{\mathit{in}}^2}\{{{\int }_{{\xi }_{\mathit{pre}}}^{{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}}\left[1-{\text{e}}^{-\frac{c-{\xi }_{\mathit{pre}}}{𝒮}}\right]}^{2}dc+{\left(1-{\text{e}}^{-\frac{{\xi }_{\mathit{in}}}{𝒮}}\right)}^2{\int }_{{\xi }_{\mathit{pre}}+{\xi }_{\mathit{in}}}^{\infty }{\text{e}}^{-\frac{2(c-{\xi }_{\mathit{pre}})}{𝒮}}dc\}$, leading to

(36)$$DEN=\frac{{\xi }_{\mathit{in}}-𝒮(1-{\text{e}}^{-\frac{{\xi }_{\mathit{in}}}{𝒮}})}{2\pi {\xi }_{\mathit{in}}^2}$$

independently of ξ_pre. Note that DEN is always ≥ 0.

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19. For interleaved RZ (iRZ) we would need two different support pulses for each polarization, so here iRZ is excluded.

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