AI & ML
Capacity Bounds for Optical Interference Channels Subject to Average Power Constraints
arXiv math.IT0 views
arXiv:2610.19715v1 Announce Type: new Abstract: Accurate information-theoretic analysis of multiuser wavelength-division multiplexed (WDM) optical links is complicated by Kerr nonlinearity and chromatic dispersion. Existing capacity bounds for first-order regular perturbative (RP) channel models have relied on per-symbol peak power constraints to simplify the underlying extremal analysis. In this paper, we replace these peak constraints with the more practically relevant average input power constraints and study the resulting RP WDM interference channel. Unlike the peak-constrained formulation, an exact finite signal-to-noise ratio (SNR) analysis under average power leads in general to a nonconvex, high-dimensional optimization problem with no closed-form solution. We show that the problem admits a tractable characterization in the high-SNR regime. Using a genie-aided argument, we derive closed-form single-user high-SNR capacity bounds for the RP interference channel and identify constant-amplitude interferer signaling as extremal. Furthermore, under an empirically validated non-negativity condition on the dominant RP kernels and within a truncated first-order RP model, we establish a finite-SNR extremality result, yielding a single-user capacity characterization within the RP model under average power constraints. Building on these single-user corner points, we construct an all-helpsone achievable region via time sharing, which strictly improves upon treating nonlinear interference as noise. Numerical results and split-step Fourier simulations of the nonlinear Schrodinger equation channel confirm that the qualitative conclusions persist over practical launch powers. The results provide new insight into extremal signaling and multiuser trade-offs for nonlinear WDM systems within the RP approximation.
Source:https://arxiv.org/abs/2610.19715PreviousEfficient construction of subspace design codes over constant alphabetNextJoint Identification, Transmission, and Sensing over State-Dependent Channels with Noiseless Feedback
Related posts
Most voted crypto projects on Coinvote, Oct 26 to Nov 1, 2026
0dev.to Data
Bluesky This Week — trending topics & AI brand mentions (2026-11-02)
0dev.to Data
Gemini 4 Carbon e DeepSeek V4.1 Flash Cyber: a Guerra Cibernética Autônoma e o Topo do Artificial Analysis
0dev.to Machine Learning
What can an AI agent do for you every day?
0dev.to Machine Learning
Mon modèle de paris semblait excellent sur 3 671 prédictions. Il n'a jamais battu le bookmaker.
0dev.to Machine Learning
HR manager LinkedIn About section examples you can adapt
0dev.to Career