Update paper.md

paper/paper.md

@@ -41,20 +41,22 @@ The module structure of the OptiCommPy package is illustrated in Fig. 1. At the
 
 The `comm` sub-package comprises three modules designed for implementing various digital modulation and demodulation schemes [@Proakis2001], including pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), phase-shift keying (PSK), and on-off keying (OOK). Evaluating the performance of these diverse digital communication schemes is made possible through different metrics, such as bit-error-rate (BER), symbol-error-rate (SER), error vector magnitude (EVM), mutual information (MI), and generalized mutual information (GMI) [@Alvarado2018], all available within the `comm.metrics` module.
 
-The `models` sub-package contains the majority of the mathematical/physical models used to build OptiCommPy simulations. Within the `models.devices` module, one can access models for a range of optical devices, encompassing optical Mach-Zehnder modulators, photodiodes, optical hybrids, optical coherent receivers, and more. These functions serve as fundamental building blocks for constructing simulations of optical transmitters and receivers. In the `models.channels` module, a collection of mathematical models for the fiber optic channel is provided, spanning from basic additive white Gaussian noise (AWGN) and linear propagation models to more sophisticated non-linear propagation models rooted in variants of the split-step Fourier method (SSFM)[@Agrawal2002]. In particular, it includes an implementation of the Manakov model to simulate nonlinear transmission over a fiber optic channel with polarization-multiplexing [@Marcuse1997a]. Certain computationally intensive models, such as the Manakov SSFM, have a CuPy-based version [@cupy_learningsys2017] accessible via `models.modelsGPU`, designed specifically for execution with CUDA GPU acceleration [@cuda].
+The `models` sub-package contains most of the mathematical/physical models used to build OptiCommPy simulations. Within the `models.devices` module, one can access models for a range of optical devices, encompassing optical Mach-Zehnder modulators, photodiodes, optical hybrids, optical coherent receivers, and more. These functions are fundamental building blocks for constructing simulations of optical transmitters and receivers. In the `models.channels` module, a collection of mathematical models for the fiber optic channel is provided, spanning from basic additive white Gaussian noise (AWGN) and linear propagation models to more sophisticated non-linear propagation models rooted in variants of the split-step Fourier method (SSFM)[@Agrawal2002]. In particular, it includes an implementation of the Manakov model to simulate nonlinear transmission over a fiber optic channel with polarization-multiplexing [@Marcuse1997a]. Certain computationally intensive models, such as the Manakov SSFM, have a CuPy-based version [@cupy_learningsys2017] accessible via `models.modelsGPU`, designed specifically for execution with CUDA GPU acceleration [@cuda].
 
-The `dsp` sub-package is a collection of DSP algorithms ranging from basic signal processing operations, such as linear finite impulse response filtering, to more advanced algorithms, e.g. blind adaptive equalization. Currently, the `dsp` sub-package contains three specialized modules: `dsp.clockRecovery` provides algorithms for clock and timing synchronization; `dsp.equalization` contains implementations of common digital equalization algorithms used in optical communications; `dsp.carrierRecovery` provides algorithms for carrier frequency and phase recovery. These sub-packages covers all the basic DSP functionalities required in most of the modern coherent optical transceivers.
+The `dsp` sub-package is a collection of DSP algorithms ranging from basic signal processing operations, such as linear finite impulse response filtering, to more advanced algorithms, e.g. blind adaptive equalization. Currently, the `dsp` sub-package contains three specialized modules: `dsp.clockRecovery` provides algorithms for clock and timing synchronization; `dsp.equalization` contains implementations of common digital equalization algorithms used in optical communications; `dsp.carrierRecovery` provides algorithms for carrier frequency and phase recovery. These sub-packages cover all the basic DSP functionalities required in most of the modern coherent optical transceivers.
 
 Finally, the `utils` and the `plot` sub-packages provide functions that implement a few general utilities and custom plotting functions to visualize signals (eyediagram plots, constellation plots, etc).
 
 Several types of analysis can be conducted to characterize transmission performance across various system parameters. For instance, one can generate performance curves that depict BER and Q-factor as functions of received optical power at varying transmission distances, as illustrated in Fig. 2. 
 
 ![Performance metrics for different transmission distances and received optical powers, characterizing the increasing penalty from chromatic dispersion with the distance in a 10 Gb/s OOK transmission system. (a) BER vs received optical power for different transmission distances; (b) Q-factor vs received optical power for different transmission distances.](metrics.png)
 
-A collection of examples of utilization of OptiCommPy to build several different simulation setups, including advanced setups with non-linear fiber propagation models, WDM transmission, and coherent detection can be found in https://github.com/edsonportosilva/OptiCommPy/tree/main/examples. Additionally, benchmarks quantifying the speedups achieved by using GPU acceleration are provided.
+# Examples of usage
+
+In the documentation, one can find a [getting started example](https://opticommpy.readthedocs.io/en/latest/getting_started.html) that demonstrates some of the core features of OptiCommPy and reproduces the curves displayed in Fig. 2. A collection of examples to build several different simulation setups, including advanced setups with non-linear fiber propagation models, WDM transmission, and coherent detection can be found in the repository's [examples](https://github.com/edsonportosilva/OptiCommPy/tree/main/examples) folder. [Benchmarks](https://github.com/edsonportosilva/OptiCommPy/blob/main/examples/benchmarck_GPU_processing.ipynb) quantifying the speedup achieved by using GPU acceleration are also provided.
 
 # Acknowledgements
 
-This work was supported by the National Council for Scientific and Technological Development (CNPq), Brazil, grant 406684/2021-9.
+The National Council for Scientific and Technological Development (CNPq), Brazil, supported this work, grant 406684/2021-9.
 
 # References