Remove example script for Discrete Task-Oriented Deep JSCC (Xie 2023) model, including model configuration, evaluation, visualization, and performance comparison across different channels.

Author: selimfirat

docs/examples/models/index.rst

@@ -121,22 +121,6 @@ Neural network models and architectures for communications, including deep learn
       <div class="sphx-glr-thumbnail-title">Projections and Cover Tests for Communication Systems</div>
     </div>
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the SequentialModel as a foundation for building modular neural network architectures. The SequentialModel allows you to compose multiple modules together, similar to PyTorch's nn.Sequential but with additional features for communication system modeling.">
-
-.. only:: html
-
-    .. image:: /auto_examples/models/images/thumb/sphx_glr_plot_sequential_model_thumb.png
-      :alt: Sequential Model for Modular Neural Network Design
-
-    :ref:`sphx_glr_auto_examples_models_plot_sequential_model.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Sequential Model for Modular Neural Network Design</div>
-    </div>
-
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the UplinkMACChannel in an end-to-end communication system. The UplinkMACChannel handles per-user channel effects and signal combining for uplink Multiple Access Channel scenarios. Since UplinkMACChannel expects separate user signals (before combining), we create a custom model that properly integrates encoders, UplinkMACChannel, and decoders. Key Features Demonstrated: - Using UplinkMACChannel for per-user channel modeling - Comparing different channel configurations (shared vs per-user channels) - Analyzing performance with varying numbers of users - Demonstrating dynamic parameter updates during transmission">
@@ -153,22 +137,6 @@ Neural network models and architectures for communications, including deep learn
       <div class="sphx-glr-thumbnail-title">UplinkMACChannel Integration Example</div>
     </div>
 
-.. raw:: html
-
-    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the Discrete Task-Oriented Deep JSCC (DT-DeepJSCC) model from Xie et al. (2023). Unlike traditional DeepJSCC which focuses on image reconstruction, DT-DeepJSCC is designed for task-oriented semantic communications, specifically for image classification tasks. It uses a discrete bottleneck for robustness against channel impairments.">
-
-.. only:: html
-
-    .. image:: /auto_examples/models/images/thumb/sphx_glr_plot_xie2023_dt_deepjscc_thumb.png
-      :alt: Discrete Task-Oriented Deep JSCC Model (Xie 2023)
-
-    :ref:`sphx_glr_auto_examples_models_plot_xie2023_dt_deepjscc.py`
-
-.. raw:: html
-
-      <div class="sphx-glr-thumbnail-title">Discrete Task-Oriented Deep JSCC Model (Xie 2023)</div>
-    </div>
-
 .. raw:: html
 
     </div>
@@ -184,6 +152,4 @@ Neural network models and architectures for communications, including deep learn
    /auto_examples/models/plot_deepjscc_model
    /auto_examples/models/plot_multiple_access_channel
    /auto_examples/models/plot_projections_and_cover_tests
-   /auto_examples/models/plot_sequential_model
    /auto_examples/models/plot_uplink_mac_integration
-   /auto_examples/models/plot_xie2023_dt_deepjscc