highlight novelty, if it is the **multimodal JEPA** (?) is this a **distributed JEPA** **over wireless**? not clear. It seems not to be the case

there is too much text in the first two pages. reader is somewhat tired, move some of the figs to fig1 to help the reader understand the problem

minimize use of text, by adding formulas whenever possible. As it looks now we have a magazine paper and not a technical paper

Semantic Communication (SC) has emerged as a transformative approach for data transmission, particularly in scenarios requiring efficiency and scalability like multimodal distributed sensing.

version 1:

\\[
X_\\text{sensor} \\xrightarrow{\\text{Encoding}}
E_{\\text{SC}} = f(X_{\\text{sensor}}) \\xrightarrow{\\text{Decoding}}
\\hat{X}*{\\text{sensor}} = g(E*{\\text{SC}})
\\]

Unlike traditional communication systems that transmit raw data, in SC, raw data \\( X_{\\text{sensor}} \\) (e.g., images, LiDAR, audio) are encoded into latent semantic embeddings \\( E_{\\text{SC}} \\) using deep learning models. \\( E_{\\text{SC}} \\) are then transmitted to a central server or other nodes, where they are finally decoded back to ( \\hat{X}_{\\text{sensor}} \\) to approach the original sensory data, thereby significantly reducing the communication load \\cite{xie2021deep}:

version 2:

\\[
X_\\text{sensor} \\xrightarrow{\\text{Encoder}} E_\\text{SC} \\xrightarrow{\\text{Modulation}} S_\\text{RF} \\xrightarrow{\\text{Transmission}} \\hat{S}_\\text{RF} \\xrightarrow{\\text{Demodulation}} \\hat{E}_\\text{SC} \\xrightarrow{\\text{Decoder}} \\hat{X}_\\text{sensor}
\\]

The sensor data \\( X_\\text{sensor} \\) is first transformed into a semantic embedding \\( E_\\text{SC} \\) via the encoder. This embedding is then modulated into RF signals \\( S_\\text{RF} \\) for wireless transmission. After transmission over the communication channel, the received signal \\( \\hat{S}_\\text{RF} \\) is demodulated to recover the estimated embedding \\( \\hat{E}_\\text{SC} \\). Finally, the decoder reconstructs the original sensor data \\( \\hat{X}_\\text{sensor} \\) from the decoded embedding.

This highlights the key stages: encoding, modulation, transmission, demodulation, and decoding.

This ability to condense and communicate semantic information holds great promise for applications such as autonomous driving and environmental monitoring, where distributed multimodal sensor networks are used to gather vast amounts of sensory data \cite{10333738, 10049005}.