Dynamic Relative Representations for Goal-Oriented Semantic Communications
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917981265592320 |
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| author | Fiorellino, Simone Battiloro, Claudio Strinati, Emilio Calvanese Di Lorenzo, Paolo |
| author_facet | Fiorellino, Simone Battiloro, Claudio Strinati, Emilio Calvanese Di Lorenzo, Paolo |
| contents | In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data. This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment. We propose a dynamic optimization strategy that adapts relative representations, communication parameters, and computation resources for energy-efficient, low-latency, goal-oriented semantic communications. Numerical results demonstrate our methodology's effectiveness in mitigating mismatches among devices, while optimizing energy consumption, delay, and effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Relative Representations for Goal-Oriented Semantic Communications Fiorellino, Simone Battiloro, Claudio Strinati, Emilio Calvanese Di Lorenzo, Paolo Networking and Internet Architecture Information Theory Machine Learning In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data. This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment. We propose a dynamic optimization strategy that adapts relative representations, communication parameters, and computation resources for energy-efficient, low-latency, goal-oriented semantic communications. Numerical results demonstrate our methodology's effectiveness in mitigating mismatches among devices, while optimizing energy consumption, delay, and effectiveness. |
| title | Dynamic Relative Representations for Goal-Oriented Semantic Communications |
| topic | Networking and Internet Architecture Information Theory Machine Learning |
| url | https://arxiv.org/abs/2403.16986 |