Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior
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| Format: | Preprint |
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2025
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| _version_ | 1866914276020584448 |
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| author | Razlighi, Ahmad Halimi Bockelmann, Carsten Dekorsy, Armin |
| author_facet | Razlighi, Ahmad Halimi Bockelmann, Carsten Dekorsy, Armin |
| contents | In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into a common unit (CU) and multiple specific units (SUs), to a more applicable design. Our proposed system model addresses real-world constraints by introducing a general design that operates over rate-limited wireless channels. Further, we aim to tackle the rate-limit constraint, represented through the Kullback-Leibler (KL) divergence, by employing the density ratio trick alongside the implicit optimal prior method (IoPm). By applying the IoPm to our multi-task processing framework, we propose a hybrid learning approach that combines deep neural networks with kernelized-parametric machine learning methods, enabling a robust solution for the CMT-SemCom. Our framework is grounded in information-theoretic principles and employs variational approximations to bridge theoretical foundations with practical implementations. Simulation results demonstrate the proposed system's effectiveness in rate-constrained multi-task SemCom scenarios, highlighting its potential for enabling intelligence in next-generation wireless networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior Razlighi, Ahmad Halimi Bockelmann, Carsten Dekorsy, Armin Signal Processing Information Theory In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into a common unit (CU) and multiple specific units (SUs), to a more applicable design. Our proposed system model addresses real-world constraints by introducing a general design that operates over rate-limited wireless channels. Further, we aim to tackle the rate-limit constraint, represented through the Kullback-Leibler (KL) divergence, by employing the density ratio trick alongside the implicit optimal prior method (IoPm). By applying the IoPm to our multi-task processing framework, we propose a hybrid learning approach that combines deep neural networks with kernelized-parametric machine learning methods, enabling a robust solution for the CMT-SemCom. Our framework is grounded in information-theoretic principles and employs variational approximations to bridge theoretical foundations with practical implementations. Simulation results demonstrate the proposed system's effectiveness in rate-constrained multi-task SemCom scenarios, highlighting its potential for enabling intelligence in next-generation wireless networks. |
| title | Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2506.08944 |