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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.05629 |
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| _version_ | 1866912147825491968 |
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| author | Liesegang, Sergi Pascual-Iserte, Antonio Muñoz, Olga |
| author_facet | Liesegang, Sergi Pascual-Iserte, Antonio Muñoz, Olga |
| contents | Machine-type communications (MTC) are crucial in the evolution of mobile communication systems. Within this context, we distinguish the so-called massive MTC (mMTC), where a large number of devices coexist in the same geographical area. In the case of sensors, a high correlation in the collected information is expected. In this letter, we evaluate the impact of correlation on the entropy of a set of quantized Gaussian sources. This model allows us to express the sensed data with the data correlation matrix. Given the nature of mMTC, these matrices may be well approximated as rank deficient. Accordingly, we exploit this singularity to design a technique for switching off several sensors that maximizes the entropy under power-related constraints. The discrete optimization problem is transformed into a convex formulation that can be solved numerically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entropy-Based Sensing Schemes for Energy Efficiency in Massive MTC Liesegang, Sergi Pascual-Iserte, Antonio Muñoz, Olga Signal Processing Machine-type communications (MTC) are crucial in the evolution of mobile communication systems. Within this context, we distinguish the so-called massive MTC (mMTC), where a large number of devices coexist in the same geographical area. In the case of sensors, a high correlation in the collected information is expected. In this letter, we evaluate the impact of correlation on the entropy of a set of quantized Gaussian sources. This model allows us to express the sensed data with the data correlation matrix. Given the nature of mMTC, these matrices may be well approximated as rank deficient. Accordingly, we exploit this singularity to design a technique for switching off several sensors that maximizes the entropy under power-related constraints. The discrete optimization problem is transformed into a convex formulation that can be solved numerically. |
| title | Entropy-Based Sensing Schemes for Energy Efficiency in Massive MTC |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.05629 |