Capacities and Optimal Input Distributions for Particle-Intensity Channels
© 2015 IEEE. This work introduces the particle-intensity channel (PIC) as a new model for molecular communication systems that includes imperfections at both transmitter and receiver and provides a new characterization of the capacity limits as well as properties of the optimal (capacity-achieving)...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133751 |
| _version_ | 1826201553119215616 |
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| author | Farsad, Nariman Chuang, Will Goldsmith, Andrea Komninakis, Christos Medard, Muriel Rose, Christopher Vandenberghe, Lieven Wesel, Emily E Wesel, Richard D |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Farsad, Nariman Chuang, Will Goldsmith, Andrea Komninakis, Christos Medard, Muriel Rose, Christopher Vandenberghe, Lieven Wesel, Emily E Wesel, Richard D |
| author_sort | Farsad, Nariman |
| collection | MIT |
| description | © 2015 IEEE. This work introduces the particle-intensity channel (PIC) as a new model for molecular communication systems that includes imperfections at both transmitter and receiver and provides a new characterization of the capacity limits as well as properties of the optimal (capacity-achieving) input distributions for such channels. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the probability of particle release, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the probability of particle release, and the receiver may not detect all the particles that arrive. We model this channel using a generalization of the binomial channel and show that the capacity-achieving input distribution for this channel always has mass points at probabilities of particle release of zero and one. To find the capacity-achieving input distributions, we develop a novel and efficient algorithm we call dynamic assignment Blahut-Arimoto (DAB). For diffusive particle transport, we also derive the conditions under which the input with two mass points is capacity-achieving. |
| first_indexed | 2024-09-23T11:53:14Z |
| format | Article |
| id | mit-1721.1/133751 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:53:14Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1337512023-09-15T19:48:50Z Capacities and Optimal Input Distributions for Particle-Intensity Channels Farsad, Nariman Chuang, Will Goldsmith, Andrea Komninakis, Christos Medard, Muriel Rose, Christopher Vandenberghe, Lieven Wesel, Emily E Wesel, Richard D Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © 2015 IEEE. This work introduces the particle-intensity channel (PIC) as a new model for molecular communication systems that includes imperfections at both transmitter and receiver and provides a new characterization of the capacity limits as well as properties of the optimal (capacity-achieving) input distributions for such channels. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the probability of particle release, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the probability of particle release, and the receiver may not detect all the particles that arrive. We model this channel using a generalization of the binomial channel and show that the capacity-achieving input distribution for this channel always has mass points at probabilities of particle release of zero and one. To find the capacity-achieving input distributions, we develop a novel and efficient algorithm we call dynamic assignment Blahut-Arimoto (DAB). For diffusive particle transport, we also derive the conditions under which the input with two mass points is capacity-achieving. 2021-10-27T19:56:28Z 2021-10-27T19:56:28Z 2020 2021-03-09T18:18:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133751 en 10.1109/TMBMC.2020.3035371 IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Farsad, Nariman Chuang, Will Goldsmith, Andrea Komninakis, Christos Medard, Muriel Rose, Christopher Vandenberghe, Lieven Wesel, Emily E Wesel, Richard D Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title | Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title_full | Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title_fullStr | Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title_full_unstemmed | Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title_short | Capacities and Optimal Input Distributions for Particle-Intensity Channels |
| title_sort | capacities and optimal input distributions for particle intensity channels |
| url | https://hdl.handle.net/1721.1/133751 |
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