Classical multiuser information theory studies the fundamental limits of models with �xed (often small) number of users as the coding blocklength goes to in�nity. This research proposes a new paradigm, referred to as many-user information theory, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a large number of users, such as machine-to-machine communication. Speci�c examples of many-user models include the many-access channel (MnAC), the many-broadcast channel, the many-relay channel, as well as models with many correlated sources. Moreover, each transmitter or receiver in the many-user system may be active with certain probability in a given block. Since the conventional notion of capacity as the supremum of the data rate measured in bits per channel use may not be the right metric for the system performance (e.g., per user rate may vanish as the number of users grows). An important question in this investigation is how to generalize the notion of capacity to many-user systems. Another question of great interest, is whether separate detection of user on-o� activities and decoding of user messages achieve the capacity in the case where users' on-o� activities are random. Intellectual Merit: This project pushes the envelop of communications, information theory and signal processing by studying models with a massive number of users, beyond existing theory for multiuser systems. The new theory will provide crucial guidance for the design of future machine- to-machine systems, where the key concepts will also be evaluated by the experimental studies proposed here. Broader Impacts: Successful design of many-user systems will facilitate applications such as machine-to-machine communication, which may a�ect many aspects of people's life, including healthcare, transportation, smart grid, smart home, smart city, and public safety, to name a few. Ideally, this research will lead to e�cient and e�ective architecture and supporting technologies for implementing future many-user systems. The project also provides many opportunities for under- graduate students, including those from underrepresented groups, to gain experience in research.
|Effective start/end date||9/1/14 → 8/31/18|
- National Science Foundation (CCF-1423040)
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