2+1 Event

Coming up: The IEEE Communication Theory Technical Committee (CTTC) invites you to the “2+1” online event

Title: Massive Random Access

Date/Time: March 10, 2023 16:00 – 17:30 CET (GMT+1)

To attend the event, register by filling the following form (registration deadline March 7, 16:00 CET): CLOSED

Question Collection Link before the seminar

Detailed Agenda (time in CET)

16:00-16:05 Welcome Speech, by Petar Popovski
16:05-16:25 Invited Talk I on Massive RA by Maxime Guillaud
16:25-16:45 Invited Talk II on Massive RA by Krishna Narayanan
16:45-17:30 Panel Discussions and Questions (Including the ones collected already). Moderator: Petar Popovski

Invited Talk I: Towards Practical Massive Random Access, by Maxime Guillaud (Inria, France)

In multi-user wireless communications, random access departs from the classical set-up of multiple access in the fact that the number and identity of the active transmitters are unknown to the receiver (typically because the sporadic nature of the traffic does not allow for coordination between transmitters). This is a significant departure from the well-understood multiple access schemes (including non-orthogonal multiple access, NOMA). Random access arises e.g. in massive internet-of-things, ultra-reliable and low-latency communications, and non-terrestrial networks applications. This talk will outline why, compared to multiple access, multi-user decoding in random access scenarios is markedly more difficult, and requires to revise some of the basic assumptions that underpin modern multi-user communications systems, such as the pre-existence of synchronization and timing offset compensation, or centralized assignment of pilot sequences. Scenarios of practical interest will be discussed, including fading and multiple-antenna channels, and synchronization impairments. In particular, I will focus on massive random access, which explicitly considers the high-contention regime, i.e. the case where the number of simultaneously active transmitters can be very large, and discuss some of the practical waveforms and coding approaches that have been proposed in practice to solve this problem.

Maxime GuillaudDr. Maxime Guillaud is a senior researcher at Inria in the MARACAS team in Lyon, France. He has over 20 years of expertise in the domain of wireless communications in both academic and industrial research environments. He received the Ph.D. degree in electrical engineering and communications from EURECOM and Telecom ParisTech in 2005. From 2000 to 2001, he was a Research Engineer with Lucent Bell Laboratories, Holmdel, NJ. From 2006 to 2010, he was a Senior Researcher with FTW, Austria. From 2010 to 2014, he was a research associate at Vienna University of Technology. From 2014 to 2022, he was with Huawei Technologies. His expertise lies the physical layer of radio access networks, including transceiver algorithms, channel modeling, and modulation design.


Invited Talk II: Connections between Unsourced Random Access and Sparse Recovery, by Krishna Narayanan (Texas A&M University, USA)

Multiple access communication has been studied extensively in information theory and wireless communications for several decades. Simultaneously, sparse recovery problems including compressed sensing, group testing, and data stream computing have also been studied in depth within their respective research communities. Although connections between multiple access and sparse recovery were pointed out as early as the 1980s, these fields have emerged mostly independently. A particular version of multiple access communication, called unsourced multiple access has become very popular recently due to its relevance for the Internet of Things and Grant-free multiple access in 5G cellular standards. Developments in unsourced multiple access have strengthened the connections between these fields. I will discuss how ideas from sparse recovery have influenced the development of coding and decoding schemes for unsourced multiple access. If time permits, I will briefly discuss how developments in unsourced random access have influenced the design of schemes for sparse recovery in very high dimensions.

This is joint work with Prof. Jean-Francois Chamberland and several former and current graduate students at Texas A&M University.

Krishna Narayanan

Krishna Narayanan is the Eric D. Rubin professor in the Electrical and Computer Engineering department at Texas A&M University. His recent research interests have been in coding theory, massive multiple access for IoT, coded distributed computing, machine learning for joint source channel coding, and graph neural networks. He is a Fellow of IEEE and he recently received the 2022 joint communications society and information theory best paper award and 2020 best paper award in data storage from IEEE communications society.




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