NSF Workshop on Low-Latency Wireless Random-Access
This workshop, hosted by the Laboratory for Information and Decision Systems, brings together experts from industry and academia to discuss the challenges and solutions for the problem of multiple-access in wireless communication and Internet-of-Things.
Current radio-access networks (RANs) are designed with the aim of maximizing data-stream throughput for a few active users. The next generation RANs will need to service massive numbers of infrequently communicating sensors (machine-type communication, MTC, or Internet-of-Things). Most present systems employ centralized resource allocation, thus orthogonalizing the access from different users. This solution is not acceptable for the MTC as it relies on a significant control-layer overhead and incurs severe latency. Presently, there is a strong demand for a new solution in both the unlicensed spectrum [low-power wide-area networks (LP-WANs)] and the licensed spectrum (5G).
While the problem of multiple-access (MAC) is classic and a number of well-established solutions exist, the massive machine-type communications pose significant new challenges: (a) a gigantic number of idle (inactive) users; (b) a still large number (hundreds) of active users; (c) short packets; (d) focus on high energy-efficiency (low energy-per-bit). Consequently, the efficiency of the currently available solutions should be contrasted with the fundamental non-asymptotic fundamental limits and new solutions developed. Furthermore, the typical assumption of vanishing overhead of headers (control layers) in network analytic literature, and unbounded blocklength assumption in information theory evidently become grossly inadequate. As another example, the classical information-theoretic model of a K-user MAC fails to capture the random-access nature of the real-world applications, while network-theoretic collision models fail to capture the superposition nature of the real-world physics.
Organizer: Yury Polyanskiy
Hosted by the Laboratory for Information and Decision Systems (LIDS)
Program committee: Yury Polyanskiy (LIDS) and Sergio Verdú (Princeton)
Associate Professor, Department of Electrical and Computer Engineering, Duke University