All Dates/Times are Australian Eastern Standard Time (AEST)

Industry Panels


July 13, 15:00 - 15:45 AEST Join Event

FEC Technology Challenges for 6G: Tbps and Low Power Consumption

In this talk, we will address FEC technology challenges for 6G, with focus on terabits-per-second and low-power consumption communications. Both features are critical to providing universal high-performance wireless connections and ultimate experience with speeds comparable to optical fibers. First, we provide the update on the latest progress on eMBB decoders for 5G and beyond, including highly reliable decoders with low latency, and we present two coding schemes targeting 6G high-throughput communications. One is product polar codes with turbo-like iterative decoding, which enjoys a higher coding gain with code lengths up to tens of thousands bits; the other is shorter polar codes with highly parallelized SC decoder to achieve an extremely high throughput (4Tbps/mm2) with an unrolled decoder, or a lower throughput (500Gbps/mm2) with a flexible recursive decoder. For 6G, low-power and low-cost devices, the key is reducing chip area with serialized decoding and boosting performance for short codes. To this end, pre-transformed polar codes with a unified SC/Serial-SCL/Fano decoder architecture leverage their improved weight spectrum and trade latency for lower energy consumption. For the proposed decoders, we will present the evaluation results of their performance and hardware implementations.

July 15, 07:00 - 07:45 AEST Join Event

Cohomological Information And Deep Learning

In this talk, we present the recent results on topological information based on the homological invariant entropy, furthermore, we present the specific information and capacity metrics defined with persistent homology. We can establish the relationship between cohomological information and the well-known topological betti number, in particular, the betti number one. Since the cohomological information is defined with the betti number of the generic topology sense, we can elaborate the meaning of the cohomological information. We also look into the topological characteristics of the deep neural networks, and we further propose the relationship of the deep neural networks and cohomological information, such that we can establish the information theory for the deep learning in the topology framework.


July 14, 07:00-08:30 AEST Join Event

Wireless Technologies For The Next Decade

In this industry panel, we invite experts in both academia and industry to chat on exciting wireless technologies for the next decade. The format would be a brief presentation by each panelist followed by Q&A. Since this is a live session, we would also welcome live audience input. Potential topics that would be covered by this session would include the following:

  • ML for wireless
  • Positioning, joint sensing/radar
  • Intelligent surfaces
  • Full duplex radio
  • New spectrum including THz
  • Coding for next gen wireless
  • Other RF, baseband, processing, and inter-disciplinary enabling technologies