Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 148 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 34 tok/s Pro
GPT-5 High 40 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 183 tok/s Pro
GPT OSS 120B 443 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Tensor-Based Modulation for Unsourced Massive Random Access (2006.06797v2)

Published 11 Jun 2020 in cs.IT and math.IT

Abstract: We introduce a modulation for unsourced massive random access whereby the transmitted symbols are rank-1 tensors constructed from Grassmannian sub-constellations. The use of a low-rank tensor structure, together with tensor decomposition in order to separate the users at the receiver, allows a convenient uncoupling between multi-user separation and single-user demapping. The proposed signaling scheme is designed for the block fading channel and multiple-antenna settings, and is shown to perform well in comparison to state-of-the-art unsourced approaches.

Citations (76)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.