Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things

Liang Liu, Erik G. Larsson, Wei Yu, Petar Popovski, Cedomir Stefanovic, Elisabeth De Carvalho

Research output: Journal article publicationJournal articleAcademic researchpeer-review

150 Citations (Scopus)


The next wave of wireless technologies will proliferate in connecting sensors, machines, and robots for myriad new applications, thereby creating the fabric for the Internet of Things (IoT). A generic scenario for IoT connectivity involves a massive number of machine-type connections, but in a typical application, only a small (unknown) subset of devices are active at any given instant; therefore, one of the key challenges of providing massive IoT connectivity is to detect the active devices first and then decode their data with low latency. This article advocates the usage of grant-free, rather than grantbased random access schemes to overcome the challenge of massive IoT access. Several key signal processing techniques that promote the performance of the grant-free strategies are outlined, with a primary focus on advanced compressed sensing techniques and their applications for the efficient detection of active devices. We argue that massive multiple-input, multiple-output (MIMO) is especially well suited for massive IoT connectivity because the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station (BS) goes to infinity by using the multiplemeasurement vector (MMV) compressed sensing techniques. This article also provides a perspective on several related important techniques for massive access, such as embedding short messages onto the device-activity detection process and the coded random access.

Original languageEnglish
Article number8454392
Pages (from-to)88-99
Number of pages12
JournalIEEE Signal Processing Magazine
Issue number5
Publication statusPublished - Sep 2018
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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