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

By Liang Liu, Erik G. Larsson, Wei Yu, Petar Popovski, Č. Stefanović, E. de Carvalho. IEEE Signal Processing Magazine, 2018

467 citations recorded by the source corpus.

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.

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2ace2a3eb70914193a9174905f6c89f04f23fcdd

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Citations in the corpus

Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis (2019, background): “, a large M , and massive connectivity [21], [22], i.”
Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC) (2018, background): “This calls for the application of contention procedures that are better suited to deal with interference, e.g. through use of successive interference cancellation [24], multi-packet reception [25], [26], etc.”
Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (2019, background): “, G ∈ [10, 18], both Scheme 1 and Scheme 2 have a poor performance.”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “Therefore, efﬁcient multiple access mechanisms such as grant-free random access and NOMA [185]–[187], are necessary to enable the massive connections required for MTC communications.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “The sparsity in the activity pattern was leveraged to develop the sparse signal processing framework for joint activity detection and channel estimation in grant-free massive access [75].”
A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs (2024, background): “On the other hand, massive communication extends the concept of massive machine-type communication (mMTC) from IMT-2020, involving the connection of a huge number of long-lasting devices or sensors for various Internet-of-Things (IoT) applications [9], [10].”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “scheme to minimize the delay of the existing contention-based random access schemes seem promising [213].”
Massive Access for 5G and Beyond (2020, methodology): “Compared to 5G NB-IoT, B5G cellular IoT imposes much more stringent requirements on power, connectivity, and coverage.”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, methodology): “In [250], the device-activity detection is cast into a compressed sensing problem, and an approximate message passing algorithm [251] is proposed to detect the active devices.”