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

Abstract

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, thus one of the key challenges for providing massive IoT connectivity is to detect the active devices first and then to decode their data with low latency. This article outlines several key signal processing techniques that are applicable to the problem of massive IoT access, focusing primarily on advanced compressed sensing technique and its application for efficient detection of the active devices. We show that massive multiple-input multiple-output (MIMO) is especially well-suited for massive IoT connectivity in the sense that the device detection error can be driven to zero asymptotically in the limit as the number of antennas at the base station goes to infinity by using the multiple-measurement vector (MMV) compressed sensing techniques. The paper also provides a perspective on several related important techniques for massive access, such as embedding of short messages onto the device activity detection process and the coded random access.

to appear in IEEE Signal Processing Magazine, special issue on signal processing for Internet-of-Things

References in corpus (11)

• Message Passing Algorithms for Compressed Sensing
• The dynamics of message passing on dense graphs, with applications to compressed sensing
• Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation
• Sparse Activity Detection for Massive Connectivity
• Coded Random Access: Applying Codes on Graphs to Design Random Access Protocols
• Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem
• Massive Connectivity with Massive MIMO-Part II: Achievable Rate Characterization
• A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
• Sparse Signal Processing Concepts for Efficient 5G System Design
• Belief propagation for joint sparse recovery
• Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul

Cited by in corpus (83)

• Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
• Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
• Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver
• White Paper on Critical and Massive Machine Type Communication Towards 6G
• A Comprehensive Survey on 6G Networks:Applications, Core Services, Enabling Technologies, and Future Challenges
• A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access
• Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
• Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity
• Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
• Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry
• Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC
• An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO
• Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis
• Towards Overfitting Avoidance: Tuning-free Tensor-aided Multi-user Channel Estimation for 3D Massive MIMO Communications
• 25 Years of Signal Processing Advances for Multiantenna Communications
• MIMO-OFDM-Based Massive Connectivity With Frequency Selectivity Compensation
• Over-the-Air Computing for Wireless Data Aggregation in Massive IoT
• Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
• An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
• Joint Device Activity Detection, Channel Estimation and Signal Detection for Massive Grant-free Access via BiGAMP
• Blind Goal-Oriented Massive Access for Future Wireless Networks
• Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity
• On the Impact of Channel Estimation on the Design and Analysis of IRSA based Systems
• Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
• Massive Access for 5G and Beyond
• Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods
• Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms
• Access-based Lightweight Physical Layer Authentication for the Internet of Things Devices
• User Activity Detection for Irregular Repetition Slotted Aloha based MMTC
• Scaling Law Analysis for Covariance Based Activity Detection in Cooperative Multi-Cell Massive MIMO
• Active User Detection and Channel Estimation for Spatial-based Random Access in Crowded Massive MIMO Systems via Blind Super-resolution
• A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
• Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes
• Rate Splitting Multiple Access for Semi-Grant-Free Transmissions
• Exploiting Pilot Mixtures in Coded Random Access
• Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms
• Semi-Grant-Free NOMA: A Stochastic Geometry Model
• Optimal Least-Squares Estimator and Precoder for Energy Beamforming over IQ-Impaired Channels
• Multiple Transmit Power Levels based NOMA for Massive Machine-type Communications
• Covariance-Based Device Activity Detection with Massive MIMO for Near-Field Correlated Channels
• Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
• Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO
• Estimating Doubly-Selective Channels for Hybrid mmWave Massive MIMO Systems: A Doubly-Sparse Approach
• Reconfigurable Intelligent Surface for Massive Connectivity
• Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications
• Joint Beamforming and Compressed Sensing for Uplink Grant-Free Access
• Efficient Massive Machine Type Communication (mMTC) via AMP
• Design of Non-Orthogonal Sequences Using a Two-Stage Genetic Algorithm for Grant-Free Massive Connectivity
• Robust Activity Detection for Massive Random Access
• Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access
• Massive Unsourced Random Access for Massive MIMO Correlated Channels
• Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee
• Uniform tight frames as optimal signals
• Relay-aided Random Access for Energy-Limited Devices in the IoT
• Compressive Massive Access for Internet of Things: Cloud Computing or Fog Computing?
• Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots
• Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity
• PDRS: A Fast Non-iterative Scheme for Massive Grant-free Access in Massive MIMO
• Asynchronous Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes
• Intelligent Link Adaptation for Grant-Free Access Cellular Networks: A Distributed Deep Reinforcement Learning Approach
• Exploiting Device Heterogeneity in Grant-Free Random Access: A Data-Driven Approach
• Hybrid Message Passing-Based Detectors for Uplink Grant-Free NOMA Systems
• Impact of Channel Correlation on Subspace-Based Activity Detection in Grant-Free NOMA
• On Massive IoT Connectivity with Temporally-Correlated User Activity
• Grant-Free Random Access in Massive MIMO for Static Low-Power IoT Nodes
• An Efficient Algorithm for Device Detection and Channel Estimation in Asynchronous IoT Systems
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications
• A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks
• ML and MAP Device Activity Detections for Grant-Free Massive Access in Multi-Cell Networks
• Supporting More Active Users for Massive Access via Data-assisted Activity Detection
• Sparse Optimization for Green Edge AI Inference
• Bayesian Receiver Design for Grant-Free NOMA with Message Passing Based Structured Signal Estimation
• Deep Learning Assisted User Identification in Massive Machine-Type Communications
• Study of Design of Rate-Compatible Polar Codes Based on Non-Uniform Channel Polarization
• A Covariance-based User Activity Detection and Channel Estimation Approach with Novel Pilot Design
• Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
• Distributed Optimization for Massive Connectivity
• Iterative List Detection and Decoding for mMTC
• Study of Adaptive Activity-Aware Iterative Detection Techniques for Massive Machine-Type Communications
• Jointly Sparse Signal Recovery via Deep Auto-Encoder and Parallel Coordinate Descent Unrolling
• NOMA-based Compressive Random Access using Gaussian Spreading
• Random Access for Massive Machine-Type Communications
• Energy Internet via Packetized Management: Enabling Technologies and Deployment Challenges