Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

Abstract

In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In the time-slotted RSL-MUD scheme, active users generate and transmit data packets with random sparsity. In the non-time-slotted SSL-MUD scheme, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of the active users. Simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

arXiv commentCorrected some typos in the published version

References in corpus (10)

• Message Passing Algorithms for Compressed Sensing
• Model-Based Compressive Sensing
• Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation
• Sparse Activity Detection for Massive Connectivity
• Bilinear Generalized Approximate Message Passing
• Turbo Compressed Sensing with Partial DFT Sensing Matrix
• Massive Connectivity with Massive MIMO-Part II: Achievable Rate Characterization
• A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
• Compressive Sensing Based Multi-User Detector for the Large-Scale SM-MIMO Uplink
• Wide-area Wireless Communication Challenges for the Internet of Things

Cited by in corpus (21)

• 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
• Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC
• MIMO-OFDM-Based Massive Connectivity With Frequency Selectivity Compensation
• Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
• Joint Device Activity Detection, Channel Estimation and Signal Detection for Massive Grant-free Access via BiGAMP
• Massive Uncoordinated Multiple Access for Beyond 5G
• Blind Data Detection in Massive MIMO via ${\ell }_3$-norm Maximization over the Stiefel Manifold
• Massive Access for Future Wireless Communication Systems
• Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
• Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications
• Model-Driven Deep Learning Method for Jammer Suppression in Massive Connectivity Systems
• Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots
• Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity
• On Massive IoT Connectivity with Temporally-Correlated User Activity
• Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA
• Random Access for Massive Machine-Type Communications
• Dynamic Message Scheduling With Activity-Aware Residual Belief Propagation for Asynchronous mMTC Systems
• Sparse Kronecker-Product Coding for Unsourced Multiple Access
• Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation
• An Efficient Algorithm for Device Detection and Channel Estimation in Asynchronous IoT Systems