Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC

Abstract

In this letter, we study the joint device activity and delay detection problem in asynchronous massive machine-type communications (mMTC), where all active devices asynchronously transmit their preassigned preamble sequences to the base station (BS) for device identification and delay detection. We first formulate this joint detection problem as a maximum likelihood estimation problem, which depends on the received signal only through its sample covariance, and then propose efficient coordinate descent type of algorithms to solve the formulated problem. Our proposed covariance-based approach is sharply different from the existing compressed sensing (CS) approach for the same problem. Numerical results show that our proposed covariance-based approach significantly outperforms the CS approach in terms of the detection performance since our proposed approach can make better use of the BS antennas than the CS approach.

Accepted by IEEE SPL

References in corpus (12)

• Massive Machine-type Communications in 5G: Physical and MAC-layer solutions
• Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation
• Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things
• Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
• Sparse Activity Detection for Massive Connectivity
• Massive Connectivity with Massive MIMO-Part II: Achievable Rate Characterization
• Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
• Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
• An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO
• An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
• Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
• Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Cited by in corpus (4)

• Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
• Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms
• Scaling Law Analysis for Covariance Based Activity Detection in Cooperative Multi-Cell Massive MIMO
• Covariance-Based Device Activity Detection with Massive MIMO for Near-Field Correlated Channels