An Algorithm for Grant-Free Random Access in Cell-Free Massive MIMO

Abstract

Massive access is one of the main use cases of beyond 5G (B5G) wireless networks and massive MIMO is a key technology for supporting it. Prior works studied massive access in the co-located massive MIMO framework. In this paper, we investigate the activity detection in grant-free random access for massive machine type communications (mMTC) in cell-free massive MIMO network. Each active device transmits a pre-assigned non-orthogonal pilot sequence to the APs and the APs send the received signals to a central processing unit (CPU) for joint activity detection. We formulate the maximum likelihood device activity detection problem and provide an algorithm based on coordinate descent method having affordable complexity. We show that the cell-free massive MIMO network can support low-powered mMTC devices and provide a broad coverage.

arXiv comment5 pages, 4 figures, published in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

References in corpus (5)

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Cited by in corpus (9)

• Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
• Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
• Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC
• Integrated Access and Backhaul in Cell-free Massive MIMO Systems
• User-Centric Perspective in Random Access Cell-Free Aided by Spatial Separability
• Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
• A Survey on User-Centric Cell-Free Massive MIMO Systems
• Grant-Free Random Access in Massive MIMO for Static Low-Power IoT Nodes
• Robust Symbol Detection in Overloaded NOMA Systems