Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
arXiv:2111.15378 · doi:10.1109/TCOMM.2021.3102635
Abstract
Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.
12 pages, 9 figures. Published in IEEE Transactions on Communications, Vol. 69, No. 11, pp. 7520 - 7530, November 2021
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Cited by in corpus (7)
- Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
- Activity Detection in Distributed MIMO: Distributed AMP via Likelihood Ratio Fusion
- User-Centric Perspective in Random Access Cell-Free Aided by Spatial Separability
- 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
- Robust Activity Detection for Massive Random Access
- Grant-Free Random Access in Massive MIMO for Static Low-Power IoT Nodes