TY - JOUR
T1 - Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity
AU - Jiang, Jia Cheng
AU - Wang, Hui Ming
AU - Vincent Poor, H.
N1 - Funding Information:
This work was supported in part by the National Key R&D Program of China under Grant 2019YFE0113200, and in part by the National Natural Science Foundation of China under Grants 61941105 and 62171364.
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the AMP state evolution function, lacking the analysis of AMP phase transition and Bayes-optimality. In this paper, we propose a novel theoretical framework to analyze the performance of the joint AUD and CE problem by adopting the replica method in the Bayes-optimal condition. Specifically, our analysis is based on a general channel model, which reduces to particular channel models in multiple typical MIMO communication scenarios. Our theoretical framework allows ones to measure the optimality and phase transition of AMP-based joint AUD and CE as well as to predict the corresponding performance metrics under our model. To reify our proposed theoretical framework, we analyze two typical scenarios from the massive random access literature, i.e., the isotropic channel scenario and the spatially correlated channel scenario. Accordingly, our performance analysis produces some novel results for both the isotropic Raleigh channel and the spatially correlated channel case.
AB - This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the AMP state evolution function, lacking the analysis of AMP phase transition and Bayes-optimality. In this paper, we propose a novel theoretical framework to analyze the performance of the joint AUD and CE problem by adopting the replica method in the Bayes-optimal condition. Specifically, our analysis is based on a general channel model, which reduces to particular channel models in multiple typical MIMO communication scenarios. Our theoretical framework allows ones to measure the optimality and phase transition of AMP-based joint AUD and CE as well as to predict the corresponding performance metrics under our model. To reify our proposed theoretical framework, we analyze two typical scenarios from the massive random access literature, i.e., the isotropic channel scenario and the spatially correlated channel scenario. Accordingly, our performance analysis produces some novel results for both the isotropic Raleigh channel and the spatially correlated channel case.
KW - Joint active user detection and channel estimation
KW - Massive random access
KW - Performance analysis
KW - Replica method
KW - massive MIMO
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U2 - 10.1109/TSP.2022.3185844
DO - 10.1109/TSP.2022.3185844
M3 - Article
AN - SCOPUS:85133740702
SN - 1053-587X
VL - 70
SP - 3647
EP - 3662
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
ER -