Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity

Abstract

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory.

arXiv comment15 pages, 7 figures

References in corpus (18)

• The dynamics of message passing on dense graphs, with applications to compressed sensing
• 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
• Sparse Activity Detection for Massive Connectivity
• Asymptotic Analysis of MAP Estimation via the Replica Method and Applications to Compressed Sensing
• Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices
• Spectral Efficiency Analysis of Cell-Free Massive MIMO Systems with Zero-Forcing Detector
• Sparsity Learning Based Multiuser Detection in Grant-Free Massive-Device Multiple Access
• On Convergence of Approximate Message Passing
• Massive MIMO Unsourced Random Access
• On the Performance of Cell-Free Massive MIMO with Short-Term Power Constraints
• Performance Analysis of Approximate Message Passing for Distributed Compressed Sensing
• Performance Limits for Noisy Multi-Measurement Vector Problems
• Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation
• Distributed Approximate Message Passing for Compressed Sensing
• Reconfigurable Intelligent Surface for Massive Connectivity
• Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff
• Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access

Cited by in corpus (3)

• 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 Activity Detection for Irregular Repetition Slotted Aloha based MMTC