Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

Abstract

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 spatially correlated channel case.

Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

“…It can also be observed that EM-SBL and AMP generally have equal performance in low average SNR regimes and also outperform OMP and ℓ 2,1 -ADMM due to the fact that they exploit more information from the measurements. In spite of that, it can be observed that AMP is more sensitive to the system configuration, which is consistent with the discussions pointed out by the authors in [38].…”

“…Based on this property, message passing (MP) algorithms on graphical models have been extensively used to handle the sporadic traffic from MTDs, e.g., using belief propagation (BP) [34], approximate MP (AMP) [21], [35]- [38], and expectation propagation (EP) [20].…”

Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks

“…. , a (l,t) K T is a sparse vector and a (l,t) 0 ≪ K [23], [24]. To characterize the time-variation of the active device indicator a (l,t) , we consider a probabilistic model to characterize the time-variation of the active MTD indicator, where MTDs are supposed to form independent Markov chains by a couple of transition probabilities p (01) {a…”

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

“…For both formulations, we derived computationally-efficient algorithms based on the alternating direction method of multipliers (ADMM). Furthermore, several theoretical analysis studies have investigated the performance of AMP for JUICE in correlated MIMO channels, in terms of activity detection accuracy [20], [21], channel estimation [21] and achievable rate [15].…”

“…While the works in [14]- [21] address the JUICE problem under the more practical spatially correlated MIMO channels, they make the assumption that the channel distribution information (CDI) for all the UEs are fully known to the BS at any transmission instance. However, this assumption can be challenging to fulfill in realistic scenarios, as the BS cannot track the CDI of the UEs with long inactive status.…”