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"Variational Bayesian Inference Clustering Based Joint User Activity an" by Zhaoji Zhang, Qinghua Guo et al.

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is form ....

Approximate Message Passing , Bayes Methods , Clustering Algorithms , Grant Free , Nference Algorithms , Nternet Of Things , Oint User Activity And Data Detection , Massive Machine Type Communications , Atching Pursuit Algorithms , Ultiuser Detection , Ariational Bayesian Inference ,

"Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-U" by Li Wei, Chongwen Huang et al.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated ....

Approximation Algorithms , Channel Estimation , Aussian Approximation , Nference Algorithms , Message Passing , Essage Passing Algorithms , Reconfigurable Intelligent Surfaces , Signal Recovery , Technological Innovation , Wireless Communication ,

"Unitary Approximate Message Passing for Sparse Bayesian Learning" by Man Luo, Qinghua Guo et al.

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm. However, it does not work well for a generic measurement matrix, which may cause AMP to diverge. Damped AMP has been used for SBL to alleviate the problem at the cost of reducing convergence speed. In this work, we propose a new SBL algorithm based on structured variational inference, leveraging AMP with a unitary transformation (UAMP). Both single measurement vector and multiple measurement vector problems are investigated. It is shown that, compared to stateof- the-art AMP-based SBL algorithms, the proposed UAMPSBL is more robust and efficient, leading to remarkably better performance. ....

Approximate Message Passing , Approximation Algorithms , Bayes Methods , Covariance Matrices , Nference Algorithms , Message Passing , Signal Processing Algorithms , Parse Bayesian Learning , Parse Matrices , Tructured Variational Inference ,