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"Blind Grant-Free Random Access with Message Passing Based Matrix Facto" by Zhengdao Yuan, Fei Liu et al.

Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine type communications (mMTC) for internet of things (IoT) applications, where the hand-shaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind ....

Approximate Message Passing , Channel Estimation , Covariance Matrices , Rant Free Random Access , Nternet Of Things , Massive Machine Type Communications , Message Passing , Illimeter Wave Communication , Millimeter Wave Communications , Imo Communication , Signal Detection , Ser Activity Detection ,

"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 ,

"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 ,