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"Low-Rank Matrix Sensing-Based Channel Estimation for mmWave and THz Hy" by Khawaja Fahad Masood, Jun Tong et al.

This paper studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training structures. In addition, it does not require knowledge about the array responses at the transceivers, in contrast to most existing solutions allowing low training overhead. Furthermore, a preconditioned conjugate gradient (PCG) algorithm-based implementation and a low-rank matrix completion (LRMC) formulation are proposed to further reduce the computational complexity. In order to enhance the channel ....

Channel Estimation , Channel Estimation , Ybrid Mimo , Ow Rank Matrix Sensing , Atching Pursuit Algorithms , Millimeter Wave , Illimeter Wave Communication , Imo Communication ,

"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 Message-Passing-Based Bidirectional Channel Estimation and Equal" by Guang Yang, Qinghua Guo et al.

Acquiring accurate channel state information and mitigating severe intersymbol interference are challenging for underwater acoustic communications with moving transceivers due to the rapid changes of the underwater acoustic channels. In this work, we address the issue using a superimposed training (ST) scheme with a powerful channel estimation method. Different from the conventional time-multiplexed training, training sequences with a small power are superimposed with symbol sequences. The training signals are transmitted over all time, leading to enhanced tracking capability to deal with time-varying channels at the cost of only a small power loss. To realize this, based on the belief propagation, we develop a message-passing-based bidirectional channel estimation (BCE) algorithm, where all messages are Gaussian, enabling efficient implementation. In particular, the channel correlations are fully exploited through a forward recursion and a backward recursion, thereby achieving accurat ....

Jiaozhou Bay , Elief Propagation , Idirectional Channel Estimation , Channel Estimation , Frequency Domain Analysis , Ow Complexity Frequency Domain Equalization , Atching Pursuit Algorithms , Uperimposed Training , Ime Varying Underwater Acoustic Channel , Underwater Acoustics ,