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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 ,

"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 ,

"Signal Detection in MIMO Systems with Hardware Imperfections: Message " by Dawei Gao, Qinghua Guo et al.

We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design ....

Approximate Message Passing Amp , Rtificial Neural Networks , Bayes Methods , Bayesian Inference , Factor Graphs , Ardware Imperfections , Iq Imbalance , Message Passing , Imo Communication , Ultiple Input Multiple Output Mimo , Eural Networks Nns , Ower Amplifier Nonlinearity , Signal Detection ,

"Efficient Channel Estimation for RIS-Aided MIMO Communications with Un" by Yabo Guo, Peng Sun et al.

Reconfigurable intelligent surface (RIS) is very promising for wireless networks to achieve high energy efficiency, extended coverage, improved capacity, massive connectivity, etc. To unleash the full potentials of RIS-aided communications, acquiring accurate channel state information is crucial, which however is very challenging. For RIS-aided multiple-input and multiple-output (MIMO) communications, the existing channel estimation methods have computational complexity growing rapidly with the number of RIS units N (e.g., in the order of N2 or N3) and/or have special requirements on the matrices involved (e.g., the matrices need to be sparse for algorithm convergence to achieve satisfactory performance), which hinder their applications. In this work, instead of using the conventional signal model in the literature, we derive a new signal model obtained through proper vectorization and reduction operations. Then, leveraging the unitary approximate message passing (UAMP), we develop a m ....

Approximate Message Passing Amp , Channel Estimation , Electronic Mail , Message Passing , Imo Communication , Econfigurable Intelligent Surface Ris , Signal Processing Algorithms , Parse Matrices ,

"Structured DNN Based Receiver for Millimeter-Wave MIMO with Nonlinear " by Dawei Gao, Qinghua Guo et al.

This work deals with the combined effect of nonlinear distortions and inter-channel interference in millimeter wave multi-input multi-output (MIMO) communications. Deep neural networks (DNNs) can be used to handle the effect, but they often require a large number of pilot symbols, hindering their applications. With the aim of online training using a relatively small number of pilot symbols, we design a deep neural network (DNN) architecture carefully, which consists of a fully connected linear hidden layer and a non-fully connected nonlinear hidden layer. The linear hidden layer is used to deal with the co-channel interference and the nonlinear hidden layer is used to handle the nonlinear distortions. Moreover, the parameters of the DNN are properly tied to reduce the number of independent parameters. With such a DNN, the receiver is much efficient in terms of training overhead and symbol error rate performance, compared to conventional (DNN-based) techniques. Simulation results demons ....

Artificial Neural Networks , Millimeter Wave , Imo Communication , Neural Networks , Onlinear Distortion , Power Amplifier , Transmitting Antennas ,